08/16/2024 11:02:54 - INFO - llamafactory.cli - Initializing distributed tasks at: 127.0.0.1:28784 usage: launcher.py [-h] --model_name_or_path MODEL_NAME_OR_PATH [--adapter_name_or_path ADAPTER_NAME_OR_PATH] [--adapter_folder ADAPTER_FOLDER] [--cache_dir CACHE_DIR] [--use_fast_tokenizer [USE_FAST_TOKENIZER]] [--no_use_fast_tokenizer] [--resize_vocab [RESIZE_VOCAB]] [--split_special_tokens [SPLIT_SPECIAL_TOKENS]] [--new_special_tokens NEW_SPECIAL_TOKENS] [--model_revision MODEL_REVISION] [--low_cpu_mem_usage [LOW_CPU_MEM_USAGE]] [--no_low_cpu_mem_usage] [--quantization_method {bitsandbytes,hqq,eetq}] [--quantization_bit QUANTIZATION_BIT] [--quantization_type {fp4,nf4}] [--double_quantization [DOUBLE_QUANTIZATION]] [--no_double_quantization] [--quantization_device_map {auto}] [--rope_scaling {linear,dynamic}] [--flash_attn {auto,disabled,sdpa,fa2}] [--shift_attn [SHIFT_ATTN]] [--mixture_of_depths {convert,load}] [--use_unsloth [USE_UNSLOTH]] [--visual_inputs [VISUAL_INPUTS]] [--moe_aux_loss_coef MOE_AUX_LOSS_COEF] [--disable_gradient_checkpointing [DISABLE_GRADIENT_CHECKPOINTING]] [--upcast_layernorm [UPCAST_LAYERNORM]] [--upcast_lmhead_output [UPCAST_LMHEAD_OUTPUT]] [--train_from_scratch [TRAIN_FROM_SCRATCH]] [--infer_backend {huggingface,vllm}] [--vllm_maxlen VLLM_MAXLEN] [--vllm_gpu_util VLLM_GPU_UTIL] [--vllm_enforce_eager [VLLM_ENFORCE_EAGER]] [--vllm_max_lora_rank VLLM_MAX_LORA_RANK] [--offload_folder OFFLOAD_FOLDER] [--use_cache [USE_CACHE]] [--no_use_cache] [--infer_dtype {auto,float16,bfloat16,float32}] [--hf_hub_token HF_HUB_TOKEN] [--ms_hub_token MS_HUB_TOKEN] [--export_dir EXPORT_DIR] [--export_size EXPORT_SIZE] [--export_device {cpu,auto}] [--export_quantization_bit EXPORT_QUANTIZATION_BIT] [--export_quantization_dataset EXPORT_QUANTIZATION_DATASET] [--export_quantization_nsamples EXPORT_QUANTIZATION_NSAMPLES] [--export_quantization_maxlen EXPORT_QUANTIZATION_MAXLEN] [--export_legacy_format [EXPORT_LEGACY_FORMAT]] [--export_hub_model_id EXPORT_HUB_MODEL_ID] [--print_param_status [PRINT_PARAM_STATUS]] [--template TEMPLATE] [--dataset DATASET] [--eval_dataset EVAL_DATASET] [--dataset_dir DATASET_DIR] [--cutoff_len CUTOFF_LEN] [--train_on_prompt [TRAIN_ON_PROMPT]] [--mask_history [MASK_HISTORY]] [--streaming [STREAMING]] [--buffer_size BUFFER_SIZE] [--mix_strategy {concat,interleave_under,interleave_over}] [--interleave_probs INTERLEAVE_PROBS] [--overwrite_cache [OVERWRITE_CACHE]] [--preprocessing_num_workers PREPROCESSING_NUM_WORKERS] [--max_samples MAX_SAMPLES] [--eval_num_beams EVAL_NUM_BEAMS] [--ignore_pad_token_for_loss [IGNORE_PAD_TOKEN_FOR_LOSS]] [--no_ignore_pad_token_for_loss] [--val_size VAL_SIZE] [--packing PACKING] [--neat_packing [NEAT_PACKING]] [--tool_format TOOL_FORMAT] [--tokenized_path TOKENIZED_PATH] --output_dir OUTPUT_DIR [--overwrite_output_dir [OVERWRITE_OUTPUT_DIR]] [--do_train [DO_TRAIN]] [--do_eval [DO_EVAL]] [--do_predict [DO_PREDICT]] [--eval_strategy {no,steps,epoch}] [--prediction_loss_only [PREDICTION_LOSS_ONLY]] [--per_device_train_batch_size PER_DEVICE_TRAIN_BATCH_SIZE] [--per_device_eval_batch_size PER_DEVICE_EVAL_BATCH_SIZE] [--per_gpu_train_batch_size PER_GPU_TRAIN_BATCH_SIZE] [--per_gpu_eval_batch_size PER_GPU_EVAL_BATCH_SIZE] [--gradient_accumulation_steps GRADIENT_ACCUMULATION_STEPS] [--eval_accumulation_steps EVAL_ACCUMULATION_STEPS] [--eval_delay EVAL_DELAY] [--torch_empty_cache_steps TORCH_EMPTY_CACHE_STEPS] [--learning_rate LEARNING_RATE] [--weight_decay WEIGHT_DECAY] [--adam_beta1 ADAM_BETA1] [--adam_beta2 ADAM_BETA2] [--adam_epsilon ADAM_EPSILON] [--max_grad_norm MAX_GRAD_NORM] [--num_train_epochs NUM_TRAIN_EPOCHS] [--max_steps MAX_STEPS] [--lr_scheduler_type {linear,cosine,cosine_with_restarts,polynomial,constant,constant_with_warmup,inverse_sqrt,reduce_lr_on_plateau,cosine_with_min_lr,warmup_stable_decay}] [--lr_scheduler_kwargs LR_SCHEDULER_KWARGS] [--warmup_ratio WARMUP_RATIO] [--warmup_steps WARMUP_STEPS] [--log_level {detail,debug,info,warning,error,critical,passive}] [--log_level_replica {detail,debug,info,warning,error,critical,passive}] [--log_on_each_node [LOG_ON_EACH_NODE]] [--no_log_on_each_node] [--logging_dir LOGGING_DIR] [--logging_strategy {no,steps,epoch}] [--logging_first_step [LOGGING_FIRST_STEP]] [--logging_steps LOGGING_STEPS] [--logging_nan_inf_filter [LOGGING_NAN_INF_FILTER]] [--no_logging_nan_inf_filter] [--save_strategy {no,steps,epoch}] [--save_steps SAVE_STEPS] [--save_total_limit SAVE_TOTAL_LIMIT] [--save_safetensors [SAVE_SAFETENSORS]] [--no_save_safetensors] [--save_on_each_node [SAVE_ON_EACH_NODE]] [--save_only_model [SAVE_ONLY_MODEL]] [--restore_callback_states_from_checkpoint [RESTORE_CALLBACK_STATES_FROM_CHECKPOINT]] [--no_cuda [NO_CUDA]] [--use_cpu [USE_CPU]] [--use_mps_device [USE_MPS_DEVICE]] [--seed SEED] [--data_seed DATA_SEED] [--jit_mode_eval [JIT_MODE_EVAL]] [--use_ipex [USE_IPEX]] [--bf16 [BF16]] [--fp16 [FP16]] [--fp16_opt_level FP16_OPT_LEVEL] [--half_precision_backend {auto,apex,cpu_amp}] [--bf16_full_eval [BF16_FULL_EVAL]] [--fp16_full_eval [FP16_FULL_EVAL]] [--tf32 TF32] [--local_rank LOCAL_RANK] [--ddp_backend {nccl,gloo,mpi,ccl,hccl,cncl}] [--tpu_num_cores TPU_NUM_CORES] [--tpu_metrics_debug [TPU_METRICS_DEBUG]] [--debug DEBUG [DEBUG ...]] [--dataloader_drop_last [DATALOADER_DROP_LAST]] [--eval_steps EVAL_STEPS] [--dataloader_num_workers DATALOADER_NUM_WORKERS] [--dataloader_prefetch_factor DATALOADER_PREFETCH_FACTOR] [--past_index PAST_INDEX] [--run_name RUN_NAME] [--disable_tqdm DISABLE_TQDM] [--remove_unused_columns [REMOVE_UNUSED_COLUMNS]] [--no_remove_unused_columns] [--label_names LABEL_NAMES [LABEL_NAMES ...]] [--load_best_model_at_end [LOAD_BEST_MODEL_AT_END]] [--metric_for_best_model METRIC_FOR_BEST_MODEL] [--greater_is_better GREATER_IS_BETTER] [--ignore_data_skip [IGNORE_DATA_SKIP]] [--fsdp FSDP] [--fsdp_min_num_params FSDP_MIN_NUM_PARAMS] [--fsdp_config FSDP_CONFIG] [--fsdp_transformer_layer_cls_to_wrap FSDP_TRANSFORMER_LAYER_CLS_TO_WRAP] [--accelerator_config ACCELERATOR_CONFIG] [--deepspeed DEEPSPEED] [--label_smoothing_factor LABEL_SMOOTHING_FACTOR] [--optim {adamw_hf,adamw_torch,adamw_torch_fused,adamw_torch_xla,adamw_torch_npu_fused,adamw_apex_fused,adafactor,adamw_anyprecision,sgd,adagrad,adamw_bnb_8bit,adamw_8bit,lion_8bit,lion_32bit,paged_adamw_32bit,paged_adamw_8bit,paged_lion_32bit,paged_lion_8bit,rmsprop,rmsprop_bnb,rmsprop_bnb_8bit,rmsprop_bnb_32bit,galore_adamw,galore_adamw_8bit,galore_adafactor,galore_adamw_layerwise,galore_adamw_8bit_layerwise,galore_adafactor_layerwise,lomo,adalomo}] [--optim_args OPTIM_ARGS] [--adafactor [ADAFACTOR]] [--group_by_length [GROUP_BY_LENGTH]] [--length_column_name LENGTH_COLUMN_NAME] [--report_to REPORT_TO] [--ddp_find_unused_parameters DDP_FIND_UNUSED_PARAMETERS] [--ddp_bucket_cap_mb DDP_BUCKET_CAP_MB] [--ddp_broadcast_buffers DDP_BROADCAST_BUFFERS] [--dataloader_pin_memory [DATALOADER_PIN_MEMORY]] [--no_dataloader_pin_memory] [--dataloader_persistent_workers [DATALOADER_PERSISTENT_WORKERS]] [--skip_memory_metrics [SKIP_MEMORY_METRICS]] [--no_skip_memory_metrics] [--use_legacy_prediction_loop [USE_LEGACY_PREDICTION_LOOP]] [--push_to_hub [PUSH_TO_HUB]] [--resume_from_checkpoint RESUME_FROM_CHECKPOINT] [--hub_model_id HUB_MODEL_ID] [--hub_strategy {end,every_save,checkpoint,all_checkpoints}] [--hub_token HUB_TOKEN] [--hub_private_repo [HUB_PRIVATE_REPO]] [--hub_always_push [HUB_ALWAYS_PUSH]] [--gradient_checkpointing [GRADIENT_CHECKPOINTING]] [--gradient_checkpointing_kwargs GRADIENT_CHECKPOINTING_KWARGS] [--include_inputs_for_metrics [INCLUDE_INPUTS_FOR_METRICS]] [--eval_do_concat_batches [EVAL_DO_CONCAT_BATCHES]] [--no_eval_do_concat_batches] [--fp16_backend {auto,apex,cpu_amp}] [--evaluation_strategy {no,steps,epoch}] [--push_to_hub_model_id PUSH_TO_HUB_MODEL_ID] [--push_to_hub_organization PUSH_TO_HUB_ORGANIZATION] [--push_to_hub_token PUSH_TO_HUB_TOKEN] [--mp_parameters MP_PARAMETERS] [--auto_find_batch_size [AUTO_FIND_BATCH_SIZE]] [--full_determinism [FULL_DETERMINISM]] [--torchdynamo TORCHDYNAMO] [--ray_scope RAY_SCOPE] [--ddp_timeout DDP_TIMEOUT] [--torch_compile [TORCH_COMPILE]] [--torch_compile_backend TORCH_COMPILE_BACKEND] [--torch_compile_mode TORCH_COMPILE_MODE] [--dispatch_batches DISPATCH_BATCHES] [--split_batches SPLIT_BATCHES] [--include_tokens_per_second [INCLUDE_TOKENS_PER_SECOND]] [--include_num_input_tokens_seen [INCLUDE_NUM_INPUT_TOKENS_SEEN]] [--neftune_noise_alpha NEFTUNE_NOISE_ALPHA] [--optim_target_modules OPTIM_TARGET_MODULES] [--batch_eval_metrics [BATCH_EVAL_METRICS]] [--eval_on_start [EVAL_ON_START]] [--eval_use_gather_object [EVAL_USE_GATHER_OBJECT]] [--sortish_sampler [SORTISH_SAMPLER]] [--predict_with_generate [PREDICT_WITH_GENERATE]] [--generation_max_length GENERATION_MAX_LENGTH] [--generation_num_beams GENERATION_NUM_BEAMS] [--generation_config GENERATION_CONFIG] [--use_badam [USE_BADAM]] [--badam_mode {layer,ratio}] [--badam_start_block BADAM_START_BLOCK] [--badam_switch_mode {ascending,descending,random,fixed}] [--badam_switch_interval BADAM_SWITCH_INTERVAL] [--badam_update_ratio BADAM_UPDATE_RATIO] [--badam_mask_mode {adjacent,scatter}] [--badam_verbose BADAM_VERBOSE] [--use_galore [USE_GALORE]] [--galore_target GALORE_TARGET] [--galore_rank GALORE_RANK] [--galore_update_interval GALORE_UPDATE_INTERVAL] [--galore_scale GALORE_SCALE] [--galore_proj_type {std,reverse_std,right,left,full}] [--galore_layerwise [GALORE_LAYERWISE]] [--pref_beta PREF_BETA] [--pref_ftx PREF_FTX] [--pref_loss {sigmoid,hinge,ipo,kto_pair,orpo,simpo}] [--dpo_label_smoothing DPO_LABEL_SMOOTHING] [--kto_chosen_weight KTO_CHOSEN_WEIGHT] [--kto_rejected_weight KTO_REJECTED_WEIGHT] [--simpo_gamma SIMPO_GAMMA] [--ppo_buffer_size PPO_BUFFER_SIZE] [--ppo_epochs PPO_EPOCHS] [--ppo_score_norm [PPO_SCORE_NORM]] [--ppo_target PPO_TARGET] [--ppo_whiten_rewards [PPO_WHITEN_REWARDS]] [--ref_model REF_MODEL] [--ref_model_adapters REF_MODEL_ADAPTERS] [--ref_model_quantization_bit REF_MODEL_QUANTIZATION_BIT] [--reward_model REWARD_MODEL] [--reward_model_adapters REWARD_MODEL_ADAPTERS] [--reward_model_quantization_bit REWARD_MODEL_QUANTIZATION_BIT] [--reward_model_type {lora,full,api}] [--additional_target ADDITIONAL_TARGET] [--lora_alpha LORA_ALPHA] [--lora_dropout LORA_DROPOUT] [--lora_rank LORA_RANK] [--lora_target LORA_TARGET] [--loraplus_lr_ratio LORAPLUS_LR_RATIO] [--loraplus_lr_embedding LORAPLUS_LR_EMBEDDING] [--use_rslora [USE_RSLORA]] [--use_dora [USE_DORA]] [--pissa_init [PISSA_INIT]] [--pissa_iter PISSA_ITER] [--pissa_convert [PISSA_CONVERT]] [--create_new_adapter [CREATE_NEW_ADAPTER]] [--freeze_trainable_layers FREEZE_TRAINABLE_LAYERS] [--freeze_trainable_modules FREEZE_TRAINABLE_MODULES] [--freeze_extra_modules FREEZE_EXTRA_MODULES] [--pure_bf16 [PURE_BF16]] [--stage {pt,sft,rm,ppo,dpo,kto}] [--finetuning_type {lora,freeze,full}] [--use_llama_pro [USE_LLAMA_PRO]] [--use_adam_mini [USE_ADAM_MINI]] [--freeze_vision_tower [FREEZE_VISION_TOWER]] [--no_freeze_vision_tower] [--train_mm_proj_only [TRAIN_MM_PROJ_ONLY]] [--compute_accuracy [COMPUTE_ACCURACY]] [--plot_loss [PLOT_LOSS]] [--do_sample [DO_SAMPLE]] [--no_do_sample] [--temperature TEMPERATURE] [--top_p TOP_P] [--top_k TOP_K] [--num_beams NUM_BEAMS] [--max_length MAX_LENGTH] [--max_new_tokens MAX_NEW_TOKENS] [--repetition_penalty REPETITION_PENALTY] [--length_penalty LENGTH_PENALTY] [--default_system DEFAULT_SYSTEM] optional arguments: -h, --help show this help message and exit --model_name_or_path MODEL_NAME_OR_PATH Path to the model weight or identifier from huggingface.co/models or modelscope.cn/models. (default: None) --adapter_name_or_path ADAPTER_NAME_OR_PATH Path to the adapter weight or identifier from huggingface.co/models. Use commas to separate multiple adapters. (default: None) --adapter_folder ADAPTER_FOLDER The folder containing the adapter weights to load. (default: None) --cache_dir CACHE_DIR Where to store the pre-trained models downloaded from huggingface.co or modelscope.cn. (default: None) --use_fast_tokenizer [USE_FAST_TOKENIZER] Whether or not to use one of the fast tokenizer (backed by the tokenizers library). (default: True) --no_use_fast_tokenizer Whether or not to use one of the fast tokenizer (backed by the tokenizers library). (default: False) --resize_vocab [RESIZE_VOCAB] Whether or not to resize the tokenizer vocab and the embedding layers. (default: False) --split_special_tokens [SPLIT_SPECIAL_TOKENS] Whether or not the special tokens should be split during the tokenization process. (default: False) --new_special_tokens NEW_SPECIAL_TOKENS Special tokens to be added into the tokenizer. Use commas to separate multiple tokens. (default: None) --model_revision MODEL_REVISION The specific model version to use (can be a branch name, tag name or commit id). (default: main) --low_cpu_mem_usage [LOW_CPU_MEM_USAGE] Whether or not to use memory-efficient model loading. (default: True) --no_low_cpu_mem_usage Whether or not to use memory-efficient model loading. (default: False) --quantization_method {bitsandbytes,hqq,eetq} Quantization method to use for on-the-fly quantization. (default: bitsandbytes) --quantization_bit QUANTIZATION_BIT The number of bits to quantize the model using bitsandbytes. (default: None) --quantization_type {fp4,nf4} Quantization data type to use in int4 training. (default: nf4) --double_quantization [DOUBLE_QUANTIZATION] Whether or not to use double quantization in int4 training. (default: True) --no_double_quantization Whether or not to use double quantization in int4 training. (default: False) --quantization_device_map {auto} Device map used to infer the 4-bit quantized model, needs bitsandbytes>=0.43.0. (default: None) --rope_scaling {linear,dynamic} Which scaling strategy should be adopted for the RoPE embeddings. (default: None) --flash_attn {auto,disabled,sdpa,fa2} Enable FlashAttention for faster training and inference. (default: auto) --shift_attn [SHIFT_ATTN] Enable shift short attention (S^2-Attn) proposed by LongLoRA. (default: False) --mixture_of_depths {convert,load} Convert the model to mixture-of-depths (MoD) or load the MoD model. (default: None) --use_unsloth [USE_UNSLOTH] Whether or not to use unsloth's optimization for the LoRA training. (default: False) --visual_inputs [VISUAL_INPUTS] Whethor or not to use multimodal LLM that accepts visual inputs. (default: False) --moe_aux_loss_coef MOE_AUX_LOSS_COEF Coefficient of the auxiliary router loss in mixture- of-experts model. (default: None) --disable_gradient_checkpointing [DISABLE_GRADIENT_CHECKPOINTING] Whether or not to disable gradient checkpointing. (default: False) --upcast_layernorm [UPCAST_LAYERNORM] Whether or not to upcast the layernorm weights in fp32. (default: False) --upcast_lmhead_output [UPCAST_LMHEAD_OUTPUT] Whether or not to upcast the output of lm_head in fp32. (default: False) --train_from_scratch [TRAIN_FROM_SCRATCH] Whether or not to randomly initialize the model weights. (default: False) --infer_backend {huggingface,vllm} Backend engine used at inference. (default: huggingface) --vllm_maxlen VLLM_MAXLEN Maximum sequence (prompt + response) length of the vLLM engine. (default: 2048) --vllm_gpu_util VLLM_GPU_UTIL The fraction of GPU memory in (0,1) to be used for the vLLM engine. (default: 0.9) --vllm_enforce_eager [VLLM_ENFORCE_EAGER] Whether or not to disable CUDA graph in the vLLM engine. (default: False) --vllm_max_lora_rank VLLM_MAX_LORA_RANK Maximum rank of all LoRAs in the vLLM engine. (default: 32) --offload_folder OFFLOAD_FOLDER Path to offload model weights. (default: offload) --use_cache [USE_CACHE] Whether or not to use KV cache in generation. (default: True) --no_use_cache Whether or not to use KV cache in generation. (default: False) --infer_dtype {auto,float16,bfloat16,float32} Data type for model weights and activations at inference. (default: auto) --hf_hub_token HF_HUB_TOKEN Auth token to log in with Hugging Face Hub. (default: None) --ms_hub_token MS_HUB_TOKEN Auth token to log in with ModelScope Hub. (default: None) --export_dir EXPORT_DIR Path to the directory to save the exported model. (default: None) --export_size EXPORT_SIZE The file shard size (in GB) of the exported model. (default: 1) --export_device {cpu,auto} The device used in model export, use `auto` to accelerate exporting. (default: cpu) --export_quantization_bit EXPORT_QUANTIZATION_BIT The number of bits to quantize the exported model. (default: None) --export_quantization_dataset EXPORT_QUANTIZATION_DATASET Path to the dataset or dataset name to use in quantizing the exported model. (default: None) --export_quantization_nsamples EXPORT_QUANTIZATION_NSAMPLES The number of samples used for quantization. (default: 128) --export_quantization_maxlen EXPORT_QUANTIZATION_MAXLEN The maximum length of the model inputs used for quantization. (default: 1024) --export_legacy_format [EXPORT_LEGACY_FORMAT] Whether or not to save the `.bin` files instead of `.safetensors`. (default: False) --export_hub_model_id EXPORT_HUB_MODEL_ID The name of the repository if push the model to the Hugging Face hub. (default: None) --print_param_status [PRINT_PARAM_STATUS] For debugging purposes, print the status of the parameters in the model. (default: False) --template TEMPLATE Which template to use for constructing prompts in training and inference. (default: None) --dataset DATASET The name of dataset(s) to use for training. Use commas to separate multiple datasets. (default: None) --eval_dataset EVAL_DATASET The name of dataset(s) to use for evaluation. Use commas to separate multiple datasets. (default: None) --dataset_dir DATASET_DIR Path to the folder containing the datasets. (default: data) --cutoff_len CUTOFF_LEN The cutoff length of the tokenized inputs in the dataset. (default: 1024) --train_on_prompt [TRAIN_ON_PROMPT] Whether or not to disable the mask on the prompt. (default: False) --mask_history [MASK_HISTORY] Whether or not to mask the history and train on the last turn only. (default: False) --streaming [STREAMING] Enable dataset streaming. (default: False) --buffer_size BUFFER_SIZE Size of the buffer to randomly sample examples from in dataset streaming. (default: 16384) --mix_strategy {concat,interleave_under,interleave_over} Strategy to use in dataset mixing (concat/interleave) (undersampling/oversampling). (default: concat) --interleave_probs INTERLEAVE_PROBS Probabilities to sample data from datasets. Use commas to separate multiple datasets. (default: None) --overwrite_cache [OVERWRITE_CACHE] Overwrite the cached training and evaluation sets. (default: False) --preprocessing_num_workers PREPROCESSING_NUM_WORKERS The number of processes to use for the pre-processing. (default: None) --max_samples MAX_SAMPLES For debugging purposes, truncate the number of examples for each dataset. (default: None) --eval_num_beams EVAL_NUM_BEAMS Number of beams to use for evaluation. This argument will be passed to `model.generate` (default: None) --ignore_pad_token_for_loss [IGNORE_PAD_TOKEN_FOR_LOSS] Whether or not to ignore the tokens corresponding to the pad label in loss computation. (default: True) --no_ignore_pad_token_for_loss Whether or not to ignore the tokens corresponding to the pad label in loss computation. (default: False) --val_size VAL_SIZE Size of the development set, should be an integer or a float in range `[0,1)`. (default: 0.0) --packing PACKING Enable sequences packing in training. Will automatically enable in pre-training. (default: None) --neat_packing [NEAT_PACKING] Enable sequence packing without cross-attention. (default: False) --tool_format TOOL_FORMAT Tool format to use for constructing function calling examples. (default: None) --tokenized_path TOKENIZED_PATH Path to save or load the tokenized datasets. (default: None) --output_dir OUTPUT_DIR The output directory where the model predictions and checkpoints will be written. (default: None) --overwrite_output_dir [OVERWRITE_OUTPUT_DIR] Overwrite the content of the output directory. Use this to continue training if output_dir points to a checkpoint directory. (default: False) --do_train [DO_TRAIN] Whether to run training. (default: False) --do_eval [DO_EVAL] Whether to run eval on the dev set. (default: False) --do_predict [DO_PREDICT] Whether to run predictions on the test set. (default: False) --eval_strategy {no,steps,epoch} The evaluation strategy to use. (default: no) --prediction_loss_only [PREDICTION_LOSS_ONLY] When performing evaluation and predictions, only returns the loss. (default: False) --per_device_train_batch_size PER_DEVICE_TRAIN_BATCH_SIZE Batch size per GPU/TPU/MPS/NPU core/CPU for training. (default: 8) --per_device_eval_batch_size PER_DEVICE_EVAL_BATCH_SIZE Batch size per GPU/TPU/MPS/NPU core/CPU for evaluation. (default: 8) --per_gpu_train_batch_size PER_GPU_TRAIN_BATCH_SIZE Deprecated, the use of `--per_device_train_batch_size` is preferred. Batch size per GPU/TPU core/CPU for training. (default: None) --per_gpu_eval_batch_size PER_GPU_EVAL_BATCH_SIZE Deprecated, the use of `--per_device_eval_batch_size` is preferred. Batch size per GPU/TPU core/CPU for evaluation. (default: None) --gradient_accumulation_steps GRADIENT_ACCUMULATION_STEPS Number of updates steps to accumulate before performing a backward/update pass. (default: 1) --eval_accumulation_steps EVAL_ACCUMULATION_STEPS Number of predictions steps to accumulate before moving the tensors to the CPU. (default: None) --eval_delay EVAL_DELAY Number of epochs or steps to wait for before the first evaluation can be performed, depending on the eval_strategy. (default: 0) --torch_empty_cache_steps TORCH_EMPTY_CACHE_STEPS Number of steps to wait before calling `torch..empty_cache()`.This can help avoid CUDA out-of-memory errors by lowering peak VRAM usage at a cost of about [10{'option_strings': ['-- torch_empty_cache_steps'], 'dest': 'torch_empty_cache_steps', 'nargs': None, 'const': None, 'default': None, 'type': 'int', 'choices': None, 'required': False, 'help': 'Number of steps to wait before calling `torch..empty_cache()`.This can help avoid CUDA out-of-memory errors by lowering peak VRAM usage at a cost of about [10% slower performance] (https://github.com/huggingface/transformers/issues/31 372).If left unset or set to None, cache will not be emptied.', 'metavar': None, 'container': , 'prog': 'launcher.py'}lower performance](https://githu b.com/huggingface/transformers/issues/31372).If left unset or set to None, cache will not be emptied. (default: None) --learning_rate LEARNING_RATE The initial learning rate for AdamW. (default: 5e-05) --weight_decay WEIGHT_DECAY Weight decay for AdamW if we apply some. (default: 0.0) --adam_beta1 ADAM_BETA1 Beta1 for AdamW optimizer (default: 0.9) --adam_beta2 ADAM_BETA2 Beta2 for AdamW optimizer (default: 0.999) --adam_epsilon ADAM_EPSILON Epsilon for AdamW optimizer. (default: 1e-08) --max_grad_norm MAX_GRAD_NORM Max gradient norm. (default: 1.0) --num_train_epochs NUM_TRAIN_EPOCHS Total number of training epochs to perform. (default: 3.0) --max_steps MAX_STEPS If > 0: set total number of training steps to perform. Override num_train_epochs. (default: -1) --lr_scheduler_type {linear,cosine,cosine_with_restarts,polynomial,constant,constant_with_warmup,inverse_sqrt,reduce_lr_on_plateau,cosine_with_min_lr,warmup_stable_decay} The scheduler type to use. (default: linear) --lr_scheduler_kwargs LR_SCHEDULER_KWARGS Extra parameters for the lr_scheduler such as {'num_cycles': 1} for the cosine with hard restarts. (default: {}) --warmup_ratio WARMUP_RATIO Linear warmup over warmup_ratio fraction of total steps. (default: 0.0) --warmup_steps WARMUP_STEPS Linear warmup over warmup_steps. (default: 0) --log_level {detail,debug,info,warning,error,critical,passive} Logger log level to use on the main node. Possible choices are the log levels as strings: 'debug', 'info', 'warning', 'error' and 'critical', plus a 'passive' level which doesn't set anything and lets the application set the level. Defaults to 'passive'. (default: passive) --log_level_replica {detail,debug,info,warning,error,critical,passive} Logger log level to use on replica nodes. Same choices and defaults as ``log_level`` (default: warning) --log_on_each_node [LOG_ON_EACH_NODE] When doing a multinode distributed training, whether to log once per node or just once on the main node. (default: True) --no_log_on_each_node When doing a multinode distributed training, whether to log once per node or just once on the main node. (default: False) --logging_dir LOGGING_DIR Tensorboard log dir. (default: None) --logging_strategy {no,steps,epoch} The logging strategy to use. (default: steps) --logging_first_step [LOGGING_FIRST_STEP] Log the first global_step (default: False) --logging_steps LOGGING_STEPS Log every X updates steps. Should be an integer or a float in range `[0,1)`. If smaller than 1, will be interpreted as ratio of total training steps. (default: 500) --logging_nan_inf_filter [LOGGING_NAN_INF_FILTER] Filter nan and inf losses for logging. (default: True) --no_logging_nan_inf_filter Filter nan and inf losses for logging. (default: False) --save_strategy {no,steps,epoch} The checkpoint save strategy to use. (default: steps) --save_steps SAVE_STEPS Save checkpoint every X updates steps. Should be an integer or a float in range `[0,1)`. If smaller than 1, will be interpreted as ratio of total training steps. (default: 500) --save_total_limit SAVE_TOTAL_LIMIT If a value is passed, will limit the total amount of checkpoints. Deletes the older checkpoints in `output_dir`. When `load_best_model_at_end` is enabled, the 'best' checkpoint according to `metric_for_best_model` will always be retained in addition to the most recent ones. For example, for `save_total_limit=5` and `load_best_model_at_end=True`, the four last checkpoints will always be retained alongside the best model. When `save_total_limit=1` and `load_best_model_at_end=True`, it is possible that two checkpoints are saved: the last one and the best one (if they are different). Default is unlimited checkpoints (default: None) --save_safetensors [SAVE_SAFETENSORS] Use safetensors saving and loading for state dicts instead of default torch.load and torch.save. (default: True) --no_save_safetensors Use safetensors saving and loading for state dicts instead of default torch.load and torch.save. (default: False) --save_on_each_node [SAVE_ON_EACH_NODE] When doing multi-node distributed training, whether to save models and checkpoints on each node, or only on the main one (default: False) --save_only_model [SAVE_ONLY_MODEL] When checkpointing, whether to only save the model, or also the optimizer, scheduler & rng state.Note that when this is true, you won't be able to resume training from checkpoint.This enables you to save storage by not storing the optimizer, scheduler & rng state.You can only load the model using from_pretrained with this option set to True. (default: False) --restore_callback_states_from_checkpoint [RESTORE_CALLBACK_STATES_FROM_CHECKPOINT] Whether to restore the callback states from the checkpoint. If `True`, will override callbacks passed to the `Trainer` if they exist in the checkpoint. (default: False) --no_cuda [NO_CUDA] This argument is deprecated. It will be removed in version 5.0 of 🤗 Transformers. (default: False) --use_cpu [USE_CPU] Whether or not to use cpu. If set to False, we will use cuda/tpu/mps/npu device if available. (default: False) --use_mps_device [USE_MPS_DEVICE] This argument is deprecated. `mps` device will be used if available similar to `cuda` device. It will be removed in version 5.0 of 🤗 Transformers (default: False) --seed SEED Random seed that will be set at the beginning of training. (default: 42) --data_seed DATA_SEED Random seed to be used with data samplers. (default: None) --jit_mode_eval [JIT_MODE_EVAL] Whether or not to use PyTorch jit trace for inference (default: False) --use_ipex [USE_IPEX] Use Intel extension for PyTorch when it is available, installation: 'https://github.com/intel/intel- extension-for-pytorch' (default: False) --bf16 [BF16] Whether to use bf16 (mixed) precision instead of 32-bit. Requires Ampere or higher NVIDIA architecture or using CPU (use_cpu) or Ascend NPU. This is an experimental API and it may change. (default: False) --fp16 [FP16] Whether to use fp16 (mixed) precision instead of 32-bit (default: False) --fp16_opt_level FP16_OPT_LEVEL For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']. See details at https://nvidia.github.io/apex/amp.html (default: O1) --half_precision_backend {auto,apex,cpu_amp} The backend to be used for half precision. (default: auto) --bf16_full_eval [BF16_FULL_EVAL] Whether to use full bfloat16 evaluation instead of 32-bit. This is an experimental API and it may change. (default: False) --fp16_full_eval [FP16_FULL_EVAL] Whether to use full float16 evaluation instead of 32-bit (default: False) --tf32 TF32 Whether to enable tf32 mode, available in Ampere and newer GPU architectures. This is an experimental API and it may change. (default: None) --local_rank LOCAL_RANK For distributed training: local_rank (default: -1) --ddp_backend {nccl,gloo,mpi,ccl,hccl,cncl} The backend to be used for distributed training (default: None) --tpu_num_cores TPU_NUM_CORES TPU: Number of TPU cores (automatically passed by launcher script) (default: None) --tpu_metrics_debug [TPU_METRICS_DEBUG] Deprecated, the use of `--debug tpu_metrics_debug` is preferred. TPU: Whether to print debug metrics (default: False) --debug DEBUG [DEBUG ...] Whether or not to enable debug mode. Current options: `underflow_overflow` (Detect underflow and overflow in activations and weights), `tpu_metrics_debug` (print debug metrics on TPU). (default: None) --dataloader_drop_last [DATALOADER_DROP_LAST] Drop the last incomplete batch if it is not divisible by the batch size. (default: False) --eval_steps EVAL_STEPS Run an evaluation every X steps. Should be an integer or a float in range `[0,1)`. If smaller than 1, will be interpreted as ratio of total training steps. (default: None) --dataloader_num_workers DATALOADER_NUM_WORKERS Number of subprocesses to use for data loading (PyTorch only). 0 means that the data will be loaded in the main process. (default: 0) --dataloader_prefetch_factor DATALOADER_PREFETCH_FACTOR Number of batches loaded in advance by each worker. 2 means there will be a total of 2 * num_workers batches prefetched across all workers. Default is 2 for PyTorch < 2.0.0 and otherwise None. (default: None) --past_index PAST_INDEX If >=0, uses the corresponding part of the output as the past state for next step. (default: -1) --run_name RUN_NAME An optional descriptor for the run. Notably used for wandb, mlflow and comet logging. (default: None) --disable_tqdm DISABLE_TQDM Whether or not to disable the tqdm progress bars. (default: None) --remove_unused_columns [REMOVE_UNUSED_COLUMNS] Remove columns not required by the model when using an nlp.Dataset. (default: True) --no_remove_unused_columns Remove columns not required by the model when using an nlp.Dataset. (default: False) --label_names LABEL_NAMES [LABEL_NAMES ...] The list of keys in your dictionary of inputs that correspond to the labels. (default: None) --load_best_model_at_end [LOAD_BEST_MODEL_AT_END] Whether or not to load the best model found during training at the end of training. When this option is enabled, the best checkpoint will always be saved. See `save_total_limit` for more. (default: False) --metric_for_best_model METRIC_FOR_BEST_MODEL The metric to use to compare two different models. (default: None) --greater_is_better GREATER_IS_BETTER Whether the `metric_for_best_model` should be maximized or not. (default: None) --ignore_data_skip [IGNORE_DATA_SKIP] When resuming training, whether or not to skip the first epochs and batches to get to the same training data. (default: False) --fsdp FSDP Whether or not to use PyTorch Fully Sharded Data Parallel (FSDP) training (in distributed training only). The base option should be `full_shard`, `shard_grad_op` or `no_shard` and you can add CPU- offload to `full_shard` or `shard_grad_op` like this: full_shard offload` or `shard_grad_op offload`. You can add auto-wrap to `full_shard` or `shard_grad_op` with the same syntax: full_shard auto_wrap` or `shard_grad_op auto_wrap`. (default: ) --fsdp_min_num_params FSDP_MIN_NUM_PARAMS This parameter is deprecated. FSDP's minimum number of parameters for Default Auto Wrapping. (useful only when `fsdp` field is passed). (default: 0) --fsdp_config FSDP_CONFIG Config to be used with FSDP (Pytorch Fully Sharded Data Parallel). The value is either a fsdp json config file (e.g., `fsdp_config.json`) or an already loaded json file as `dict`. (default: None) --fsdp_transformer_layer_cls_to_wrap FSDP_TRANSFORMER_LAYER_CLS_TO_WRAP This parameter is deprecated. Transformer layer class name (case-sensitive) to wrap, e.g, `BertLayer`, `GPTJBlock`, `T5Block` .... (useful only when `fsdp` flag is passed). (default: None) --accelerator_config ACCELERATOR_CONFIG Config to be used with the internal Accelerator object initializtion. The value is either a accelerator json config file (e.g., `accelerator_config.json`) or an already loaded json file as `dict`. (default: None) --deepspeed DEEPSPEED Enable deepspeed and pass the path to deepspeed json config file (e.g. `ds_config.json`) or an already loaded json file as a dict (default: None) --label_smoothing_factor LABEL_SMOOTHING_FACTOR The label smoothing epsilon to apply (zero means no label smoothing). (default: 0.0) --optim {adamw_hf,adamw_torch,adamw_torch_fused,adamw_torch_xla,adamw_torch_npu_fused,adamw_apex_fused,adafactor,adamw_anyprecision,sgd,adagrad,adamw_bnb_8bit,adamw_8bit,lion_8bit,lion_32bit,paged_adamw_32bit,paged_adamw_8bit,paged_lion_32bit,paged_lion_8bit,rmsprop,rmsprop_bnb,rmsprop_bnb_8bit,rmsprop_bnb_32bit,galore_adamw,galore_adamw_8bit,galore_adafactor,galore_adamw_layerwise,galore_adamw_8bit_layerwise,galore_adafactor_layerwise,lomo,adalomo} The optimizer to use. (default: adamw_torch) --optim_args OPTIM_ARGS Optional arguments to supply to optimizer. (default: None) --adafactor [ADAFACTOR] Whether or not to replace AdamW by Adafactor. (default: False) --group_by_length [GROUP_BY_LENGTH] Whether or not to group samples of roughly the same length together when batching. (default: False) --length_column_name LENGTH_COLUMN_NAME Column name with precomputed lengths to use when grouping by length. (default: length) --report_to REPORT_TO The list of integrations to report the results and logs to. (default: None) --ddp_find_unused_parameters DDP_FIND_UNUSED_PARAMETERS When using distributed training, the value of the flag `find_unused_parameters` passed to `DistributedDataParallel`. (default: None) --ddp_bucket_cap_mb DDP_BUCKET_CAP_MB When using distributed training, the value of the flag `bucket_cap_mb` passed to `DistributedDataParallel`. (default: None) --ddp_broadcast_buffers DDP_BROADCAST_BUFFERS When using distributed training, the value of the flag `broadcast_buffers` passed to `DistributedDataParallel`. (default: None) --dataloader_pin_memory [DATALOADER_PIN_MEMORY] Whether or not to pin memory for DataLoader. (default: True) --no_dataloader_pin_memory Whether or not to pin memory for DataLoader. (default: False) --dataloader_persistent_workers [DATALOADER_PERSISTENT_WORKERS] If True, the data loader will not shut down the worker processes after a dataset has been consumed once. This allows to maintain the workers Dataset instances alive. Can potentially speed up training, but will increase RAM usage. (default: False) --skip_memory_metrics [SKIP_MEMORY_METRICS] Whether or not to skip adding of memory profiler reports to metrics. (default: True) --no_skip_memory_metrics Whether or not to skip adding of memory profiler reports to metrics. (default: False) --use_legacy_prediction_loop [USE_LEGACY_PREDICTION_LOOP] Whether or not to use the legacy prediction_loop in the Trainer. (default: False) --push_to_hub [PUSH_TO_HUB] Whether or not to upload the trained model to the model hub after training. (default: False) --resume_from_checkpoint RESUME_FROM_CHECKPOINT The path to a folder with a valid checkpoint for your model. (default: None) --hub_model_id HUB_MODEL_ID The name of the repository to keep in sync with the local `output_dir`. (default: None) --hub_strategy {end,every_save,checkpoint,all_checkpoints} The hub strategy to use when `--push_to_hub` is activated. (default: every_save) --hub_token HUB_TOKEN The token to use to push to the Model Hub. (default: None) --hub_private_repo [HUB_PRIVATE_REPO] Whether the model repository is private or not. (default: False) --hub_always_push [HUB_ALWAYS_PUSH] Unless `True`, the Trainer will skip pushes if the previous one wasn't finished yet. (default: False) --gradient_checkpointing [GRADIENT_CHECKPOINTING] If True, use gradient checkpointing to save memory at the expense of slower backward pass. (default: False) --gradient_checkpointing_kwargs GRADIENT_CHECKPOINTING_KWARGS Gradient checkpointing key word arguments such as `use_reentrant`. Will be passed to `torch.utils.checkpoint.checkpoint` through `model.gradient_checkpointing_enable`. (default: None) --include_inputs_for_metrics [INCLUDE_INPUTS_FOR_METRICS] Whether or not the inputs will be passed to the `compute_metrics` function. (default: False) --eval_do_concat_batches [EVAL_DO_CONCAT_BATCHES] Whether to recursively concat inputs/losses/labels/predictions across batches. If `False`, will instead store them as lists, with each batch kept separate. (default: True) --no_eval_do_concat_batches Whether to recursively concat inputs/losses/labels/predictions across batches. If `False`, will instead store them as lists, with each batch kept separate. (default: False) --fp16_backend {auto,apex,cpu_amp} Deprecated. Use half_precision_backend instead (default: auto) --evaluation_strategy {no,steps,epoch} Deprecated. Use `eval_strategy` instead (default: None) --push_to_hub_model_id PUSH_TO_HUB_MODEL_ID The name of the repository to which push the `Trainer`. (default: None) --push_to_hub_organization PUSH_TO_HUB_ORGANIZATION The name of the organization in with to which push the `Trainer`. (default: None) --push_to_hub_token PUSH_TO_HUB_TOKEN The token to use to push to the Model Hub. (default: None) --mp_parameters MP_PARAMETERS Used by the SageMaker launcher to send mp-specific args. Ignored in Trainer (default: ) --auto_find_batch_size [AUTO_FIND_BATCH_SIZE] Whether to automatically decrease the batch size in half and rerun the training loop again each time a CUDA Out-of-Memory was reached (default: False) --full_determinism [FULL_DETERMINISM] Whether to call enable_full_determinism instead of set_seed for reproducibility in distributed training. Important: this will negatively impact the performance, so only use it for debugging. (default: False) --torchdynamo TORCHDYNAMO This argument is deprecated, use `--torch_compile_backend` instead. (default: None) --ray_scope RAY_SCOPE The scope to use when doing hyperparameter search with Ray. By default, `"last"` will be used. Ray will then use the last checkpoint of all trials, compare those, and select the best one. However, other options are also available. See the Ray documentation (https://doc s.ray.io/en/latest/tune/api_docs/analysis.html#ray.tun e.ExperimentAnalysis.get_best_trial) for more options. (default: last) --ddp_timeout DDP_TIMEOUT Overrides the default timeout for distributed training (value should be given in seconds). (default: 1800) --torch_compile [TORCH_COMPILE] If set to `True`, the model will be wrapped in `torch.compile`. (default: False) --torch_compile_backend TORCH_COMPILE_BACKEND Which backend to use with `torch.compile`, passing one will trigger a model compilation. (default: None) --torch_compile_mode TORCH_COMPILE_MODE Which mode to use with `torch.compile`, passing one will trigger a model compilation. (default: None) --dispatch_batches DISPATCH_BATCHES Deprecated. Pass {'dispatch_batches':VALUE} to `accelerator_config`. (default: None) --split_batches SPLIT_BATCHES Deprecated. Pass {'split_batches':True} to `accelerator_config`. (default: None) --include_tokens_per_second [INCLUDE_TOKENS_PER_SECOND] If set to `True`, the speed metrics will include `tgs` (tokens per second per device). (default: False) --include_num_input_tokens_seen [INCLUDE_NUM_INPUT_TOKENS_SEEN] If set to `True`, will track the number of input tokens seen throughout training. (May be slower in distributed training) (default: False) --neftune_noise_alpha NEFTUNE_NOISE_ALPHA Activates neftune noise embeddings into the model. NEFTune has been proven to drastically improve model performances for instrcution fine-tuning. Check out the original paper here: https://arxiv.org/abs/2310.05914 and the original code here: https://github.com/neelsjain/NEFTune. Only supported for `PreTrainedModel` and `PeftModel` classes. (default: None) --optim_target_modules OPTIM_TARGET_MODULES Target modules for the optimizer defined in the `optim` argument. Only used for the GaLore optimizer at the moment. (default: None) --batch_eval_metrics [BATCH_EVAL_METRICS] Break eval metrics calculation into batches to save memory. (default: False) --eval_on_start [EVAL_ON_START] Whether to run through the entire `evaluation` step at the very beginning of training as a sanity check. (default: False) --eval_use_gather_object [EVAL_USE_GATHER_OBJECT] Whether to run recursively gather object in a nested list/tuple/dictionary of objects from all devices. (default: False) --sortish_sampler [SORTISH_SAMPLER] Whether to use SortishSampler or not. (default: False) --predict_with_generate [PREDICT_WITH_GENERATE] Whether to use generate to calculate generative metrics (ROUGE, BLEU). (default: False) --generation_max_length GENERATION_MAX_LENGTH The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default to the `max_length` value of the model configuration. (default: None) --generation_num_beams GENERATION_NUM_BEAMS The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default to the `num_beams` value of the model configuration. (default: None) --generation_config GENERATION_CONFIG Model id, file path or url pointing to a GenerationConfig json file, to use during prediction. (default: None) --use_badam [USE_BADAM] Whether or not to use the BAdam optimizer. (default: False) --badam_mode {layer,ratio} Whether to use layer-wise or ratio-wise BAdam optimizer. (default: layer) --badam_start_block BADAM_START_BLOCK The starting block index for layer-wise BAdam. (default: None) --badam_switch_mode {ascending,descending,random,fixed} the strategy of picking block to update for layer-wise BAdam. (default: ascending) --badam_switch_interval BADAM_SWITCH_INTERVAL Number of steps to update the block for layer-wise BAdam. Use -1 to disable the block update. (default: 50) --badam_update_ratio BADAM_UPDATE_RATIO The ratio of the update for ratio-wise BAdam. (default: 0.05) --badam_mask_mode {adjacent,scatter} The mode of the mask for BAdam optimizer. `adjacent` means that the trainable parameters are adjacent to each other, `scatter` means that trainable parameters are randomly choosed from the weight. (default: adjacent) --badam_verbose BADAM_VERBOSE The verbosity level of BAdam optimizer. 0 for no print, 1 for print the block prefix, 2 for print trainable parameters. (default: 0) --use_galore [USE_GALORE] Whether or not to use the gradient low-Rank projection (GaLore). (default: False) --galore_target GALORE_TARGET Name(s) of modules to apply GaLore. Use commas to separate multiple modules. Use `all` to specify all the linear modules. (default: all) --galore_rank GALORE_RANK The rank of GaLore gradients. (default: 16) --galore_update_interval GALORE_UPDATE_INTERVAL Number of steps to update the GaLore projection. (default: 200) --galore_scale GALORE_SCALE GaLore scaling coefficient. (default: 0.25) --galore_proj_type {std,reverse_std,right,left,full} Type of GaLore projection. (default: std) --galore_layerwise [GALORE_LAYERWISE] Whether or not to enable layer-wise update to further save memory. (default: False) --pref_beta PREF_BETA The beta parameter in the preference loss. (default: 0.1) --pref_ftx PREF_FTX The supervised fine-tuning loss coefficient in DPO training. (default: 0.0) --pref_loss {sigmoid,hinge,ipo,kto_pair,orpo,simpo} The type of DPO loss to use. (default: sigmoid) --dpo_label_smoothing DPO_LABEL_SMOOTHING The robust DPO label smoothing parameter in cDPO that should be between 0 and 0.5. (default: 0.0) --kto_chosen_weight KTO_CHOSEN_WEIGHT The weight factor of the desirable losses in KTO training. (default: 1.0) --kto_rejected_weight KTO_REJECTED_WEIGHT The weight factor of the undesirable losses in KTO training. (default: 1.0) --simpo_gamma SIMPO_GAMMA The target reward margin term in SimPO loss. (default: 0.5) --ppo_buffer_size PPO_BUFFER_SIZE The number of mini-batches to make experience buffer in a PPO optimization step. (default: 1) --ppo_epochs PPO_EPOCHS The number of epochs to perform in a PPO optimization step. (default: 4) --ppo_score_norm [PPO_SCORE_NORM] Use score normalization in PPO training. (default: False) --ppo_target PPO_TARGET Target KL value for adaptive KL control in PPO training. (default: 6.0) --ppo_whiten_rewards [PPO_WHITEN_REWARDS] Whiten the rewards before compute advantages in PPO training. (default: False) --ref_model REF_MODEL Path to the reference model used for the PPO or DPO training. (default: None) --ref_model_adapters REF_MODEL_ADAPTERS Path to the adapters of the reference model. (default: None) --ref_model_quantization_bit REF_MODEL_QUANTIZATION_BIT The number of bits to quantize the reference model. (default: None) --reward_model REWARD_MODEL Path to the reward model used for the PPO training. (default: None) --reward_model_adapters REWARD_MODEL_ADAPTERS Path to the adapters of the reward model. (default: None) --reward_model_quantization_bit REWARD_MODEL_QUANTIZATION_BIT The number of bits to quantize the reward model. (default: None) --reward_model_type {lora,full,api} The type of the reward model in PPO training. Lora model only supports lora training. (default: lora) --additional_target ADDITIONAL_TARGET Name(s) of modules apart from LoRA layers to be set as trainable and saved in the final checkpoint. Use commas to separate multiple modules. (default: None) --lora_alpha LORA_ALPHA The scale factor for LoRA fine-tuning (default: lora_rank * 2). (default: None) --lora_dropout LORA_DROPOUT Dropout rate for the LoRA fine-tuning. (default: 0.0) --lora_rank LORA_RANK The intrinsic dimension for LoRA fine-tuning. (default: 8) --lora_target LORA_TARGET Name(s) of target modules to apply LoRA. Use commas to separate multiple modules. Use `all` to specify all the linear modules. (default: all) --loraplus_lr_ratio LORAPLUS_LR_RATIO LoRA plus learning rate ratio (lr_B / lr_A). (default: None) --loraplus_lr_embedding LORAPLUS_LR_EMBEDDING LoRA plus learning rate for lora embedding layers. (default: 1e-06) --use_rslora [USE_RSLORA] Whether or not to use the rank stabilization scaling factor for LoRA layer. (default: False) --use_dora [USE_DORA] Whether or not to use the weight-decomposed lora method (DoRA). (default: False) --pissa_init [PISSA_INIT] Whether or not to initialize a PiSSA adapter. (default: False) --pissa_iter PISSA_ITER The number of iteration steps performed by FSVD in PiSSA. Use -1 to disable it. (default: 16) --pissa_convert [PISSA_CONVERT] Whether or not to convert the PiSSA adapter to a normal LoRA adapter. (default: False) --create_new_adapter [CREATE_NEW_ADAPTER] Whether or not to create a new adapter with randomly initialized weight. (default: False) --freeze_trainable_layers FREEZE_TRAINABLE_LAYERS The number of trainable layers for freeze (partial- parameter) fine-tuning. Positive numbers mean the last n layers are set as trainable, negative numbers mean the first n layers are set as trainable. (default: 2) --freeze_trainable_modules FREEZE_TRAINABLE_MODULES Name(s) of trainable modules for freeze (partial- parameter) fine-tuning. Use commas to separate multiple modules. Use `all` to specify all the available modules. (default: all) --freeze_extra_modules FREEZE_EXTRA_MODULES Name(s) of modules apart from hidden layers to be set as trainable for freeze (partial-parameter) fine- tuning. Use commas to separate multiple modules. (default: None) --pure_bf16 [PURE_BF16] Whether or not to train model in purely bf16 precision (without AMP). (default: False) --stage {pt,sft,rm,ppo,dpo,kto} Which stage will be performed in training. (default: sft) --finetuning_type {lora,freeze,full} Which fine-tuning method to use. (default: lora) --use_llama_pro [USE_LLAMA_PRO] Whether or not to make only the parameters in the expanded blocks trainable. (default: False) --use_adam_mini [USE_ADAM_MINI] Whether or not to use the Adam-mini optimizer. (default: False) --freeze_vision_tower [FREEZE_VISION_TOWER] Whether ot not to freeze vision tower in MLLM training. (default: True) --no_freeze_vision_tower Whether ot not to freeze vision tower in MLLM training. (default: False) --train_mm_proj_only [TRAIN_MM_PROJ_ONLY] Whether or not to train the multimodal projector for MLLM only. (default: False) --compute_accuracy [COMPUTE_ACCURACY] Whether or not to compute the token-level accuracy at evaluation. (default: False) --plot_loss [PLOT_LOSS] Whether or not to save the training loss curves. (default: False) --do_sample [DO_SAMPLE] Whether or not to use sampling, use greedy decoding otherwise. (default: True) --no_do_sample Whether or not to use sampling, use greedy decoding otherwise. (default: False) --temperature TEMPERATURE The value used to modulate the next token probabilities. (default: 0.95) --top_p TOP_P The smallest set of most probable tokens with probabilities that add up to top_p or higher are kept. (default: 0.7) --top_k TOP_K The number of highest probability vocabulary tokens to keep for top-k filtering. (default: 50) --num_beams NUM_BEAMS Number of beams for beam search. 1 means no beam search. (default: 1) --max_length MAX_LENGTH The maximum length the generated tokens can have. It can be overridden by max_new_tokens. (default: 1024) --max_new_tokens MAX_NEW_TOKENS The maximum numbers of tokens to generate, ignoring the number of tokens in the prompt. (default: 1024) --repetition_penalty REPETITION_PENALTY The parameter for repetition penalty. 1.0 means no penalty. (default: 1.0) --length_penalty LENGTH_PENALTY Exponential penalty to the length that is used with beam-based generation. (default: 1.0) --default_system DEFAULT_SYSTEM Default system message to use in chat completion. (default: None) usage: launcher.py [-h] --model_name_or_path MODEL_NAME_OR_PATH [--adapter_name_or_path ADAPTER_NAME_OR_PATH] [--adapter_folder ADAPTER_FOLDER] [--cache_dir CACHE_DIR] [--use_fast_tokenizer [USE_FAST_TOKENIZER]] [--no_use_fast_tokenizer] [--resize_vocab [RESIZE_VOCAB]] [--split_special_tokens [SPLIT_SPECIAL_TOKENS]] [--new_special_tokens NEW_SPECIAL_TOKENS] [--model_revision MODEL_REVISION] [--low_cpu_mem_usage [LOW_CPU_MEM_USAGE]] [--no_low_cpu_mem_usage] [--quantization_method {bitsandbytes,hqq,eetq}] [--quantization_bit QUANTIZATION_BIT] [--quantization_type {fp4,nf4}] [--double_quantization [DOUBLE_QUANTIZATION]] [--no_double_quantization] [--quantization_device_map {auto}] [--rope_scaling {linear,dynamic}] [--flash_attn {auto,disabled,sdpa,fa2}] [--shift_attn [SHIFT_ATTN]] [--mixture_of_depths {convert,load}] [--use_unsloth [USE_UNSLOTH]] [--visual_inputs [VISUAL_INPUTS]] [--moe_aux_loss_coef MOE_AUX_LOSS_COEF] [--disable_gradient_checkpointing [DISABLE_GRADIENT_CHECKPOINTING]] [--upcast_layernorm [UPCAST_LAYERNORM]] [--upcast_lmhead_output [UPCAST_LMHEAD_OUTPUT]] [--train_from_scratch [TRAIN_FROM_SCRATCH]] [--infer_backend {huggingface,vllm}] [--vllm_maxlen VLLM_MAXLEN] [--vllm_gpu_util VLLM_GPU_UTIL] [--vllm_enforce_eager [VLLM_ENFORCE_EAGER]] [--vllm_max_lora_rank VLLM_MAX_LORA_RANK] [--offload_folder OFFLOAD_FOLDER] [--use_cache [USE_CACHE]] [--no_use_cache] [--infer_dtype {auto,float16,bfloat16,float32}] [--hf_hub_token HF_HUB_TOKEN] [--ms_hub_token MS_HUB_TOKEN] [--export_dir EXPORT_DIR] [--export_size EXPORT_SIZE] [--export_device {cpu,auto}] [--export_quantization_bit EXPORT_QUANTIZATION_BIT] [--export_quantization_dataset EXPORT_QUANTIZATION_DATASET] [--export_quantization_nsamples EXPORT_QUANTIZATION_NSAMPLES] [--export_quantization_maxlen EXPORT_QUANTIZATION_MAXLEN] [--export_legacy_format [EXPORT_LEGACY_FORMAT]] [--export_hub_model_id EXPORT_HUB_MODEL_ID] [--print_param_status [PRINT_PARAM_STATUS]] [--template TEMPLATE] [--dataset DATASET] [--eval_dataset EVAL_DATASET] [--dataset_dir DATASET_DIR] [--cutoff_len CUTOFF_LEN] [--train_on_prompt [TRAIN_ON_PROMPT]] [--mask_history [MASK_HISTORY]] [--streaming [STREAMING]] [--buffer_size BUFFER_SIZE] [--mix_strategy {concat,interleave_under,interleave_over}] [--interleave_probs INTERLEAVE_PROBS] [--overwrite_cache [OVERWRITE_CACHE]] [--preprocessing_num_workers PREPROCESSING_NUM_WORKERS] [--max_samples MAX_SAMPLES] [--eval_num_beams EVAL_NUM_BEAMS] [--ignore_pad_token_for_loss [IGNORE_PAD_TOKEN_FOR_LOSS]] [--no_ignore_pad_token_for_loss] [--val_size VAL_SIZE] [--packing PACKING] [--neat_packing [NEAT_PACKING]] [--tool_format TOOL_FORMAT] [--tokenized_path TOKENIZED_PATH] --output_dir OUTPUT_DIR [--overwrite_output_dir [OVERWRITE_OUTPUT_DIR]] [--do_train [DO_TRAIN]] [--do_eval [DO_EVAL]] [--do_predict [DO_PREDICT]] [--eval_strategy {no,steps,epoch}] [--prediction_loss_only [PREDICTION_LOSS_ONLY]] [--per_device_train_batch_size PER_DEVICE_TRAIN_BATCH_SIZE] [--per_device_eval_batch_size PER_DEVICE_EVAL_BATCH_SIZE] [--per_gpu_train_batch_size PER_GPU_TRAIN_BATCH_SIZE] [--per_gpu_eval_batch_size PER_GPU_EVAL_BATCH_SIZE] [--gradient_accumulation_steps GRADIENT_ACCUMULATION_STEPS] [--eval_accumulation_steps EVAL_ACCUMULATION_STEPS] [--eval_delay EVAL_DELAY] [--torch_empty_cache_steps TORCH_EMPTY_CACHE_STEPS] [--learning_rate LEARNING_RATE] [--weight_decay WEIGHT_DECAY] [--adam_beta1 ADAM_BETA1] [--adam_beta2 ADAM_BETA2] [--adam_epsilon ADAM_EPSILON] [--max_grad_norm MAX_GRAD_NORM] [--num_train_epochs NUM_TRAIN_EPOCHS] [--max_steps MAX_STEPS] [--lr_scheduler_type {linear,cosine,cosine_with_restarts,polynomial,constant,constant_with_warmup,inverse_sqrt,reduce_lr_on_plateau,cosine_with_min_lr,warmup_stable_decay}] [--lr_scheduler_kwargs LR_SCHEDULER_KWARGS] [--warmup_ratio WARMUP_RATIO] [--warmup_steps WARMUP_STEPS] [--log_level {detail,debug,info,warning,error,critical,passive}] [--log_level_replica {detail,debug,info,warning,error,critical,passive}] [--log_on_each_node [LOG_ON_EACH_NODE]] [--no_log_on_each_node] [--logging_dir LOGGING_DIR] [--logging_strategy {no,steps,epoch}] [--logging_first_step [LOGGING_FIRST_STEP]] [--logging_steps LOGGING_STEPS] [--logging_nan_inf_filter [LOGGING_NAN_INF_FILTER]] [--no_logging_nan_inf_filter] [--save_strategy {no,steps,epoch}] [--save_steps SAVE_STEPS] [--save_total_limit SAVE_TOTAL_LIMIT] [--save_safetensors [SAVE_SAFETENSORS]] [--no_save_safetensors] [--save_on_each_node [SAVE_ON_EACH_NODE]] [--save_only_model [SAVE_ONLY_MODEL]] [--restore_callback_states_from_checkpoint [RESTORE_CALLBACK_STATES_FROM_CHECKPOINT]] [--no_cuda [NO_CUDA]] [--use_cpu [USE_CPU]] [--use_mps_device [USE_MPS_DEVICE]] [--seed SEED] [--data_seed DATA_SEED] [--jit_mode_eval [JIT_MODE_EVAL]] [--use_ipex [USE_IPEX]] [--bf16 [BF16]] [--fp16 [FP16]] [--fp16_opt_level FP16_OPT_LEVEL] [--half_precision_backend {auto,apex,cpu_amp}] [--bf16_full_eval [BF16_FULL_EVAL]] [--fp16_full_eval [FP16_FULL_EVAL]] [--tf32 TF32] [--local_rank LOCAL_RANK] [--ddp_backend {nccl,gloo,mpi,ccl,hccl,cncl}] [--tpu_num_cores TPU_NUM_CORES] [--tpu_metrics_debug [TPU_METRICS_DEBUG]] [--debug DEBUG [DEBUG ...]] [--dataloader_drop_last [DATALOADER_DROP_LAST]] [--eval_steps EVAL_STEPS] [--dataloader_num_workers DATALOADER_NUM_WORKERS] [--dataloader_prefetch_factor DATALOADER_PREFETCH_FACTOR] [--past_index PAST_INDEX] [--run_name RUN_NAME] [--disable_tqdm DISABLE_TQDM] [--remove_unused_columns [REMOVE_UNUSED_COLUMNS]] [--no_remove_unused_columns] [--label_names LABEL_NAMES [LABEL_NAMES ...]] [--load_best_model_at_end [LOAD_BEST_MODEL_AT_END]] [--metric_for_best_model METRIC_FOR_BEST_MODEL] [--greater_is_better GREATER_IS_BETTER] [--ignore_data_skip [IGNORE_DATA_SKIP]] [--fsdp FSDP] [--fsdp_min_num_params FSDP_MIN_NUM_PARAMS] [--fsdp_config FSDP_CONFIG] [--fsdp_transformer_layer_cls_to_wrap FSDP_TRANSFORMER_LAYER_CLS_TO_WRAP] [--accelerator_config ACCELERATOR_CONFIG] [--deepspeed DEEPSPEED] [--label_smoothing_factor LABEL_SMOOTHING_FACTOR] [--optim {adamw_hf,adamw_torch,adamw_torch_fused,adamw_torch_xla,adamw_torch_npu_fused,adamw_apex_fused,adafactor,adamw_anyprecision,sgd,adagrad,adamw_bnb_8bit,adamw_8bit,lion_8bit,lion_32bit,paged_adamw_32bit,paged_adamw_8bit,paged_lion_32bit,paged_lion_8bit,rmsprop,rmsprop_bnb,rmsprop_bnb_8bit,rmsprop_bnb_32bit,galore_adamw,galore_adamw_8bit,galore_adafactor,galore_adamw_layerwise,galore_adamw_8bit_layerwise,galore_adafactor_layerwise,lomo,adalomo}] [--optim_args OPTIM_ARGS] [--adafactor [ADAFACTOR]] [--group_by_length [GROUP_BY_LENGTH]] [--length_column_name LENGTH_COLUMN_NAME] [--report_to REPORT_TO] [--ddp_find_unused_parameters DDP_FIND_UNUSED_PARAMETERS] [--ddp_bucket_cap_mb DDP_BUCKET_CAP_MB] [--ddp_broadcast_buffers DDP_BROADCAST_BUFFERS] [--dataloader_pin_memory [DATALOADER_PIN_MEMORY]] [--no_dataloader_pin_memory] [--dataloader_persistent_workers [DATALOADER_PERSISTENT_WORKERS]] [--skip_memory_metrics [SKIP_MEMORY_METRICS]] [--no_skip_memory_metrics] [--use_legacy_prediction_loop [USE_LEGACY_PREDICTION_LOOP]] [--push_to_hub [PUSH_TO_HUB]] [--resume_from_checkpoint RESUME_FROM_CHECKPOINT] [--hub_model_id HUB_MODEL_ID] [--hub_strategy {end,every_save,checkpoint,all_checkpoints}] [--hub_token HUB_TOKEN] [--hub_private_repo [HUB_PRIVATE_REPO]] [--hub_always_push [HUB_ALWAYS_PUSH]] [--gradient_checkpointing [GRADIENT_CHECKPOINTING]] [--gradient_checkpointing_kwargs GRADIENT_CHECKPOINTING_KWARGS] [--include_inputs_for_metrics [INCLUDE_INPUTS_FOR_METRICS]] [--eval_do_concat_batches [EVAL_DO_CONCAT_BATCHES]] [--no_eval_do_concat_batches] [--fp16_backend {auto,apex,cpu_amp}] [--evaluation_strategy {no,steps,epoch}] [--push_to_hub_model_id PUSH_TO_HUB_MODEL_ID] [--push_to_hub_organization PUSH_TO_HUB_ORGANIZATION] [--push_to_hub_token PUSH_TO_HUB_TOKEN] [--mp_parameters MP_PARAMETERS] [--auto_find_batch_size [AUTO_FIND_BATCH_SIZE]] [--full_determinism [FULL_DETERMINISM]] [--torchdynamo TORCHDYNAMO] [--ray_scope RAY_SCOPE] [--ddp_timeout DDP_TIMEOUT] [--torch_compile [TORCH_COMPILE]] [--torch_compile_backend TORCH_COMPILE_BACKEND] [--torch_compile_mode TORCH_COMPILE_MODE] [--dispatch_batches DISPATCH_BATCHES] [--split_batches SPLIT_BATCHES] [--include_tokens_per_second [INCLUDE_TOKENS_PER_SECOND]] [--include_num_input_tokens_seen [INCLUDE_NUM_INPUT_TOKENS_SEEN]] [--neftune_noise_alpha NEFTUNE_NOISE_ALPHA] [--optim_target_modules OPTIM_TARGET_MODULES] [--batch_eval_metrics [BATCH_EVAL_METRICS]] [--eval_on_start [EVAL_ON_START]] [--eval_use_gather_object [EVAL_USE_GATHER_OBJECT]] [--sortish_sampler [SORTISH_SAMPLER]] [--predict_with_generate [PREDICT_WITH_GENERATE]] [--generation_max_length GENERATION_MAX_LENGTH] [--generation_num_beams GENERATION_NUM_BEAMS] [--generation_config GENERATION_CONFIG] [--use_badam [USE_BADAM]] [--badam_mode {layer,ratio}] [--badam_start_block BADAM_START_BLOCK] [--badam_switch_mode {ascending,descending,random,fixed}] [--badam_switch_interval BADAM_SWITCH_INTERVAL] [--badam_update_ratio BADAM_UPDATE_RATIO] [--badam_mask_mode {adjacent,scatter}] [--badam_verbose BADAM_VERBOSE] [--use_galore [USE_GALORE]] [--galore_target GALORE_TARGET] [--galore_rank GALORE_RANK] [--galore_update_interval GALORE_UPDATE_INTERVAL] [--galore_scale GALORE_SCALE] [--galore_proj_type {std,reverse_std,right,left,full}] [--galore_layerwise [GALORE_LAYERWISE]] [--pref_beta PREF_BETA] [--pref_ftx PREF_FTX] [--pref_loss {sigmoid,hinge,ipo,kto_pair,orpo,simpo}] [--dpo_label_smoothing DPO_LABEL_SMOOTHING] [--kto_chosen_weight KTO_CHOSEN_WEIGHT] [--kto_rejected_weight KTO_REJECTED_WEIGHT] [--simpo_gamma SIMPO_GAMMA] [--ppo_buffer_size PPO_BUFFER_SIZE] [--ppo_epochs PPO_EPOCHS] [--ppo_score_norm [PPO_SCORE_NORM]] [--ppo_target PPO_TARGET] [--ppo_whiten_rewards [PPO_WHITEN_REWARDS]] [--ref_model REF_MODEL] [--ref_model_adapters REF_MODEL_ADAPTERS] [--ref_model_quantization_bit REF_MODEL_QUANTIZATION_BIT] [--reward_model REWARD_MODEL] [--reward_model_adapters REWARD_MODEL_ADAPTERS] [--reward_model_quantization_bit REWARD_MODEL_QUANTIZATION_BIT] [--reward_model_type {lora,full,api}] [--additional_target ADDITIONAL_TARGET] [--lora_alpha LORA_ALPHA] [--lora_dropout LORA_DROPOUT] [--lora_rank LORA_RANK] [--lora_target LORA_TARGET] [--loraplus_lr_ratio LORAPLUS_LR_RATIO] [--loraplus_lr_embedding LORAPLUS_LR_EMBEDDING] [--use_rslora [USE_RSLORA]] [--use_dora [USE_DORA]] [--pissa_init [PISSA_INIT]] [--pissa_iter PISSA_ITER] [--pissa_convert [PISSA_CONVERT]] [--create_new_adapter [CREATE_NEW_ADAPTER]] [--freeze_trainable_layers FREEZE_TRAINABLE_LAYERS] [--freeze_trainable_modules FREEZE_TRAINABLE_MODULES] [--freeze_extra_modules FREEZE_EXTRA_MODULES] [--pure_bf16 [PURE_BF16]] [--stage {pt,sft,rm,ppo,dpo,kto}] [--finetuning_type {lora,freeze,full}] [--use_llama_pro [USE_LLAMA_PRO]] [--use_adam_mini [USE_ADAM_MINI]] [--freeze_vision_tower [FREEZE_VISION_TOWER]] [--no_freeze_vision_tower] [--train_mm_proj_only [TRAIN_MM_PROJ_ONLY]] [--compute_accuracy [COMPUTE_ACCURACY]] [--plot_loss [PLOT_LOSS]] [--do_sample [DO_SAMPLE]] [--no_do_sample] [--temperature TEMPERATURE] [--top_p TOP_P] [--top_k TOP_K] [--num_beams NUM_BEAMS] [--max_length MAX_LENGTH] [--max_new_tokens MAX_NEW_TOKENS] [--repetition_penalty REPETITION_PENALTY] [--length_penalty LENGTH_PENALTY] [--default_system DEFAULT_SYSTEM] optional arguments: -h, --help show this help message and exit --model_name_or_path MODEL_NAME_OR_PATH Path to the model weight or identifier from huggingface.co/models or modelscope.cn/models. (default: None) --adapter_name_or_path ADAPTER_NAME_OR_PATH Path to the adapter weight or identifier from huggingface.co/models. Use commas to separate multiple adapters. (default: None) --adapter_folder ADAPTER_FOLDER The folder containing the adapter weights to load. (default: None) --cache_dir CACHE_DIR Where to store the pre-trained models downloaded from huggingface.co or modelscope.cn. (default: None) --use_fast_tokenizer [USE_FAST_TOKENIZER] Whether or not to use one of the fast tokenizer (backed by the tokenizers library). (default: True) --no_use_fast_tokenizer Whether or not to use one of the fast tokenizer (backed by the tokenizers library). (default: False) --resize_vocab [RESIZE_VOCAB] Whether or not to resize the tokenizer vocab and the embedding layers. (default: False) --split_special_tokens [SPLIT_SPECIAL_TOKENS] Whether or not the special tokens should be split during the tokenization process. (default: False) --new_special_tokens NEW_SPECIAL_TOKENS Special tokens to be added into the tokenizer. Use commas to separate multiple tokens. (default: None) --model_revision MODEL_REVISION The specific model version to use (can be a branch name, tag name or commit id). (default: main) --low_cpu_mem_usage [LOW_CPU_MEM_USAGE] Whether or not to use memory-efficient model loading. (default: True) --no_low_cpu_mem_usage Whether or not to use memory-efficient model loading. (default: False) --quantization_method {bitsandbytes,hqq,eetq} Quantization method to use for on-the-fly quantization. (default: bitsandbytes) --quantization_bit QUANTIZATION_BIT The number of bits to quantize the model using bitsandbytes. (default: None) --quantization_type {fp4,nf4} Quantization data type to use in int4 training. (default: nf4) --double_quantization [DOUBLE_QUANTIZATION] Whether or not to use double quantization in int4 training. (default: True) --no_double_quantization Whether or not to use double quantization in int4 training. (default: False) --quantization_device_map {auto} Device map used to infer the 4-bit quantized model, needs bitsandbytes>=0.43.0. (default: None) --rope_scaling {linear,dynamic} Which scaling strategy should be adopted for the RoPE embeddings. (default: None) --flash_attn {auto,disabled,sdpa,fa2} Enable FlashAttention for faster training and inference. (default: auto) --shift_attn [SHIFT_ATTN] Enable shift short attention (S^2-Attn) proposed by LongLoRA. (default: False) --mixture_of_depths {convert,load} Convert the model to mixture-of-depths (MoD) or load the MoD model. (default: None) --use_unsloth [USE_UNSLOTH] Whether or not to use unsloth's optimization for the LoRA training. (default: False) --visual_inputs [VISUAL_INPUTS] Whethor or not to use multimodal LLM that accepts visual inputs. (default: False) --moe_aux_loss_coef MOE_AUX_LOSS_COEF Coefficient of the auxiliary router loss in mixture- of-experts model. (default: None) --disable_gradient_checkpointing [DISABLE_GRADIENT_CHECKPOINTING] Whether or not to disable gradient checkpointing. (default: False) --upcast_layernorm [UPCAST_LAYERNORM] Whether or not to upcast the layernorm weights in fp32. (default: False) --upcast_lmhead_output [UPCAST_LMHEAD_OUTPUT] Whether or not to upcast the output of lm_head in fp32. (default: False) --train_from_scratch [TRAIN_FROM_SCRATCH] Whether or not to randomly initialize the model weights. (default: False) --infer_backend {huggingface,vllm} Backend engine used at inference. (default: huggingface) --vllm_maxlen VLLM_MAXLEN Maximum sequence (prompt + response) length of the vLLM engine. (default: 2048) --vllm_gpu_util VLLM_GPU_UTIL The fraction of GPU memory in (0,1) to be used for the vLLM engine. (default: 0.9) --vllm_enforce_eager [VLLM_ENFORCE_EAGER] Whether or not to disable CUDA graph in the vLLM engine. (default: False) --vllm_max_lora_rank VLLM_MAX_LORA_RANK Maximum rank of all LoRAs in the vLLM engine. (default: 32) --offload_folder OFFLOAD_FOLDER Path to offload model weights. (default: offload) --use_cache [USE_CACHE] Whether or not to use KV cache in generation. (default: True) --no_use_cache Whether or not to use KV cache in generation. (default: False) --infer_dtype {auto,float16,bfloat16,float32} Data type for model weights and activations at inference. (default: auto) --hf_hub_token HF_HUB_TOKEN Auth token to log in with Hugging Face Hub. (default: None) --ms_hub_token MS_HUB_TOKEN Auth token to log in with ModelScope Hub. (default: None) --export_dir EXPORT_DIR Path to the directory to save the exported model. (default: None) --export_size EXPORT_SIZE The file shard size (in GB) of the exported model. (default: 1) --export_device {cpu,auto} The device used in model export, use `auto` to accelerate exporting. (default: cpu) --export_quantization_bit EXPORT_QUANTIZATION_BIT The number of bits to quantize the exported model. (default: None) --export_quantization_dataset EXPORT_QUANTIZATION_DATASET Path to the dataset or dataset name to use in quantizing the exported model. (default: None) --export_quantization_nsamples EXPORT_QUANTIZATION_NSAMPLES The number of samples used for quantization. (default: 128) --export_quantization_maxlen EXPORT_QUANTIZATION_MAXLEN The maximum length of the model inputs used for quantization. (default: 1024) --export_legacy_format [EXPORT_LEGACY_FORMAT] Whether or not to save the `.bin` files instead of `.safetensors`. (default: False) --export_hub_model_id EXPORT_HUB_MODEL_ID The name of the repository if push the model to the Hugging Face hub. (default: None) --print_param_status [PRINT_PARAM_STATUS] For debugging purposes, print the status of the parameters in the model. (default: False) --template TEMPLATE Which template to use for constructing prompts in training and inference. (default: None) --dataset DATASET The name of dataset(s) to use for training. Use commas to separate multiple datasets. (default: None) --eval_dataset EVAL_DATASET The name of dataset(s) to use for evaluation. Use commas to separate multiple datasets. (default: None) --dataset_dir DATASET_DIR Path to the folder containing the datasets. (default: data) --cutoff_len CUTOFF_LEN The cutoff length of the tokenized inputs in the dataset. (default: 1024) --train_on_prompt [TRAIN_ON_PROMPT] Whether or not to disable the mask on the prompt. (default: False) --mask_history [MASK_HISTORY] Whether or not to mask the history and train on the last turn only. (default: False) --streaming [STREAMING] Enable dataset streaming. (default: False) --buffer_size BUFFER_SIZE Size of the buffer to randomly sample examples from in dataset streaming. (default: 16384) --mix_strategy {concat,interleave_under,interleave_over} Strategy to use in dataset mixing (concat/interleave) (undersampling/oversampling). (default: concat) --interleave_probs INTERLEAVE_PROBS Probabilities to sample data from datasets. Use commas to separate multiple datasets. (default: None) --overwrite_cache [OVERWRITE_CACHE] Overwrite the cached training and evaluation sets. (default: False) --preprocessing_num_workers PREPROCESSING_NUM_WORKERS The number of processes to use for the pre-processing. (default: None) --max_samples MAX_SAMPLES For debugging purposes, truncate the number of examples for each dataset. (default: None) --eval_num_beams EVAL_NUM_BEAMS Number of beams to use for evaluation. This argument will be passed to `model.generate` (default: None) --ignore_pad_token_for_loss [IGNORE_PAD_TOKEN_FOR_LOSS] Whether or not to ignore the tokens corresponding to the pad label in loss computation. (default: True) --no_ignore_pad_token_for_loss Whether or not to ignore the tokens corresponding to the pad label in loss computation. (default: False) --val_size VAL_SIZE Size of the development set, should be an integer or a float in range `[0,1)`. (default: 0.0) --packing PACKING Enable sequences packing in training. Will automatically enable in pre-training. (default: None) --neat_packing [NEAT_PACKING] Enable sequence packing without cross-attention. (default: False) --tool_format TOOL_FORMAT Tool format to use for constructing function calling examples. (default: None) --tokenized_path TOKENIZED_PATH Path to save or load the tokenized datasets. (default: None) --output_dir OUTPUT_DIR The output directory where the model predictions and checkpoints will be written. (default: None) --overwrite_output_dir [OVERWRITE_OUTPUT_DIR] Overwrite the content of the output directory. Use this to continue training if output_dir points to a checkpoint directory. (default: False) --do_train [DO_TRAIN] Whether to run training. (default: False) --do_eval [DO_EVAL] Whether to run eval on the dev set. (default: False) --do_predict [DO_PREDICT] Whether to run predictions on the test set. (default: False) --eval_strategy {no,steps,epoch} The evaluation strategy to use. (default: no) --prediction_loss_only [PREDICTION_LOSS_ONLY] When performing evaluation and predictions, only returns the loss. (default: False) --per_device_train_batch_size PER_DEVICE_TRAIN_BATCH_SIZE Batch size per GPU/TPU/MPS/NPU core/CPU for training. (default: 8) --per_device_eval_batch_size PER_DEVICE_EVAL_BATCH_SIZE Batch size per GPU/TPU/MPS/NPU core/CPU for evaluation. (default: 8) --per_gpu_train_batch_size PER_GPU_TRAIN_BATCH_SIZE Deprecated, the use of `--per_device_train_batch_size` is preferred. Batch size per GPU/TPU core/CPU for training. (default: None) --per_gpu_eval_batch_size PER_GPU_EVAL_BATCH_SIZE Deprecated, the use of `--per_device_eval_batch_size` is preferred. Batch size per GPU/TPU core/CPU for evaluation. (default: None) --gradient_accumulation_steps GRADIENT_ACCUMULATION_STEPS Number of updates steps to accumulate before performing a backward/update pass. (default: 1) --eval_accumulation_steps EVAL_ACCUMULATION_STEPS Number of predictions steps to accumulate before moving the tensors to the CPU. (default: None) --eval_delay EVAL_DELAY Number of epochs or steps to wait for before the first evaluation can be performed, depending on the eval_strategy. (default: 0) --torch_empty_cache_steps TORCH_EMPTY_CACHE_STEPS Number of steps to wait before calling `torch..empty_cache()`.This can help avoid CUDA out-of-memory errors by lowering peak VRAM usage at a cost of about [10{'option_strings': ['-- torch_empty_cache_steps'], 'dest': 'torch_empty_cache_steps', 'nargs': None, 'const': None, 'default': None, 'type': 'int', 'choices': None, 'required': False, 'help': 'Number of steps to wait before calling `torch..empty_cache()`.This can help avoid CUDA out-of-memory errors by lowering peak VRAM usage at a cost of about [10% slower performance] (https://github.com/huggingface/transformers/issues/31 372).If left unset or set to None, cache will not be emptied.', 'metavar': None, 'container': , 'prog': 'launcher.py'}lower performance](https://githu b.com/huggingface/transformers/issues/31372).If left unset or set to None, cache will not be emptied. (default: None) --learning_rate LEARNING_RATE The initial learning rate for AdamW. (default: 5e-05) --weight_decay WEIGHT_DECAY Weight decay for AdamW if we apply some. (default: 0.0) --adam_beta1 ADAM_BETA1 Beta1 for AdamW optimizer (default: 0.9) --adam_beta2 ADAM_BETA2 Beta2 for AdamW optimizer (default: 0.999) --adam_epsilon ADAM_EPSILON Epsilon for AdamW optimizer. (default: 1e-08) --max_grad_norm MAX_GRAD_NORM Max gradient norm. (default: 1.0) --num_train_epochs NUM_TRAIN_EPOCHS Total number of training epochs to perform. (default: 3.0) --max_steps MAX_STEPS If > 0: set total number of training steps to perform. Override num_train_epochs. (default: -1) --lr_scheduler_type {linear,cosine,cosine_with_restarts,polynomial,constant,constant_with_warmup,inverse_sqrt,reduce_lr_on_plateau,cosine_with_min_lr,warmup_stable_decay} The scheduler type to use. (default: linear) --lr_scheduler_kwargs LR_SCHEDULER_KWARGS Extra parameters for the lr_scheduler such as {'num_cycles': 1} for the cosine with hard restarts. (default: {}) --warmup_ratio WARMUP_RATIO Linear warmup over warmup_ratio fraction of total steps. (default: 0.0) --warmup_steps WARMUP_STEPS Linear warmup over warmup_steps. (default: 0) --log_level {detail,debug,info,warning,error,critical,passive} Logger log level to use on the main node. Possible choices are the log levels as strings: 'debug', 'info', 'warning', 'error' and 'critical', plus a 'passive' level which doesn't set anything and lets the application set the level. Defaults to 'passive'. (default: passive) --log_level_replica {detail,debug,info,warning,error,critical,passive} Logger log level to use on replica nodes. Same choices and defaults as ``log_level`` (default: warning) --log_on_each_node [LOG_ON_EACH_NODE] When doing a multinode distributed training, whether to log once per node or just once on the main node. (default: True) --no_log_on_each_node When doing a multinode distributed training, whether to log once per node or just once on the main node. (default: False) --logging_dir LOGGING_DIR Tensorboard log dir. (default: None) --logging_strategy {no,steps,epoch} The logging strategy to use. (default: steps) --logging_first_step [LOGGING_FIRST_STEP] Log the first global_step (default: False) --logging_steps LOGGING_STEPS Log every X updates steps. Should be an integer or a float in range `[0,1)`. If smaller than 1, will be interpreted as ratio of total training steps. (default: 500) --logging_nan_inf_filter [LOGGING_NAN_INF_FILTER] Filter nan and inf losses for logging. (default: True) --no_logging_nan_inf_filter Filter nan and inf losses for logging. (default: False) --save_strategy {no,steps,epoch} The checkpoint save strategy to use. (default: steps) --save_steps SAVE_STEPS Save checkpoint every X updates steps. Should be an integer or a float in range `[0,1)`. If smaller than 1, will be interpreted as ratio of total training steps. (default: 500) --save_total_limit SAVE_TOTAL_LIMIT If a value is passed, will limit the total amount of checkpoints. Deletes the older checkpoints in `output_dir`. When `load_best_model_at_end` is enabled, the 'best' checkpoint according to `metric_for_best_model` will always be retained in addition to the most recent ones. For example, for `save_total_limit=5` and `load_best_model_at_end=True`, the four last checkpoints will always be retained alongside the best model. When `save_total_limit=1` and `load_best_model_at_end=True`, it is possible that two checkpoints are saved: the last one and the best one (if they are different). Default is unlimited checkpoints (default: None) --save_safetensors [SAVE_SAFETENSORS] Use safetensors saving and loading for state dicts instead of default torch.load and torch.save. (default: True) --no_save_safetensors Use safetensors saving and loading for state dicts instead of default torch.load and torch.save. (default: False) --save_on_each_node [SAVE_ON_EACH_NODE] When doing multi-node distributed training, whether to save models and checkpoints on each node, or only on the main one (default: False) --save_only_model [SAVE_ONLY_MODEL] When checkpointing, whether to only save the model, or also the optimizer, scheduler & rng state.Note that when this is true, you won't be able to resume training from checkpoint.This enables you to save storage by not storing the optimizer, scheduler & rng state.You can only load the model using from_pretrained with this option set to True. (default: False) --restore_callback_states_from_checkpoint [RESTORE_CALLBACK_STATES_FROM_CHECKPOINT] Whether to restore the callback states from the checkpoint. If `True`, will override callbacks passed to the `Trainer` if they exist in the checkpoint. (default: False) --no_cuda [NO_CUDA] This argument is deprecated. It will be removed in version 5.0 of 🤗 Transformers. (default: False) --use_cpu [USE_CPU] Whether or not to use cpu. If set to False, we will use cuda/tpu/mps/npu device if available. (default: False) --use_mps_device [USE_MPS_DEVICE] This argument is deprecated. `mps` device will be used if available similar to `cuda` device. It will be removed in version 5.0 of 🤗 Transformers (default: False) --seed SEED Random seed that will be set at the beginning of training. (default: 42) --data_seed DATA_SEED Random seed to be used with data samplers. (default: None) --jit_mode_eval [JIT_MODE_EVAL] Whether or not to use PyTorch jit trace for inference (default: False) --use_ipex [USE_IPEX] Use Intel extension for PyTorch when it is available, installation: 'https://github.com/intel/intel- extension-for-pytorch' (default: False) --bf16 [BF16] Whether to use bf16 (mixed) precision instead of 32-bit. Requires Ampere or higher NVIDIA architecture or using CPU (use_cpu) or Ascend NPU. This is an experimental API and it may change. (default: False) --fp16 [FP16] Whether to use fp16 (mixed) precision instead of 32-bit (default: False) --fp16_opt_level FP16_OPT_LEVEL For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']. See details at https://nvidia.github.io/apex/amp.html (default: O1) --half_precision_backend {auto,apex,cpu_amp} The backend to be used for half precision. (default: auto) --bf16_full_eval [BF16_FULL_EVAL] Whether to use full bfloat16 evaluation instead of 32-bit. This is an experimental API and it may change. (default: False) --fp16_full_eval [FP16_FULL_EVAL] Whether to use full float16 evaluation instead of 32-bit (default: False) --tf32 TF32 Whether to enable tf32 mode, available in Ampere and newer GPU architectures. This is an experimental API and it may change. (default: None) --local_rank LOCAL_RANK For distributed training: local_rank (default: -1) --ddp_backend {nccl,gloo,mpi,ccl,hccl,cncl} The backend to be used for distributed training (default: None) --tpu_num_cores TPU_NUM_CORES TPU: Number of TPU cores (automatically passed by launcher script) (default: None) --tpu_metrics_debug [TPU_METRICS_DEBUG] Deprecated, the use of `--debug tpu_metrics_debug` is preferred. TPU: Whether to print debug metrics (default: False) --debug DEBUG [DEBUG ...] Whether or not to enable debug mode. Current options: `underflow_overflow` (Detect underflow and overflow in activations and weights), `tpu_metrics_debug` (print debug metrics on TPU). (default: None) --dataloader_drop_last [DATALOADER_DROP_LAST] Drop the last incomplete batch if it is not divisible by the batch size. (default: False) --eval_steps EVAL_STEPS Run an evaluation every X steps. Should be an integer or a float in range `[0,1)`. If smaller than 1, will be interpreted as ratio of total training steps. (default: None) --dataloader_num_workers DATALOADER_NUM_WORKERS Number of subprocesses to use for data loading (PyTorch only). 0 means that the data will be loaded in the main process. (default: 0) --dataloader_prefetch_factor DATALOADER_PREFETCH_FACTOR Number of batches loaded in advance by each worker. 2 means there will be a total of 2 * num_workers batches prefetched across all workers. Default is 2 for PyTorch < 2.0.0 and otherwise None. (default: None) --past_index PAST_INDEX If >=0, uses the corresponding part of the output as the past state for next step. (default: -1) --run_name RUN_NAME An optional descriptor for the run. Notably used for wandb, mlflow and comet logging. (default: None) --disable_tqdm DISABLE_TQDM Whether or not to disable the tqdm progress bars. (default: None) --remove_unused_columns [REMOVE_UNUSED_COLUMNS] Remove columns not required by the model when using an nlp.Dataset. (default: True) --no_remove_unused_columns Remove columns not required by the model when using an nlp.Dataset. (default: False) --label_names LABEL_NAMES [LABEL_NAMES ...] The list of keys in your dictionary of inputs that correspond to the labels. (default: None) --load_best_model_at_end [LOAD_BEST_MODEL_AT_END] Whether or not to load the best model found during training at the end of training. When this option is enabled, the best checkpoint will always be saved. See `save_total_limit` for more. (default: False) --metric_for_best_model METRIC_FOR_BEST_MODEL The metric to use to compare two different models. (default: None) --greater_is_better GREATER_IS_BETTER Whether the `metric_for_best_model` should be maximized or not. (default: None) --ignore_data_skip [IGNORE_DATA_SKIP] When resuming training, whether or not to skip the first epochs and batches to get to the same training data. (default: False) --fsdp FSDP Whether or not to use PyTorch Fully Sharded Data Parallel (FSDP) training (in distributed training only). The base option should be `full_shard`, `shard_grad_op` or `no_shard` and you can add CPU- offload to `full_shard` or `shard_grad_op` like this: full_shard offload` or `shard_grad_op offload`. You can add auto-wrap to `full_shard` or `shard_grad_op` with the same syntax: full_shard auto_wrap` or `shard_grad_op auto_wrap`. (default: ) --fsdp_min_num_params FSDP_MIN_NUM_PARAMS This parameter is deprecated. FSDP's minimum number of parameters for Default Auto Wrapping. (useful only when `fsdp` field is passed). (default: 0) --fsdp_config FSDP_CONFIG Config to be used with FSDP (Pytorch Fully Sharded Data Parallel). The value is either a fsdp json config file (e.g., `fsdp_config.json`) or an already loaded json file as `dict`. (default: None) --fsdp_transformer_layer_cls_to_wrap FSDP_TRANSFORMER_LAYER_CLS_TO_WRAP This parameter is deprecated. Transformer layer class name (case-sensitive) to wrap, e.g, `BertLayer`, `GPTJBlock`, `T5Block` .... (useful only when `fsdp` flag is passed). (default: None) --accelerator_config ACCELERATOR_CONFIG Config to be used with the internal Accelerator object initializtion. The value is either a accelerator json config file (e.g., `accelerator_config.json`) or an already loaded json file as `dict`. (default: None) --deepspeed DEEPSPEED Enable deepspeed and pass the path to deepspeed json config file (e.g. `ds_config.json`) or an already loaded json file as a dict (default: None) --label_smoothing_factor LABEL_SMOOTHING_FACTOR The label smoothing epsilon to apply (zero means no label smoothing). (default: 0.0) --optim {adamw_hf,adamw_torch,adamw_torch_fused,adamw_torch_xla,adamw_torch_npu_fused,adamw_apex_fused,adafactor,adamw_anyprecision,sgd,adagrad,adamw_bnb_8bit,adamw_8bit,lion_8bit,lion_32bit,paged_adamw_32bit,paged_adamw_8bit,paged_lion_32bit,paged_lion_8bit,rmsprop,rmsprop_bnb,rmsprop_bnb_8bit,rmsprop_bnb_32bit,galore_adamw,galore_adamw_8bit,galore_adafactor,galore_adamw_layerwise,galore_adamw_8bit_layerwise,galore_adafactor_layerwise,lomo,adalomo} The optimizer to use. (default: adamw_torch) --optim_args OPTIM_ARGS Optional arguments to supply to optimizer. (default: None) --adafactor [ADAFACTOR] Whether or not to replace AdamW by Adafactor. (default: False) --group_by_length [GROUP_BY_LENGTH] Whether or not to group samples of roughly the same length together when batching. (default: False) --length_column_name LENGTH_COLUMN_NAME Column name with precomputed lengths to use when grouping by length. (default: length) --report_to REPORT_TO The list of integrations to report the results and logs to. (default: None) --ddp_find_unused_parameters DDP_FIND_UNUSED_PARAMETERS When using distributed training, the value of the flag `find_unused_parameters` passed to `DistributedDataParallel`. (default: None) --ddp_bucket_cap_mb DDP_BUCKET_CAP_MB When using distributed training, the value of the flag `bucket_cap_mb` passed to `DistributedDataParallel`. (default: None) --ddp_broadcast_buffers DDP_BROADCAST_BUFFERS When using distributed training, the value of the flag `broadcast_buffers` passed to `DistributedDataParallel`. (default: None) --dataloader_pin_memory [DATALOADER_PIN_MEMORY] Whether or not to pin memory for DataLoader. (default: True) --no_dataloader_pin_memory Whether or not to pin memory for DataLoader. (default: False) --dataloader_persistent_workers [DATALOADER_PERSISTENT_WORKERS] If True, the data loader will not shut down the worker processes after a dataset has been consumed once. This allows to maintain the workers Dataset instances alive. Can potentially speed up training, but will increase RAM usage. (default: False) --skip_memory_metrics [SKIP_MEMORY_METRICS] Whether or not to skip adding of memory profiler reports to metrics. (default: True) --no_skip_memory_metrics Whether or not to skip adding of memory profiler reports to metrics. (default: False) --use_legacy_prediction_loop [USE_LEGACY_PREDICTION_LOOP] Whether or not to use the legacy prediction_loop in the Trainer. (default: False) --push_to_hub [PUSH_TO_HUB] Whether or not to upload the trained model to the model hub after training. (default: False) --resume_from_checkpoint RESUME_FROM_CHECKPOINT The path to a folder with a valid checkpoint for your model. (default: None) --hub_model_id HUB_MODEL_ID The name of the repository to keep in sync with the local `output_dir`. (default: None) --hub_strategy {end,every_save,checkpoint,all_checkpoints} The hub strategy to use when `--push_to_hub` is activated. (default: every_save) --hub_token HUB_TOKEN The token to use to push to the Model Hub. (default: None) --hub_private_repo [HUB_PRIVATE_REPO] Whether the model repository is private or not. (default: False) --hub_always_push [HUB_ALWAYS_PUSH] Unless `True`, the Trainer will skip pushes if the previous one wasn't finished yet. (default: False) --gradient_checkpointing [GRADIENT_CHECKPOINTING] If True, use gradient checkpointing to save memory at the expense of slower backward pass. (default: False) --gradient_checkpointing_kwargs GRADIENT_CHECKPOINTING_KWARGS Gradient checkpointing key word arguments such as `use_reentrant`. Will be passed to `torch.utils.checkpoint.checkpoint` through `model.gradient_checkpointing_enable`. (default: None) --include_inputs_for_metrics [INCLUDE_INPUTS_FOR_METRICS] Whether or not the inputs will be passed to the `compute_metrics` function. (default: False) --eval_do_concat_batches [EVAL_DO_CONCAT_BATCHES] Whether to recursively concat inputs/losses/labels/predictions across batches. If `False`, will instead store them as lists, with each batch kept separate. (default: True) --no_eval_do_concat_batches Whether to recursively concat inputs/losses/labels/predictions across batches. If `False`, will instead store them as lists, with each batch kept separate. (default: False) --fp16_backend {auto,apex,cpu_amp} Deprecated. Use half_precision_backend instead (default: auto) --evaluation_strategy {no,steps,epoch} Deprecated. Use `eval_strategy` instead (default: None) --push_to_hub_model_id PUSH_TO_HUB_MODEL_ID The name of the repository to which push the `Trainer`. (default: None) --push_to_hub_organization PUSH_TO_HUB_ORGANIZATION The name of the organization in with to which push the `Trainer`. (default: None) --push_to_hub_token PUSH_TO_HUB_TOKEN The token to use to push to the Model Hub. (default: None) --mp_parameters MP_PARAMETERS Used by the SageMaker launcher to send mp-specific args. Ignored in Trainer (default: ) --auto_find_batch_size [AUTO_FIND_BATCH_SIZE] Whether to automatically decrease the batch size in half and rerun the training loop again each time a CUDA Out-of-Memory was reached (default: False) --full_determinism [FULL_DETERMINISM] Whether to call enable_full_determinism instead of set_seed for reproducibility in distributed training. Important: this will negatively impact the performance, so only use it for debugging. (default: False) --torchdynamo TORCHDYNAMO This argument is deprecated, use `--torch_compile_backend` instead. (default: None) --ray_scope RAY_SCOPE The scope to use when doing hyperparameter search with Ray. By default, `"last"` will be used. Ray will then use the last checkpoint of all trials, compare those, and select the best one. However, other options are also available. See the Ray documentation (https://doc s.ray.io/en/latest/tune/api_docs/analysis.html#ray.tun e.ExperimentAnalysis.get_best_trial) for more options. (default: last) --ddp_timeout DDP_TIMEOUT Overrides the default timeout for distributed training (value should be given in seconds). (default: 1800) --torch_compile [TORCH_COMPILE] If set to `True`, the model will be wrapped in `torch.compile`. (default: False) --torch_compile_backend TORCH_COMPILE_BACKEND Which backend to use with `torch.compile`, passing one will trigger a model compilation. (default: None) --torch_compile_mode TORCH_COMPILE_MODE Which mode to use with `torch.compile`, passing one will trigger a model compilation. (default: None) --dispatch_batches DISPATCH_BATCHES Deprecated. Pass {'dispatch_batches':VALUE} to `accelerator_config`. (default: None) --split_batches SPLIT_BATCHES Deprecated. Pass {'split_batches':True} to `accelerator_config`. (default: None) --include_tokens_per_second [INCLUDE_TOKENS_PER_SECOND] If set to `True`, the speed metrics will include `tgs` (tokens per second per device). (default: False) --include_num_input_tokens_seen [INCLUDE_NUM_INPUT_TOKENS_SEEN] If set to `True`, will track the number of input tokens seen throughout training. (May be slower in distributed training) (default: False) --neftune_noise_alpha NEFTUNE_NOISE_ALPHA Activates neftune noise embeddings into the model. NEFTune has been proven to drastically improve model performances for instrcution fine-tuning. Check out the original paper here: https://arxiv.org/abs/2310.05914 and the original code here: https://github.com/neelsjain/NEFTune. Only supported for `PreTrainedModel` and `PeftModel` classes. (default: None) --optim_target_modules OPTIM_TARGET_MODULES Target modules for the optimizer defined in the `optim` argument. Only used for the GaLore optimizer at the moment. (default: None) --batch_eval_metrics [BATCH_EVAL_METRICS] Break eval metrics calculation into batches to save memory. (default: False) --eval_on_start [EVAL_ON_START] Whether to run through the entire `evaluation` step at the very beginning of training as a sanity check. (default: False) --eval_use_gather_object [EVAL_USE_GATHER_OBJECT] Whether to run recursively gather object in a nested list/tuple/dictionary of objects from all devices. (default: False) --sortish_sampler [SORTISH_SAMPLER] Whether to use SortishSampler or not. (default: False) --predict_with_generate [PREDICT_WITH_GENERATE] Whether to use generate to calculate generative metrics (ROUGE, BLEU). (default: False) --generation_max_length GENERATION_MAX_LENGTH The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default to the `max_length` value of the model configuration. (default: None) --generation_num_beams GENERATION_NUM_BEAMS The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default to the `num_beams` value of the model configuration. (default: None) --generation_config GENERATION_CONFIG Model id, file path or url pointing to a GenerationConfig json file, to use during prediction. (default: None) --use_badam [USE_BADAM] Whether or not to use the BAdam optimizer. (default: False) --badam_mode {layer,ratio} Whether to use layer-wise or ratio-wise BAdam optimizer. (default: layer) --badam_start_block BADAM_START_BLOCK The starting block index for layer-wise BAdam. (default: None) --badam_switch_mode {ascending,descending,random,fixed} the strategy of picking block to update for layer-wise BAdam. (default: ascending) --badam_switch_interval BADAM_SWITCH_INTERVAL Number of steps to update the block for layer-wise BAdam. Use -1 to disable the block update. (default: 50) --badam_update_ratio BADAM_UPDATE_RATIO The ratio of the update for ratio-wise BAdam. (default: 0.05) --badam_mask_mode {adjacent,scatter} The mode of the mask for BAdam optimizer. `adjacent` means that the trainable parameters are adjacent to each other, `scatter` means that trainable parameters are randomly choosed from the weight. (default: adjacent) --badam_verbose BADAM_VERBOSE The verbosity level of BAdam optimizer. 0 for no print, 1 for print the block prefix, 2 for print trainable parameters. (default: 0) --use_galore [USE_GALORE] Whether or not to use the gradient low-Rank projection (GaLore). (default: False) --galore_target GALORE_TARGET Name(s) of modules to apply GaLore. Use commas to separate multiple modules. Use `all` to specify all the linear modules. (default: all) --galore_rank GALORE_RANK The rank of GaLore gradients. (default: 16) --galore_update_interval GALORE_UPDATE_INTERVAL Number of steps to update the GaLore projection. (default: 200) --galore_scale GALORE_SCALE GaLore scaling coefficient. (default: 0.25) --galore_proj_type {std,reverse_std,right,left,full} Type of GaLore projection. (default: std) --galore_layerwise [GALORE_LAYERWISE] Whether or not to enable layer-wise update to further save memory. (default: False) --pref_beta PREF_BETA The beta parameter in the preference loss. (default: 0.1) --pref_ftx PREF_FTX The supervised fine-tuning loss coefficient in DPO training. (default: 0.0) --pref_loss {sigmoid,hinge,ipo,kto_pair,orpo,simpo} The type of DPO loss to use. (default: sigmoid) --dpo_label_smoothing DPO_LABEL_SMOOTHING The robust DPO label smoothing parameter in cDPO that should be between 0 and 0.5. (default: 0.0) --kto_chosen_weight KTO_CHOSEN_WEIGHT The weight factor of the desirable losses in KTO training. (default: 1.0) --kto_rejected_weight KTO_REJECTED_WEIGHT The weight factor of the undesirable losses in KTO training. (default: 1.0) --simpo_gamma SIMPO_GAMMA The target reward margin term in SimPO loss. (default: 0.5) --ppo_buffer_size PPO_BUFFER_SIZE The number of mini-batches to make experience buffer in a PPO optimization step. (default: 1) --ppo_epochs PPO_EPOCHS The number of epochs to perform in a PPO optimization step. (default: 4) --ppo_score_norm [PPO_SCORE_NORM] Use score normalization in PPO training. (default: False) --ppo_target PPO_TARGET Target KL value for adaptive KL control in PPO training. (default: 6.0) --ppo_whiten_rewards [PPO_WHITEN_REWARDS] Whiten the rewards before compute advantages in PPO training. (default: False) --ref_model REF_MODEL Path to the reference model used for the PPO or DPO training. (default: None) --ref_model_adapters REF_MODEL_ADAPTERS Path to the adapters of the reference model. (default: None) --ref_model_quantization_bit REF_MODEL_QUANTIZATION_BIT The number of bits to quantize the reference model. (default: None) --reward_model REWARD_MODEL Path to the reward model used for the PPO training. (default: None) --reward_model_adapters REWARD_MODEL_ADAPTERS Path to the adapters of the reward model. (default: None) --reward_model_quantization_bit REWARD_MODEL_QUANTIZATION_BIT The number of bits to quantize the reward model. (default: None) --reward_model_type {lora,full,api} The type of the reward model in PPO training. Lora model only supports lora training. (default: lora) --additional_target ADDITIONAL_TARGET Name(s) of modules apart from LoRA layers to be set as trainable and saved in the final checkpoint. Use commas to separate multiple modules. (default: None) --lora_alpha LORA_ALPHA The scale factor for LoRA fine-tuning (default: lora_rank * 2). (default: None) --lora_dropout LORA_DROPOUT Dropout rate for the LoRA fine-tuning. (default: 0.0) --lora_rank LORA_RANK The intrinsic dimension for LoRA fine-tuning. (default: 8) --lora_target LORA_TARGET Name(s) of target modules to apply LoRA. Use commas to separate multiple modules. Use `all` to specify all the linear modules. (default: all) --loraplus_lr_ratio LORAPLUS_LR_RATIO LoRA plus learning rate ratio (lr_B / lr_A). (default: None) --loraplus_lr_embedding LORAPLUS_LR_EMBEDDING LoRA plus learning rate for lora embedding layers. (default: 1e-06) --use_rslora [USE_RSLORA] Whether or not to use the rank stabilization scaling factor for LoRA layer. (default: False) --use_dora [USE_DORA] Whether or not to use the weight-decomposed lora method (DoRA). (default: False) --pissa_init [PISSA_INIT] Whether or not to initialize a PiSSA adapter. (default: False) --pissa_iter PISSA_ITER The number of iteration steps performed by FSVD in PiSSA. Use -1 to disable it. (default: 16) --pissa_convert [PISSA_CONVERT] Whether or not to convert the PiSSA adapter to a normal LoRA adapter. (default: False) --create_new_adapter [CREATE_NEW_ADAPTER] Whether or not to create a new adapter with randomly initialized weight. (default: False) --freeze_trainable_layers FREEZE_TRAINABLE_LAYERS The number of trainable layers for freeze (partial- parameter) fine-tuning. Positive numbers mean the last n layers are set as trainable, negative numbers mean the first n layers are set as trainable. (default: 2) --freeze_trainable_modules FREEZE_TRAINABLE_MODULES Name(s) of trainable modules for freeze (partial- parameter) fine-tuning. Use commas to separate multiple modules. Use `all` to specify all the available modules. (default: all) --freeze_extra_modules FREEZE_EXTRA_MODULES Name(s) of modules apart from hidden layers to be set as trainable for freeze (partial-parameter) fine- tuning. Use commas to separate multiple modules. (default: None) --pure_bf16 [PURE_BF16] Whether or not to train model in purely bf16 precision (without AMP). (default: False) --stage {pt,sft,rm,ppo,dpo,kto} Which stage will be performed in training. (default: sft) --finetuning_type {lora,freeze,full} Which fine-tuning method to use. (default: lora) --use_llama_pro [USE_LLAMA_PRO] Whether or not to make only the parameters in the expanded blocks trainable. (default: False) --use_adam_mini [USE_ADAM_MINI] Whether or not to use the Adam-mini optimizer. (default: False) --freeze_vision_tower [FREEZE_VISION_TOWER] Whether ot not to freeze vision tower in MLLM training. (default: True) --no_freeze_vision_tower Whether ot not to freeze vision tower in MLLM training. (default: False) --train_mm_proj_only [TRAIN_MM_PROJ_ONLY] Whether or not to train the multimodal projector for MLLM only. (default: False) --compute_accuracy [COMPUTE_ACCURACY] Whether or not to compute the token-level accuracy at evaluation. (default: False) --plot_loss [PLOT_LOSS] Whether or not to save the training loss curves. (default: False) --do_sample [DO_SAMPLE] Whether or not to use sampling, use greedy decoding otherwise. (default: True) --no_do_sample Whether or not to use sampling, use greedy decoding otherwise. (default: False) --temperature TEMPERATURE The value used to modulate the next token probabilities. (default: 0.95) --top_p TOP_P The smallest set of most probable tokens with probabilities that add up to top_p or higher are kept. (default: 0.7) --top_k TOP_K The number of highest probability vocabulary tokens to keep for top-k filtering. (default: 50) --num_beams NUM_BEAMS Number of beams for beam search. 1 means no beam search. (default: 1) --max_length MAX_LENGTH The maximum length the generated tokens can have. It can be overridden by max_new_tokens. (default: 1024) --max_new_tokens MAX_NEW_TOKENS The maximum numbers of tokens to generate, ignoring the number of tokens in the prompt. (default: 1024) --repetition_penalty REPETITION_PENALTY The parameter for repetition penalty. 1.0 means no penalty. (default: 1.0) --length_penalty LENGTH_PENALTY Exponential penalty to the length that is used with beam-based generation. (default: 1.0) --default_system DEFAULT_SYSTEM Default system message to use in chat completion. (default: None) usage: launcher.py [-h] --model_name_or_path MODEL_NAME_OR_PATH [--adapter_name_or_path ADAPTER_NAME_OR_PATH] [--adapter_folder ADAPTER_FOLDER] [--cache_dir CACHE_DIR] [--use_fast_tokenizer [USE_FAST_TOKENIZER]] [--no_use_fast_tokenizer] [--resize_vocab [RESIZE_VOCAB]] [--split_special_tokens [SPLIT_SPECIAL_TOKENS]] [--new_special_tokens NEW_SPECIAL_TOKENS] [--model_revision MODEL_REVISION] [--low_cpu_mem_usage [LOW_CPU_MEM_USAGE]] [--no_low_cpu_mem_usage] [--quantization_method {bitsandbytes,hqq,eetq}] [--quantization_bit QUANTIZATION_BIT] [--quantization_type {fp4,nf4}] [--double_quantization [DOUBLE_QUANTIZATION]] [--no_double_quantization] [--quantization_device_map {auto}] [--rope_scaling {linear,dynamic}] [--flash_attn {auto,disabled,sdpa,fa2}] [--shift_attn [SHIFT_ATTN]] [--mixture_of_depths {convert,load}] [--use_unsloth [USE_UNSLOTH]] [--visual_inputs [VISUAL_INPUTS]] [--moe_aux_loss_coef MOE_AUX_LOSS_COEF] [--disable_gradient_checkpointing [DISABLE_GRADIENT_CHECKPOINTING]] [--upcast_layernorm [UPCAST_LAYERNORM]] [--upcast_lmhead_output [UPCAST_LMHEAD_OUTPUT]] [--train_from_scratch [TRAIN_FROM_SCRATCH]] [--infer_backend {huggingface,vllm}] [--vllm_maxlen VLLM_MAXLEN] [--vllm_gpu_util VLLM_GPU_UTIL] [--vllm_enforce_eager [VLLM_ENFORCE_EAGER]] [--vllm_max_lora_rank VLLM_MAX_LORA_RANK] [--offload_folder OFFLOAD_FOLDER] [--use_cache [USE_CACHE]] [--no_use_cache] [--infer_dtype {auto,float16,bfloat16,float32}] [--hf_hub_token HF_HUB_TOKEN] [--ms_hub_token MS_HUB_TOKEN] [--export_dir EXPORT_DIR] [--export_size EXPORT_SIZE] [--export_device {cpu,auto}] [--export_quantization_bit EXPORT_QUANTIZATION_BIT] [--export_quantization_dataset EXPORT_QUANTIZATION_DATASET] [--export_quantization_nsamples EXPORT_QUANTIZATION_NSAMPLES] [--export_quantization_maxlen EXPORT_QUANTIZATION_MAXLEN] [--export_legacy_format [EXPORT_LEGACY_FORMAT]] [--export_hub_model_id EXPORT_HUB_MODEL_ID] [--print_param_status [PRINT_PARAM_STATUS]] [--template TEMPLATE] [--dataset DATASET] [--eval_dataset EVAL_DATASET] [--dataset_dir DATASET_DIR] [--cutoff_len CUTOFF_LEN] [--train_on_prompt [TRAIN_ON_PROMPT]] [--mask_history [MASK_HISTORY]] [--streaming [STREAMING]] [--buffer_size BUFFER_SIZE] [--mix_strategy {concat,interleave_under,interleave_over}] [--interleave_probs INTERLEAVE_PROBS] [--overwrite_cache [OVERWRITE_CACHE]] [--preprocessing_num_workers PREPROCESSING_NUM_WORKERS] [--max_samples MAX_SAMPLES] [--eval_num_beams EVAL_NUM_BEAMS] [--ignore_pad_token_for_loss [IGNORE_PAD_TOKEN_FOR_LOSS]] [--no_ignore_pad_token_for_loss] [--val_size VAL_SIZE] [--packing PACKING] [--neat_packing [NEAT_PACKING]] [--tool_format TOOL_FORMAT] [--tokenized_path TOKENIZED_PATH] --output_dir OUTPUT_DIR [--overwrite_output_dir [OVERWRITE_OUTPUT_DIR]] [--do_train [DO_TRAIN]] [--do_eval [DO_EVAL]] [--do_predict [DO_PREDICT]] [--eval_strategy {no,steps,epoch}] [--prediction_loss_only [PREDICTION_LOSS_ONLY]] [--per_device_train_batch_size PER_DEVICE_TRAIN_BATCH_SIZE] [--per_device_eval_batch_size PER_DEVICE_EVAL_BATCH_SIZE] [--per_gpu_train_batch_size PER_GPU_TRAIN_BATCH_SIZE] [--per_gpu_eval_batch_size PER_GPU_EVAL_BATCH_SIZE] [--gradient_accumulation_steps GRADIENT_ACCUMULATION_STEPS] [--eval_accumulation_steps EVAL_ACCUMULATION_STEPS] [--eval_delay EVAL_DELAY] [--torch_empty_cache_steps TORCH_EMPTY_CACHE_STEPS] [--learning_rate LEARNING_RATE] [--weight_decay WEIGHT_DECAY] [--adam_beta1 ADAM_BETA1] [--adam_beta2 ADAM_BETA2] [--adam_epsilon ADAM_EPSILON] [--max_grad_norm MAX_GRAD_NORM] [--num_train_epochs NUM_TRAIN_EPOCHS] [--max_steps MAX_STEPS] [--lr_scheduler_type {linear,cosine,cosine_with_restarts,polynomial,constant,constant_with_warmup,inverse_sqrt,reduce_lr_on_plateau,cosine_with_min_lr,warmup_stable_decay}] [--lr_scheduler_kwargs LR_SCHEDULER_KWARGS] [--warmup_ratio WARMUP_RATIO] [--warmup_steps WARMUP_STEPS] [--log_level {detail,debug,info,warning,error,critical,passive}] [--log_level_replica {detail,debug,info,warning,error,critical,passive}] [--log_on_each_node [LOG_ON_EACH_NODE]] [--no_log_on_each_node] [--logging_dir LOGGING_DIR] [--logging_strategy {no,steps,epoch}] [--logging_first_step [LOGGING_FIRST_STEP]] [--logging_steps LOGGING_STEPS] [--logging_nan_inf_filter [LOGGING_NAN_INF_FILTER]] [--no_logging_nan_inf_filter] [--save_strategy {no,steps,epoch}] [--save_steps SAVE_STEPS] [--save_total_limit SAVE_TOTAL_LIMIT] [--save_safetensors [SAVE_SAFETENSORS]] [--no_save_safetensors] [--save_on_each_node [SAVE_ON_EACH_NODE]] [--save_only_model [SAVE_ONLY_MODEL]] [--restore_callback_states_from_checkpoint [RESTORE_CALLBACK_STATES_FROM_CHECKPOINT]] [--no_cuda [NO_CUDA]] [--use_cpu [USE_CPU]] [--use_mps_device [USE_MPS_DEVICE]] [--seed SEED] [--data_seed DATA_SEED] [--jit_mode_eval [JIT_MODE_EVAL]] [--use_ipex [USE_IPEX]] [--bf16 [BF16]] [--fp16 [FP16]] [--fp16_opt_level FP16_OPT_LEVEL] [--half_precision_backend {auto,apex,cpu_amp}] [--bf16_full_eval [BF16_FULL_EVAL]] [--fp16_full_eval [FP16_FULL_EVAL]] [--tf32 TF32] [--local_rank LOCAL_RANK] [--ddp_backend {nccl,gloo,mpi,ccl,hccl,cncl}] [--tpu_num_cores TPU_NUM_CORES] [--tpu_metrics_debug [TPU_METRICS_DEBUG]] [--debug DEBUG [DEBUG ...]] [--dataloader_drop_last [DATALOADER_DROP_LAST]] [--eval_steps EVAL_STEPS] [--dataloader_num_workers DATALOADER_NUM_WORKERS] [--dataloader_prefetch_factor DATALOADER_PREFETCH_FACTOR] [--past_index PAST_INDEX] [--run_name RUN_NAME] [--disable_tqdm DISABLE_TQDM] [--remove_unused_columns [REMOVE_UNUSED_COLUMNS]] [--no_remove_unused_columns] [--label_names LABEL_NAMES [LABEL_NAMES ...]] [--load_best_model_at_end [LOAD_BEST_MODEL_AT_END]] [--metric_for_best_model METRIC_FOR_BEST_MODEL] [--greater_is_better GREATER_IS_BETTER] [--ignore_data_skip [IGNORE_DATA_SKIP]] [--fsdp FSDP] [--fsdp_min_num_params FSDP_MIN_NUM_PARAMS] [--fsdp_config FSDP_CONFIG] [--fsdp_transformer_layer_cls_to_wrap FSDP_TRANSFORMER_LAYER_CLS_TO_WRAP] [--accelerator_config ACCELERATOR_CONFIG] [--deepspeed DEEPSPEED] [--label_smoothing_factor LABEL_SMOOTHING_FACTOR] [--optim {adamw_hf,adamw_torch,adamw_torch_fused,adamw_torch_xla,adamw_torch_npu_fused,adamw_apex_fused,adafactor,adamw_anyprecision,sgd,adagrad,adamw_bnb_8bit,adamw_8bit,lion_8bit,lion_32bit,paged_adamw_32bit,paged_adamw_8bit,paged_lion_32bit,paged_lion_8bit,rmsprop,rmsprop_bnb,rmsprop_bnb_8bit,rmsprop_bnb_32bit,galore_adamw,galore_adamw_8bit,galore_adafactor,galore_adamw_layerwise,galore_adamw_8bit_layerwise,galore_adafactor_layerwise,lomo,adalomo}] [--optim_args OPTIM_ARGS] [--adafactor [ADAFACTOR]] [--group_by_length [GROUP_BY_LENGTH]] [--length_column_name LENGTH_COLUMN_NAME] [--report_to REPORT_TO] [--ddp_find_unused_parameters DDP_FIND_UNUSED_PARAMETERS] [--ddp_bucket_cap_mb DDP_BUCKET_CAP_MB] [--ddp_broadcast_buffers DDP_BROADCAST_BUFFERS] [--dataloader_pin_memory [DATALOADER_PIN_MEMORY]] [--no_dataloader_pin_memory] [--dataloader_persistent_workers [DATALOADER_PERSISTENT_WORKERS]] [--skip_memory_metrics [SKIP_MEMORY_METRICS]] [--no_skip_memory_metrics] [--use_legacy_prediction_loop [USE_LEGACY_PREDICTION_LOOP]] [--push_to_hub [PUSH_TO_HUB]] [--resume_from_checkpoint RESUME_FROM_CHECKPOINT] [--hub_model_id HUB_MODEL_ID] [--hub_strategy {end,every_save,checkpoint,all_checkpoints}] [--hub_token HUB_TOKEN] [--hub_private_repo [HUB_PRIVATE_REPO]] [--hub_always_push [HUB_ALWAYS_PUSH]] [--gradient_checkpointing [GRADIENT_CHECKPOINTING]] [--gradient_checkpointing_kwargs GRADIENT_CHECKPOINTING_KWARGS] [--include_inputs_for_metrics [INCLUDE_INPUTS_FOR_METRICS]] [--eval_do_concat_batches [EVAL_DO_CONCAT_BATCHES]] [--no_eval_do_concat_batches] [--fp16_backend {auto,apex,cpu_amp}] [--evaluation_strategy {no,steps,epoch}] [--push_to_hub_model_id PUSH_TO_HUB_MODEL_ID] [--push_to_hub_organization PUSH_TO_HUB_ORGANIZATION] [--push_to_hub_token PUSH_TO_HUB_TOKEN] [--mp_parameters MP_PARAMETERS] [--auto_find_batch_size [AUTO_FIND_BATCH_SIZE]] [--full_determinism [FULL_DETERMINISM]] [--torchdynamo TORCHDYNAMO] [--ray_scope RAY_SCOPE] [--ddp_timeout DDP_TIMEOUT] [--torch_compile [TORCH_COMPILE]] [--torch_compile_backend TORCH_COMPILE_BACKEND] [--torch_compile_mode TORCH_COMPILE_MODE] [--dispatch_batches DISPATCH_BATCHES] [--split_batches SPLIT_BATCHES] [--include_tokens_per_second [INCLUDE_TOKENS_PER_SECOND]] [--include_num_input_tokens_seen [INCLUDE_NUM_INPUT_TOKENS_SEEN]] [--neftune_noise_alpha NEFTUNE_NOISE_ALPHA] [--optim_target_modules OPTIM_TARGET_MODULES] [--batch_eval_metrics [BATCH_EVAL_METRICS]] [--eval_on_start [EVAL_ON_START]] [--eval_use_gather_object [EVAL_USE_GATHER_OBJECT]] [--sortish_sampler [SORTISH_SAMPLER]] [--predict_with_generate [PREDICT_WITH_GENERATE]] [--generation_max_length GENERATION_MAX_LENGTH] [--generation_num_beams GENERATION_NUM_BEAMS] [--generation_config GENERATION_CONFIG] [--use_badam [USE_BADAM]] [--badam_mode {layer,ratio}] [--badam_start_block BADAM_START_BLOCK] [--badam_switch_mode {ascending,descending,random,fixed}] [--badam_switch_interval BADAM_SWITCH_INTERVAL] [--badam_update_ratio BADAM_UPDATE_RATIO] [--badam_mask_mode {adjacent,scatter}] [--badam_verbose BADAM_VERBOSE] [--use_galore [USE_GALORE]] [--galore_target GALORE_TARGET] [--galore_rank GALORE_RANK] [--galore_update_interval GALORE_UPDATE_INTERVAL] [--galore_scale GALORE_SCALE] [--galore_proj_type {std,reverse_std,right,left,full}] [--galore_layerwise [GALORE_LAYERWISE]] [--pref_beta PREF_BETA] [--pref_ftx PREF_FTX] [--pref_loss {sigmoid,hinge,ipo,kto_pair,orpo,simpo}] [--dpo_label_smoothing DPO_LABEL_SMOOTHING] [--kto_chosen_weight KTO_CHOSEN_WEIGHT] [--kto_rejected_weight KTO_REJECTED_WEIGHT] [--simpo_gamma SIMPO_GAMMA] [--ppo_buffer_size PPO_BUFFER_SIZE] [--ppo_epochs PPO_EPOCHS] [--ppo_score_norm [PPO_SCORE_NORM]] [--ppo_target PPO_TARGET] [--ppo_whiten_rewards [PPO_WHITEN_REWARDS]] [--ref_model REF_MODEL] [--ref_model_adapters REF_MODEL_ADAPTERS] [--ref_model_quantization_bit REF_MODEL_QUANTIZATION_BIT] [--reward_model REWARD_MODEL] [--reward_model_adapters REWARD_MODEL_ADAPTERS] [--reward_model_quantization_bit REWARD_MODEL_QUANTIZATION_BIT] [--reward_model_type {lora,full,api}] [--additional_target ADDITIONAL_TARGET] [--lora_alpha LORA_ALPHA] [--lora_dropout LORA_DROPOUT] [--lora_rank LORA_RANK] [--lora_target LORA_TARGET] [--loraplus_lr_ratio LORAPLUS_LR_RATIO] [--loraplus_lr_embedding LORAPLUS_LR_EMBEDDING] [--use_rslora [USE_RSLORA]] [--use_dora [USE_DORA]] [--pissa_init [PISSA_INIT]] [--pissa_iter PISSA_ITER] [--pissa_convert [PISSA_CONVERT]] [--create_new_adapter [CREATE_NEW_ADAPTER]] [--freeze_trainable_layers FREEZE_TRAINABLE_LAYERS] [--freeze_trainable_modules FREEZE_TRAINABLE_MODULES] [--freeze_extra_modules FREEZE_EXTRA_MODULES] [--pure_bf16 [PURE_BF16]] [--stage {pt,sft,rm,ppo,dpo,kto}] [--finetuning_type {lora,freeze,full}] [--use_llama_pro [USE_LLAMA_PRO]] [--use_adam_mini [USE_ADAM_MINI]] [--freeze_vision_tower [FREEZE_VISION_TOWER]] [--no_freeze_vision_tower] [--train_mm_proj_only [TRAIN_MM_PROJ_ONLY]] [--compute_accuracy [COMPUTE_ACCURACY]] [--plot_loss [PLOT_LOSS]] [--do_sample [DO_SAMPLE]] [--no_do_sample] [--temperature TEMPERATURE] [--top_p TOP_P] [--top_k TOP_K] [--num_beams NUM_BEAMS] [--max_length MAX_LENGTH] [--max_new_tokens MAX_NEW_TOKENS] [--repetition_penalty REPETITION_PENALTY] [--length_penalty LENGTH_PENALTY] [--default_system DEFAULT_SYSTEM] optional arguments: -h, --help show this help message and exit --model_name_or_path MODEL_NAME_OR_PATH Path to the model weight or identifier from huggingface.co/models or modelscope.cn/models. (default: None) --adapter_name_or_path ADAPTER_NAME_OR_PATH Path to the adapter weight or identifier from huggingface.co/models. Use commas to separate multiple adapters. (default: None) --adapter_folder ADAPTER_FOLDER The folder containing the adapter weights to load. (default: None) --cache_dir CACHE_DIR Where to store the pre-trained models downloaded from huggingface.co or modelscope.cn. (default: None) --use_fast_tokenizer [USE_FAST_TOKENIZER] Whether or not to use one of the fast tokenizer (backed by the tokenizers library). (default: True) --no_use_fast_tokenizer Whether or not to use one of the fast tokenizer (backed by the tokenizers library). (default: False) --resize_vocab [RESIZE_VOCAB] Whether or not to resize the tokenizer vocab and the embedding layers. (default: False) --split_special_tokens [SPLIT_SPECIAL_TOKENS] Whether or not the special tokens should be split during the tokenization process. (default: False) --new_special_tokens NEW_SPECIAL_TOKENS Special tokens to be added into the tokenizer. Use commas to separate multiple tokens. (default: None) --model_revision MODEL_REVISION The specific model version to use (can be a branch name, tag name or commit id). (default: main) --low_cpu_mem_usage [LOW_CPU_MEM_USAGE] Whether or not to use memory-efficient model loading. (default: True) --no_low_cpu_mem_usage Whether or not to use memory-efficient model loading. (default: False) --quantization_method {bitsandbytes,hqq,eetq} Quantization method to use for on-the-fly quantization. (default: bitsandbytes) --quantization_bit QUANTIZATION_BIT The number of bits to quantize the model using bitsandbytes. (default: None) --quantization_type {fp4,nf4} Quantization data type to use in int4 training. (default: nf4) --double_quantization [DOUBLE_QUANTIZATION] Whether or not to use double quantization in int4 training. (default: True) --no_double_quantization Whether or not to use double quantization in int4 training. (default: False) --quantization_device_map {auto} Device map used to infer the 4-bit quantized model, needs bitsandbytes>=0.43.0. (default: None) --rope_scaling {linear,dynamic} Which scaling strategy should be adopted for the RoPE embeddings. (default: None) --flash_attn {auto,disabled,sdpa,fa2} Enable FlashAttention for faster training and inference. (default: auto) --shift_attn [SHIFT_ATTN] Enable shift short attention (S^2-Attn) proposed by LongLoRA. (default: False) --mixture_of_depths {convert,load} Convert the model to mixture-of-depths (MoD) or load the MoD model. (default: None) --use_unsloth [USE_UNSLOTH] Whether or not to use unsloth's optimization for the LoRA training. (default: False) --visual_inputs [VISUAL_INPUTS] Whethor or not to use multimodal LLM that accepts visual inputs. (default: False) --moe_aux_loss_coef MOE_AUX_LOSS_COEF Coefficient of the auxiliary router loss in mixture- of-experts model. (default: None) --disable_gradient_checkpointing [DISABLE_GRADIENT_CHECKPOINTING] Whether or not to disable gradient checkpointing. (default: False) --upcast_layernorm [UPCAST_LAYERNORM] Whether or not to upcast the layernorm weights in fp32. (default: False) --upcast_lmhead_output [UPCAST_LMHEAD_OUTPUT] Whether or not to upcast the output of lm_head in fp32. (default: False) --train_from_scratch [TRAIN_FROM_SCRATCH] Whether or not to randomly initialize the model weights. (default: False) --infer_backend {huggingface,vllm} Backend engine used at inference. (default: huggingface) --vllm_maxlen VLLM_MAXLEN Maximum sequence (prompt + response) length of the vLLM engine. (default: 2048) --vllm_gpu_util VLLM_GPU_UTIL The fraction of GPU memory in (0,1) to be used for the vLLM engine. (default: 0.9) --vllm_enforce_eager [VLLM_ENFORCE_EAGER] Whether or not to disable CUDA graph in the vLLM engine. (default: False) --vllm_max_lora_rank VLLM_MAX_LORA_RANK Maximum rank of all LoRAs in the vLLM engine. (default: 32) --offload_folder OFFLOAD_FOLDER Path to offload model weights. (default: offload) --use_cache [USE_CACHE] Whether or not to use KV cache in generation. (default: True) --no_use_cache Whether or not to use KV cache in generation. (default: False) --infer_dtype {auto,float16,bfloat16,float32} Data type for model weights and activations at inference. (default: auto) --hf_hub_token HF_HUB_TOKEN Auth token to log in with Hugging Face Hub. (default: None) --ms_hub_token MS_HUB_TOKEN Auth token to log in with ModelScope Hub. (default: None) --export_dir EXPORT_DIR Path to the directory to save the exported model. (default: None) --export_size EXPORT_SIZE The file shard size (in GB) of the exported model. (default: 1) --export_device {cpu,auto} The device used in model export, use `auto` to accelerate exporting. (default: cpu) --export_quantization_bit EXPORT_QUANTIZATION_BIT The number of bits to quantize the exported model. (default: None) --export_quantization_dataset EXPORT_QUANTIZATION_DATASET Path to the dataset or dataset name to use in quantizing the exported model. (default: None) --export_quantization_nsamples EXPORT_QUANTIZATION_NSAMPLES The number of samples used for quantization. (default: 128) --export_quantization_maxlen EXPORT_QUANTIZATION_MAXLEN The maximum length of the model inputs used for quantization. (default: 1024) --export_legacy_format [EXPORT_LEGACY_FORMAT] Whether or not to save the `.bin` files instead of `.safetensors`. (default: False) --export_hub_model_id EXPORT_HUB_MODEL_ID The name of the repository if push the model to the Hugging Face hub. (default: None) --print_param_status [PRINT_PARAM_STATUS] For debugging purposes, print the status of the parameters in the model. (default: False) --template TEMPLATE Which template to use for constructing prompts in training and inference. (default: None) --dataset DATASET The name of dataset(s) to use for training. Use commas to separate multiple datasets. (default: None) --eval_dataset EVAL_DATASET The name of dataset(s) to use for evaluation. Use commas to separate multiple datasets. (default: None) --dataset_dir DATASET_DIR Path to the folder containing the datasets. (default: data) --cutoff_len CUTOFF_LEN The cutoff length of the tokenized inputs in the dataset. (default: 1024) --train_on_prompt [TRAIN_ON_PROMPT] Whether or not to disable the mask on the prompt. (default: False) --mask_history [MASK_HISTORY] Whether or not to mask the history and train on the last turn only. (default: False) --streaming [STREAMING] Enable dataset streaming. (default: False) --buffer_size BUFFER_SIZE Size of the buffer to randomly sample examples from in dataset streaming. (default: 16384) --mix_strategy {concat,interleave_under,interleave_over} Strategy to use in dataset mixing (concat/interleave) (undersampling/oversampling). (default: concat) --interleave_probs INTERLEAVE_PROBS Probabilities to sample data from datasets. Use commas to separate multiple datasets. (default: None) --overwrite_cache [OVERWRITE_CACHE] Overwrite the cached training and evaluation sets. (default: False) --preprocessing_num_workers PREPROCESSING_NUM_WORKERS The number of processes to use for the pre-processing. (default: None) --max_samples MAX_SAMPLES For debugging purposes, truncate the number of examples for each dataset. (default: None) --eval_num_beams EVAL_NUM_BEAMS Number of beams to use for evaluation. This argument will be passed to `model.generate` (default: None) --ignore_pad_token_for_loss [IGNORE_PAD_TOKEN_FOR_LOSS] Whether or not to ignore the tokens corresponding to the pad label in loss computation. (default: True) --no_ignore_pad_token_for_loss Whether or not to ignore the tokens corresponding to the pad label in loss computation. (default: False) --val_size VAL_SIZE Size of the development set, should be an integer or a float in range `[0,1)`. (default: 0.0) --packing PACKING Enable sequences packing in training. Will automatically enable in pre-training. (default: None) --neat_packing [NEAT_PACKING] Enable sequence packing without cross-attention. (default: False) --tool_format TOOL_FORMAT Tool format to use for constructing function calling examples. (default: None) --tokenized_path TOKENIZED_PATH Path to save or load the tokenized datasets. (default: None) --output_dir OUTPUT_DIR The output directory where the model predictions and checkpoints will be written. (default: None) --overwrite_output_dir [OVERWRITE_OUTPUT_DIR] Overwrite the content of the output directory. Use this to continue training if output_dir points to a checkpoint directory. (default: False) --do_train [DO_TRAIN] Whether to run training. (default: False) --do_eval [DO_EVAL] Whether to run eval on the dev set. (default: False) --do_predict [DO_PREDICT] Whether to run predictions on the test set. (default: False) --eval_strategy {no,steps,epoch} The evaluation strategy to use. (default: no) --prediction_loss_only [PREDICTION_LOSS_ONLY] When performing evaluation and predictions, only returns the loss. (default: False) --per_device_train_batch_size PER_DEVICE_TRAIN_BATCH_SIZE Batch size per GPU/TPU/MPS/NPU core/CPU for training. (default: 8) --per_device_eval_batch_size PER_DEVICE_EVAL_BATCH_SIZE Batch size per GPU/TPU/MPS/NPU core/CPU for evaluation. (default: 8) --per_gpu_train_batch_size PER_GPU_TRAIN_BATCH_SIZE Deprecated, the use of `--per_device_train_batch_size` is preferred. Batch size per GPU/TPU core/CPU for training. (default: None) --per_gpu_eval_batch_size PER_GPU_EVAL_BATCH_SIZE Deprecated, the use of `--per_device_eval_batch_size` is preferred. Batch size per GPU/TPU core/CPU for evaluation. (default: None) --gradient_accumulation_steps GRADIENT_ACCUMULATION_STEPS Number of updates steps to accumulate before performing a backward/update pass. (default: 1) --eval_accumulation_steps EVAL_ACCUMULATION_STEPS Number of predictions steps to accumulate before moving the tensors to the CPU. (default: None) --eval_delay EVAL_DELAY Number of epochs or steps to wait for before the first evaluation can be performed, depending on the eval_strategy. (default: 0) --torch_empty_cache_steps TORCH_EMPTY_CACHE_STEPS Number of steps to wait before calling `torch..empty_cache()`.This can help avoid CUDA out-of-memory errors by lowering peak VRAM usage at a cost of about [10{'option_strings': ['-- torch_empty_cache_steps'], 'dest': 'torch_empty_cache_steps', 'nargs': None, 'const': None, 'default': None, 'type': 'int', 'choices': None, 'required': False, 'help': 'Number of steps to wait before calling `torch..empty_cache()`.This can help avoid CUDA out-of-memory errors by lowering peak VRAM usage at a cost of about [10% slower performance] (https://github.com/huggingface/transformers/issues/31 372).If left unset or set to None, cache will not be emptied.', 'metavar': None, 'container': , 'prog': 'launcher.py'}lower performance](https://githu b.com/huggingface/transformers/issues/31372).If left unset or set to None, cache will not be emptied. (default: None) --learning_rate LEARNING_RATE The initial learning rate for AdamW. (default: 5e-05) --weight_decay WEIGHT_DECAY Weight decay for AdamW if we apply some. (default: 0.0) --adam_beta1 ADAM_BETA1 Beta1 for AdamW optimizer (default: 0.9) --adam_beta2 ADAM_BETA2 Beta2 for AdamW optimizer (default: 0.999) --adam_epsilon ADAM_EPSILON Epsilon for AdamW optimizer. (default: 1e-08) --max_grad_norm MAX_GRAD_NORM Max gradient norm. (default: 1.0) --num_train_epochs NUM_TRAIN_EPOCHS Total number of training epochs to perform. (default: 3.0) --max_steps MAX_STEPS If > 0: set total number of training steps to perform. Override num_train_epochs. (default: -1) --lr_scheduler_type {linear,cosine,cosine_with_restarts,polynomial,constant,constant_with_warmup,inverse_sqrt,reduce_lr_on_plateau,cosine_with_min_lr,warmup_stable_decay} The scheduler type to use. (default: linear) --lr_scheduler_kwargs LR_SCHEDULER_KWARGS Extra parameters for the lr_scheduler such as {'num_cycles': 1} for the cosine with hard restarts. (default: {}) --warmup_ratio WARMUP_RATIO Linear warmup over warmup_ratio fraction of total steps. (default: 0.0) --warmup_steps WARMUP_STEPS Linear warmup over warmup_steps. (default: 0) --log_level {detail,debug,info,warning,error,critical,passive} Logger log level to use on the main node. Possible choices are the log levels as strings: 'debug', 'info', 'warning', 'error' and 'critical', plus a 'passive' level which doesn't set anything and lets the application set the level. Defaults to 'passive'. (default: passive) --log_level_replica {detail,debug,info,warning,error,critical,passive} Logger log level to use on replica nodes. Same choices and defaults as ``log_level`` (default: warning) --log_on_each_node [LOG_ON_EACH_NODE] When doing a multinode distributed training, whether to log once per node or just once on the main node. (default: True) --no_log_on_each_node When doing a multinode distributed training, whether to log once per node or just once on the main node. (default: False) --logging_dir LOGGING_DIR Tensorboard log dir. (default: None) --logging_strategy {no,steps,epoch} The logging strategy to use. (default: steps) --logging_first_step [LOGGING_FIRST_STEP] Log the first global_step (default: False) --logging_steps LOGGING_STEPS Log every X updates steps. Should be an integer or a float in range `[0,1)`. If smaller than 1, will be interpreted as ratio of total training steps. (default: 500) --logging_nan_inf_filter [LOGGING_NAN_INF_FILTER] Filter nan and inf losses for logging. (default: True) --no_logging_nan_inf_filter Filter nan and inf losses for logging. (default: False) --save_strategy {no,steps,epoch} The checkpoint save strategy to use. (default: steps) --save_steps SAVE_STEPS Save checkpoint every X updates steps. Should be an integer or a float in range `[0,1)`. If smaller than 1, will be interpreted as ratio of total training steps. (default: 500) --save_total_limit SAVE_TOTAL_LIMIT If a value is passed, will limit the total amount of checkpoints. Deletes the older checkpoints in `output_dir`. When `load_best_model_at_end` is enabled, the 'best' checkpoint according to `metric_for_best_model` will always be retained in addition to the most recent ones. For example, for `save_total_limit=5` and `load_best_model_at_end=True`, the four last checkpoints will always be retained alongside the best model. When `save_total_limit=1` and `load_best_model_at_end=True`, it is possible that two checkpoints are saved: the last one and the best one (if they are different). Default is unlimited checkpoints (default: None) --save_safetensors [SAVE_SAFETENSORS] Use safetensors saving and loading for state dicts instead of default torch.load and torch.save. (default: True) --no_save_safetensors Use safetensors saving and loading for state dicts instead of default torch.load and torch.save. (default: False) --save_on_each_node [SAVE_ON_EACH_NODE] When doing multi-node distributed training, whether to save models and checkpoints on each node, or only on the main one (default: False) --save_only_model [SAVE_ONLY_MODEL] When checkpointing, whether to only save the model, or also the optimizer, scheduler & rng state.Note that when this is true, you won't be able to resume training from checkpoint.This enables you to save storage by not storing the optimizer, scheduler & rng state.You can only load the model using from_pretrained with this option set to True. (default: False) --restore_callback_states_from_checkpoint [RESTORE_CALLBACK_STATES_FROM_CHECKPOINT] Whether to restore the callback states from the checkpoint. If `True`, will override callbacks passed to the `Trainer` if they exist in the checkpoint. (default: False) --no_cuda [NO_CUDA] This argument is deprecated. It will be removed in version 5.0 of 🤗 Transformers. (default: False) --use_cpu [USE_CPU] Whether or not to use cpu. If set to False, we will use cuda/tpu/mps/npu device if available. (default: False) --use_mps_device [USE_MPS_DEVICE] This argument is deprecated. `mps` device will be used if available similar to `cuda` device. It will be removed in version 5.0 of 🤗 Transformers (default: False) --seed SEED Random seed that will be set at the beginning of training. (default: 42) --data_seed DATA_SEED Random seed to be used with data samplers. (default: None) --jit_mode_eval [JIT_MODE_EVAL] Whether or not to use PyTorch jit trace for inference (default: False) --use_ipex [USE_IPEX] Use Intel extension for PyTorch when it is available, installation: 'https://github.com/intel/intel- extension-for-pytorch' (default: False) --bf16 [BF16] Whether to use bf16 (mixed) precision instead of 32-bit. Requires Ampere or higher NVIDIA architecture or using CPU (use_cpu) or Ascend NPU. This is an experimental API and it may change. (default: False) --fp16 [FP16] Whether to use fp16 (mixed) precision instead of 32-bit (default: False) --fp16_opt_level FP16_OPT_LEVEL For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']. See details at https://nvidia.github.io/apex/amp.html (default: O1) --half_precision_backend {auto,apex,cpu_amp} The backend to be used for half precision. (default: auto) --bf16_full_eval [BF16_FULL_EVAL] Whether to use full bfloat16 evaluation instead of 32-bit. This is an experimental API and it may change. (default: False) --fp16_full_eval [FP16_FULL_EVAL] Whether to use full float16 evaluation instead of 32-bit (default: False) --tf32 TF32 Whether to enable tf32 mode, available in Ampere and newer GPU architectures. This is an experimental API and it may change. (default: None) --local_rank LOCAL_RANK For distributed training: local_rank (default: -1) --ddp_backend {nccl,gloo,mpi,ccl,hccl,cncl} The backend to be used for distributed training (default: None) --tpu_num_cores TPU_NUM_CORES TPU: Number of TPU cores (automatically passed by launcher script) (default: None) --tpu_metrics_debug [TPU_METRICS_DEBUG] Deprecated, the use of `--debug tpu_metrics_debug` is preferred. TPU: Whether to print debug metrics (default: False) --debug DEBUG [DEBUG ...] Whether or not to enable debug mode. Current options: `underflow_overflow` (Detect underflow and overflow in activations and weights), `tpu_metrics_debug` (print debug metrics on TPU). (default: None) --dataloader_drop_last [DATALOADER_DROP_LAST] Drop the last incomplete batch if it is not divisible by the batch size. (default: False) --eval_steps EVAL_STEPS Run an evaluation every X steps. Should be an integer or a float in range `[0,1)`. If smaller than 1, will be interpreted as ratio of total training steps. (default: None) --dataloader_num_workers DATALOADER_NUM_WORKERS Number of subprocesses to use for data loading (PyTorch only). 0 means that the data will be loaded in the main process. (default: 0) --dataloader_prefetch_factor DATALOADER_PREFETCH_FACTOR Number of batches loaded in advance by each worker. 2 means there will be a total of 2 * num_workers batches prefetched across all workers. Default is 2 for PyTorch < 2.0.0 and otherwise None. (default: None) --past_index PAST_INDEX If >=0, uses the corresponding part of the output as the past state for next step. (default: -1) --run_name RUN_NAME An optional descriptor for the run. Notably used for wandb, mlflow and comet logging. (default: None) --disable_tqdm DISABLE_TQDM Whether or not to disable the tqdm progress bars. (default: None) --remove_unused_columns [REMOVE_UNUSED_COLUMNS] Remove columns not required by the model when using an nlp.Dataset. (default: True) --no_remove_unused_columns Remove columns not required by the model when using an nlp.Dataset. (default: False) --label_names LABEL_NAMES [LABEL_NAMES ...] The list of keys in your dictionary of inputs that correspond to the labels. (default: None) --load_best_model_at_end [LOAD_BEST_MODEL_AT_END] Whether or not to load the best model found during training at the end of training. When this option is enabled, the best checkpoint will always be saved. See `save_total_limit` for more. (default: False) --metric_for_best_model METRIC_FOR_BEST_MODEL The metric to use to compare two different models. (default: None) --greater_is_better GREATER_IS_BETTER Whether the `metric_for_best_model` should be maximized or not. (default: None) --ignore_data_skip [IGNORE_DATA_SKIP] When resuming training, whether or not to skip the first epochs and batches to get to the same training data. (default: False) --fsdp FSDP Whether or not to use PyTorch Fully Sharded Data Parallel (FSDP) training (in distributed training only). The base option should be `full_shard`, `shard_grad_op` or `no_shard` and you can add CPU- offload to `full_shard` or `shard_grad_op` like this: full_shard offload` or `shard_grad_op offload`. You can add auto-wrap to `full_shard` or `shard_grad_op` with the same syntax: full_shard auto_wrap` or `shard_grad_op auto_wrap`. (default: ) --fsdp_min_num_params FSDP_MIN_NUM_PARAMS This parameter is deprecated. FSDP's minimum number of parameters for Default Auto Wrapping. (useful only when `fsdp` field is passed). (default: 0) --fsdp_config FSDP_CONFIG Config to be used with FSDP (Pytorch Fully Sharded Data Parallel). The value is either a fsdp json config file (e.g., `fsdp_config.json`) or an already loaded json file as `dict`. (default: None) --fsdp_transformer_layer_cls_to_wrap FSDP_TRANSFORMER_LAYER_CLS_TO_WRAP This parameter is deprecated. Transformer layer class name (case-sensitive) to wrap, e.g, `BertLayer`, `GPTJBlock`, `T5Block` .... (useful only when `fsdp` flag is passed). (default: None) --accelerator_config ACCELERATOR_CONFIG Config to be used with the internal Accelerator object initializtion. The value is either a accelerator json config file (e.g., `accelerator_config.json`) or an already loaded json file as `dict`. (default: None) --deepspeed DEEPSPEED Enable deepspeed and pass the path to deepspeed json config file (e.g. `ds_config.json`) or an already loaded json file as a dict (default: None) --label_smoothing_factor LABEL_SMOOTHING_FACTOR The label smoothing epsilon to apply (zero means no label smoothing). (default: 0.0) --optim {adamw_hf,adamw_torch,adamw_torch_fused,adamw_torch_xla,adamw_torch_npu_fused,adamw_apex_fused,adafactor,adamw_anyprecision,sgd,adagrad,adamw_bnb_8bit,adamw_8bit,lion_8bit,lion_32bit,paged_adamw_32bit,paged_adamw_8bit,paged_lion_32bit,paged_lion_8bit,rmsprop,rmsprop_bnb,rmsprop_bnb_8bit,rmsprop_bnb_32bit,galore_adamw,galore_adamw_8bit,galore_adafactor,galore_adamw_layerwise,galore_adamw_8bit_layerwise,galore_adafactor_layerwise,lomo,adalomo} The optimizer to use. (default: adamw_torch) --optim_args OPTIM_ARGS Optional arguments to supply to optimizer. (default: None) --adafactor [ADAFACTOR] Whether or not to replace AdamW by Adafactor. (default: False) --group_by_length [GROUP_BY_LENGTH] Whether or not to group samples of roughly the same length together when batching. (default: False) --length_column_name LENGTH_COLUMN_NAME Column name with precomputed lengths to use when grouping by length. (default: length) --report_to REPORT_TO The list of integrations to report the results and logs to. (default: None) --ddp_find_unused_parameters DDP_FIND_UNUSED_PARAMETERS When using distributed training, the value of the flag `find_unused_parameters` passed to `DistributedDataParallel`. (default: None) --ddp_bucket_cap_mb DDP_BUCKET_CAP_MB When using distributed training, the value of the flag `bucket_cap_mb` passed to `DistributedDataParallel`. (default: None) --ddp_broadcast_buffers DDP_BROADCAST_BUFFERS When using distributed training, the value of the flag `broadcast_buffers` passed to `DistributedDataParallel`. (default: None) --dataloader_pin_memory [DATALOADER_PIN_MEMORY] Whether or not to pin memory for DataLoader. (default: True) --no_dataloader_pin_memory Whether or not to pin memory for DataLoader. (default: False) --dataloader_persistent_workers [DATALOADER_PERSISTENT_WORKERS] If True, the data loader will not shut down the worker processes after a dataset has been consumed once. This allows to maintain the workers Dataset instances alive. Can potentially speed up training, but will increase RAM usage. (default: False) --skip_memory_metrics [SKIP_MEMORY_METRICS] Whether or not to skip adding of memory profiler reports to metrics. (default: True) --no_skip_memory_metrics Whether or not to skip adding of memory profiler reports to metrics. (default: False) --use_legacy_prediction_loop [USE_LEGACY_PREDICTION_LOOP] Whether or not to use the legacy prediction_loop in the Trainer. (default: False) --push_to_hub [PUSH_TO_HUB] Whether or not to upload the trained model to the model hub after training. (default: False) --resume_from_checkpoint RESUME_FROM_CHECKPOINT The path to a folder with a valid checkpoint for your model. (default: None) --hub_model_id HUB_MODEL_ID The name of the repository to keep in sync with the local `output_dir`. (default: None) --hub_strategy {end,every_save,checkpoint,all_checkpoints} The hub strategy to use when `--push_to_hub` is activated. (default: every_save) --hub_token HUB_TOKEN The token to use to push to the Model Hub. (default: None) --hub_private_repo [HUB_PRIVATE_REPO] Whether the model repository is private or not. (default: False) --hub_always_push [HUB_ALWAYS_PUSH] Unless `True`, the Trainer will skip pushes if the previous one wasn't finished yet. (default: False) --gradient_checkpointing [GRADIENT_CHECKPOINTING] If True, use gradient checkpointing to save memory at the expense of slower backward pass. (default: False) --gradient_checkpointing_kwargs GRADIENT_CHECKPOINTING_KWARGS Gradient checkpointing key word arguments such as `use_reentrant`. Will be passed to `torch.utils.checkpoint.checkpoint` through `model.gradient_checkpointing_enable`. (default: None) --include_inputs_for_metrics [INCLUDE_INPUTS_FOR_METRICS] Whether or not the inputs will be passed to the `compute_metrics` function. (default: False) --eval_do_concat_batches [EVAL_DO_CONCAT_BATCHES] Whether to recursively concat inputs/losses/labels/predictions across batches. If `False`, will instead store them as lists, with each batch kept separate. (default: True) --no_eval_do_concat_batches Whether to recursively concat inputs/losses/labels/predictions across batches. If `False`, will instead store them as lists, with each batch kept separate. (default: False) --fp16_backend {auto,apex,cpu_amp} Deprecated. Use half_precision_backend instead (default: auto) --evaluation_strategy {no,steps,epoch} Deprecated. Use `eval_strategy` instead (default: None) --push_to_hub_model_id PUSH_TO_HUB_MODEL_ID The name of the repository to which push the `Trainer`. (default: None) --push_to_hub_organization PUSH_TO_HUB_ORGANIZATION The name of the organization in with to which push the `Trainer`. (default: None) --push_to_hub_token PUSH_TO_HUB_TOKEN The token to use to push to the Model Hub. (default: None) --mp_parameters MP_PARAMETERS Used by the SageMaker launcher to send mp-specific args. Ignored in Trainer (default: ) --auto_find_batch_size [AUTO_FIND_BATCH_SIZE] Whether to automatically decrease the batch size in half and rerun the training loop again each time a CUDA Out-of-Memory was reached (default: False) --full_determinism [FULL_DETERMINISM] Whether to call enable_full_determinism instead of set_seed for reproducibility in distributed training. Important: this will negatively impact the performance, so only use it for debugging. (default: False) --torchdynamo TORCHDYNAMO This argument is deprecated, use `--torch_compile_backend` instead. (default: None) --ray_scope RAY_SCOPE The scope to use when doing hyperparameter search with Ray. By default, `"last"` will be used. Ray will then use the last checkpoint of all trials, compare those, and select the best one. However, other options are also available. See the Ray documentation (https://doc s.ray.io/en/latest/tune/api_docs/analysis.html#ray.tun e.ExperimentAnalysis.get_best_trial) for more options. (default: last) --ddp_timeout DDP_TIMEOUT Overrides the default timeout for distributed training (value should be given in seconds). (default: 1800) --torch_compile [TORCH_COMPILE] If set to `True`, the model will be wrapped in `torch.compile`. (default: False) --torch_compile_backend TORCH_COMPILE_BACKEND Which backend to use with `torch.compile`, passing one will trigger a model compilation. (default: None) --torch_compile_mode TORCH_COMPILE_MODE Which mode to use with `torch.compile`, passing one will trigger a model compilation. (default: None) --dispatch_batches DISPATCH_BATCHES Deprecated. Pass {'dispatch_batches':VALUE} to `accelerator_config`. (default: None) --split_batches SPLIT_BATCHES Deprecated. Pass {'split_batches':True} to `accelerator_config`. (default: None) --include_tokens_per_second [INCLUDE_TOKENS_PER_SECOND] If set to `True`, the speed metrics will include `tgs` (tokens per second per device). (default: False) --include_num_input_tokens_seen [INCLUDE_NUM_INPUT_TOKENS_SEEN] If set to `True`, will track the number of input tokens seen throughout training. (May be slower in distributed training) (default: False) --neftune_noise_alpha NEFTUNE_NOISE_ALPHA Activates neftune noise embeddings into the model. NEFTune has been proven to drastically improve model performances for instrcution fine-tuning. Check out the original paper here: https://arxiv.org/abs/2310.05914 and the original code here: https://github.com/neelsjain/NEFTune. Only supported for `PreTrainedModel` and `PeftModel` classes. (default: None) --optim_target_modules OPTIM_TARGET_MODULES Target modules for the optimizer defined in the `optim` argument. Only used for the GaLore optimizer at the moment. (default: None) --batch_eval_metrics [BATCH_EVAL_METRICS] Break eval metrics calculation into batches to save memory. (default: False) --eval_on_start [EVAL_ON_START] Whether to run through the entire `evaluation` step at the very beginning of training as a sanity check. (default: False) --eval_use_gather_object [EVAL_USE_GATHER_OBJECT] Whether to run recursively gather object in a nested list/tuple/dictionary of objects from all devices. (default: False) --sortish_sampler [SORTISH_SAMPLER] Whether to use SortishSampler or not. (default: False) --predict_with_generate [PREDICT_WITH_GENERATE] Whether to use generate to calculate generative metrics (ROUGE, BLEU). (default: False) --generation_max_length GENERATION_MAX_LENGTH The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default to the `max_length` value of the model configuration. (default: None) --generation_num_beams GENERATION_NUM_BEAMS The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default to the `num_beams` value of the model configuration. (default: None) --generation_config GENERATION_CONFIG Model id, file path or url pointing to a GenerationConfig json file, to use during prediction. (default: None) --use_badam [USE_BADAM] Whether or not to use the BAdam optimizer. (default: False) --badam_mode {layer,ratio} Whether to use layer-wise or ratio-wise BAdam optimizer. (default: layer) --badam_start_block BADAM_START_BLOCK The starting block index for layer-wise BAdam. (default: None) --badam_switch_mode {ascending,descending,random,fixed} the strategy of picking block to update for layer-wise BAdam. (default: ascending) --badam_switch_interval BADAM_SWITCH_INTERVAL Number of steps to update the block for layer-wise BAdam. Use -1 to disable the block update. (default: 50) --badam_update_ratio BADAM_UPDATE_RATIO The ratio of the update for ratio-wise BAdam. (default: 0.05) --badam_mask_mode {adjacent,scatter} The mode of the mask for BAdam optimizer. `adjacent` means that the trainable parameters are adjacent to each other, `scatter` means that trainable parameters are randomly choosed from the weight. (default: adjacent) --badam_verbose BADAM_VERBOSE The verbosity level of BAdam optimizer. 0 for no print, 1 for print the block prefix, 2 for print trainable parameters. (default: 0) --use_galore [USE_GALORE] Whether or not to use the gradient low-Rank projection (GaLore). (default: False) --galore_target GALORE_TARGET Name(s) of modules to apply GaLore. Use commas to separate multiple modules. Use `all` to specify all the linear modules. (default: all) --galore_rank GALORE_RANK The rank of GaLore gradients. (default: 16) --galore_update_interval GALORE_UPDATE_INTERVAL Number of steps to update the GaLore projection. (default: 200) --galore_scale GALORE_SCALE GaLore scaling coefficient. (default: 0.25) --galore_proj_type {std,reverse_std,right,left,full} Type of GaLore projection. (default: std) --galore_layerwise [GALORE_LAYERWISE] Whether or not to enable layer-wise update to further save memory. (default: False) --pref_beta PREF_BETA The beta parameter in the preference loss. (default: 0.1) --pref_ftx PREF_FTX The supervised fine-tuning loss coefficient in DPO training. (default: 0.0) --pref_loss {sigmoid,hinge,ipo,kto_pair,orpo,simpo} The type of DPO loss to use. (default: sigmoid) --dpo_label_smoothing DPO_LABEL_SMOOTHING The robust DPO label smoothing parameter in cDPO that should be between 0 and 0.5. (default: 0.0) --kto_chosen_weight KTO_CHOSEN_WEIGHT The weight factor of the desirable losses in KTO training. (default: 1.0) --kto_rejected_weight KTO_REJECTED_WEIGHT The weight factor of the undesirable losses in KTO training. (default: 1.0) --simpo_gamma SIMPO_GAMMA The target reward margin term in SimPO loss. (default: 0.5) --ppo_buffer_size PPO_BUFFER_SIZE The number of mini-batches to make experience buffer in a PPO optimization step. (default: 1) --ppo_epochs PPO_EPOCHS The number of epochs to perform in a PPO optimization step. (default: 4) --ppo_score_norm [PPO_SCORE_NORM] Use score normalization in PPO training. (default: False) --ppo_target PPO_TARGET Target KL value for adaptive KL control in PPO training. (default: 6.0) --ppo_whiten_rewards [PPO_WHITEN_REWARDS] Whiten the rewards before compute advantages in PPO training. (default: False) --ref_model REF_MODEL Path to the reference model used for the PPO or DPO training. (default: None) --ref_model_adapters REF_MODEL_ADAPTERS Path to the adapters of the reference model. (default: None) --ref_model_quantization_bit REF_MODEL_QUANTIZATION_BIT The number of bits to quantize the reference model. (default: None) --reward_model REWARD_MODEL Path to the reward model used for the PPO training. (default: None) --reward_model_adapters REWARD_MODEL_ADAPTERS Path to the adapters of the reward model. (default: None) --reward_model_quantization_bit REWARD_MODEL_QUANTIZATION_BIT The number of bits to quantize the reward model. (default: None) --reward_model_type {lora,full,api} The type of the reward model in PPO training. Lora model only supports lora training. (default: lora) --additional_target ADDITIONAL_TARGET Name(s) of modules apart from LoRA layers to be set as trainable and saved in the final checkpoint. Use commas to separate multiple modules. (default: None) --lora_alpha LORA_ALPHA The scale factor for LoRA fine-tuning (default: lora_rank * 2). (default: None) --lora_dropout LORA_DROPOUT Dropout rate for the LoRA fine-tuning. (default: 0.0) --lora_rank LORA_RANK The intrinsic dimension for LoRA fine-tuning. (default: 8) --lora_target LORA_TARGET Name(s) of target modules to apply LoRA. Use commas to separate multiple modules. Use `all` to specify all the linear modules. (default: all) --loraplus_lr_ratio LORAPLUS_LR_RATIO LoRA plus learning rate ratio (lr_B / lr_A). (default: None) --loraplus_lr_embedding LORAPLUS_LR_EMBEDDING LoRA plus learning rate for lora embedding layers. (default: 1e-06) --use_rslora [USE_RSLORA] Whether or not to use the rank stabilization scaling factor for LoRA layer. (default: False) --use_dora [USE_DORA] Whether or not to use the weight-decomposed lora method (DoRA). (default: False) --pissa_init [PISSA_INIT] Whether or not to initialize a PiSSA adapter. (default: False) --pissa_iter PISSA_ITER The number of iteration steps performed by FSVD in PiSSA. Use -1 to disable it. (default: 16) --pissa_convert [PISSA_CONVERT] Whether or not to convert the PiSSA adapter to a normal LoRA adapter. (default: False) --create_new_adapter [CREATE_NEW_ADAPTER] Whether or not to create a new adapter with randomly initialized weight. (default: False) --freeze_trainable_layers FREEZE_TRAINABLE_LAYERS The number of trainable layers for freeze (partial- parameter) fine-tuning. Positive numbers mean the last n layers are set as trainable, negative numbers mean the first n layers are set as trainable. (default: 2) --freeze_trainable_modules FREEZE_TRAINABLE_MODULES Name(s) of trainable modules for freeze (partial- parameter) fine-tuning. Use commas to separate multiple modules. Use `all` to specify all the available modules. (default: all) --freeze_extra_modules FREEZE_EXTRA_MODULES Name(s) of modules apart from hidden layers to be set as trainable for freeze (partial-parameter) fine- tuning. Use commas to separate multiple modules. (default: None) --pure_bf16 [PURE_BF16] Whether or not to train model in purely bf16 precision (without AMP). (default: False) --stage {pt,sft,rm,ppo,dpo,kto} Which stage will be performed in training. (default: sft) --finetuning_type {lora,freeze,full} Which fine-tuning method to use. (default: lora) --use_llama_pro [USE_LLAMA_PRO] Whether or not to make only the parameters in the expanded blocks trainable. (default: False) --use_adam_mini [USE_ADAM_MINI] Whether or not to use the Adam-mini optimizer. (default: False) --freeze_vision_tower [FREEZE_VISION_TOWER] Whether ot not to freeze vision tower in MLLM training. (default: True) --no_freeze_vision_tower Whether ot not to freeze vision tower in MLLM training. (default: False) --train_mm_proj_only [TRAIN_MM_PROJ_ONLY] Whether or not to train the multimodal projector for MLLM only. (default: False) --compute_accuracy [COMPUTE_ACCURACY] Whether or not to compute the token-level accuracy at evaluation. (default: False) --plot_loss [PLOT_LOSS] Whether or not to save the training loss curves. (default: False) --do_sample [DO_SAMPLE] Whether or not to use sampling, use greedy decoding otherwise. (default: True) --no_do_sample Whether or not to use sampling, use greedy decoding otherwise. (default: False) --temperature TEMPERATURE The value used to modulate the next token probabilities. (default: 0.95) --top_p TOP_P The smallest set of most probable tokens with probabilities that add up to top_p or higher are kept. (default: 0.7) --top_k TOP_K The number of highest probability vocabulary tokens to keep for top-k filtering. (default: 50) --num_beams NUM_BEAMS Number of beams for beam search. 1 means no beam search. (default: 1) --max_length MAX_LENGTH The maximum length the generated tokens can have. It can be overridden by max_new_tokens. (default: 1024) --max_new_tokens MAX_NEW_TOKENS The maximum numbers of tokens to generate, ignoring the number of tokens in the prompt. (default: 1024) --repetition_penalty REPETITION_PENALTY The parameter for repetition penalty. 1.0 means no penalty. (default: 1.0) --length_penalty LENGTH_PENALTY Exponential penalty to the length that is used with beam-based generation. (default: 1.0) --default_system DEFAULT_SYSTEM Default system message to use in chat completion. (default: None) usage: launcher.py [-h] --model_name_or_path MODEL_NAME_OR_PATH [--adapter_name_or_path ADAPTER_NAME_OR_PATH] [--adapter_folder ADAPTER_FOLDER] [--cache_dir CACHE_DIR] [--use_fast_tokenizer [USE_FAST_TOKENIZER]] [--no_use_fast_tokenizer] [--resize_vocab [RESIZE_VOCAB]] [--split_special_tokens [SPLIT_SPECIAL_TOKENS]] [--new_special_tokens NEW_SPECIAL_TOKENS] [--model_revision MODEL_REVISION] [--low_cpu_mem_usage [LOW_CPU_MEM_USAGE]] [--no_low_cpu_mem_usage] [--quantization_method {bitsandbytes,hqq,eetq}] [--quantization_bit QUANTIZATION_BIT] [--quantization_type {fp4,nf4}] [--double_quantization [DOUBLE_QUANTIZATION]] [--no_double_quantization] [--quantization_device_map {auto}] [--rope_scaling {linear,dynamic}] [--flash_attn {auto,disabled,sdpa,fa2}] [--shift_attn [SHIFT_ATTN]] [--mixture_of_depths {convert,load}] [--use_unsloth [USE_UNSLOTH]] [--visual_inputs [VISUAL_INPUTS]] [--moe_aux_loss_coef MOE_AUX_LOSS_COEF] [--disable_gradient_checkpointing [DISABLE_GRADIENT_CHECKPOINTING]] [--upcast_layernorm [UPCAST_LAYERNORM]] [--upcast_lmhead_output [UPCAST_LMHEAD_OUTPUT]] [--train_from_scratch [TRAIN_FROM_SCRATCH]] [--infer_backend {huggingface,vllm}] [--vllm_maxlen VLLM_MAXLEN] [--vllm_gpu_util VLLM_GPU_UTIL] [--vllm_enforce_eager [VLLM_ENFORCE_EAGER]] [--vllm_max_lora_rank VLLM_MAX_LORA_RANK] [--offload_folder OFFLOAD_FOLDER] [--use_cache [USE_CACHE]] [--no_use_cache] [--infer_dtype {auto,float16,bfloat16,float32}] [--hf_hub_token HF_HUB_TOKEN] [--ms_hub_token MS_HUB_TOKEN] [--export_dir EXPORT_DIR] [--export_size EXPORT_SIZE] [--export_device {cpu,auto}] [--export_quantization_bit EXPORT_QUANTIZATION_BIT] [--export_quantization_dataset EXPORT_QUANTIZATION_DATASET] [--export_quantization_nsamples EXPORT_QUANTIZATION_NSAMPLES] [--export_quantization_maxlen EXPORT_QUANTIZATION_MAXLEN] [--export_legacy_format [EXPORT_LEGACY_FORMAT]] [--export_hub_model_id EXPORT_HUB_MODEL_ID] [--print_param_status [PRINT_PARAM_STATUS]] [--template TEMPLATE] [--dataset DATASET] [--eval_dataset EVAL_DATASET] [--dataset_dir DATASET_DIR] [--cutoff_len CUTOFF_LEN] [--train_on_prompt [TRAIN_ON_PROMPT]] [--mask_history [MASK_HISTORY]] [--streaming [STREAMING]] [--buffer_size BUFFER_SIZE] [--mix_strategy {concat,interleave_under,interleave_over}] [--interleave_probs INTERLEAVE_PROBS] [--overwrite_cache [OVERWRITE_CACHE]] [--preprocessing_num_workers PREPROCESSING_NUM_WORKERS] [--max_samples MAX_SAMPLES] [--eval_num_beams EVAL_NUM_BEAMS] [--ignore_pad_token_for_loss [IGNORE_PAD_TOKEN_FOR_LOSS]] [--no_ignore_pad_token_for_loss] [--val_size VAL_SIZE] [--packing PACKING] [--neat_packing [NEAT_PACKING]] [--tool_format TOOL_FORMAT] [--tokenized_path TOKENIZED_PATH] --output_dir OUTPUT_DIR [--overwrite_output_dir [OVERWRITE_OUTPUT_DIR]] [--do_train [DO_TRAIN]] [--do_eval [DO_EVAL]] [--do_predict [DO_PREDICT]] [--eval_strategy {no,steps,epoch}] [--prediction_loss_only [PREDICTION_LOSS_ONLY]] [--per_device_train_batch_size PER_DEVICE_TRAIN_BATCH_SIZE] [--per_device_eval_batch_size PER_DEVICE_EVAL_BATCH_SIZE] [--per_gpu_train_batch_size PER_GPU_TRAIN_BATCH_SIZE] [--per_gpu_eval_batch_size PER_GPU_EVAL_BATCH_SIZE] [--gradient_accumulation_steps GRADIENT_ACCUMULATION_STEPS] [--eval_accumulation_steps EVAL_ACCUMULATION_STEPS] [--eval_delay EVAL_DELAY] [--torch_empty_cache_steps TORCH_EMPTY_CACHE_STEPS] [--learning_rate LEARNING_RATE] [--weight_decay WEIGHT_DECAY] [--adam_beta1 ADAM_BETA1] [--adam_beta2 ADAM_BETA2] [--adam_epsilon ADAM_EPSILON] [--max_grad_norm MAX_GRAD_NORM] [--num_train_epochs NUM_TRAIN_EPOCHS] [--max_steps MAX_STEPS] [--lr_scheduler_type {linear,cosine,cosine_with_restarts,polynomial,constant,constant_with_warmup,inverse_sqrt,reduce_lr_on_plateau,cosine_with_min_lr,warmup_stable_decay}] [--lr_scheduler_kwargs LR_SCHEDULER_KWARGS] [--warmup_ratio WARMUP_RATIO] [--warmup_steps WARMUP_STEPS] [--log_level {detail,debug,info,warning,error,critical,passive}] [--log_level_replica {detail,debug,info,warning,error,critical,passive}] [--log_on_each_node [LOG_ON_EACH_NODE]] [--no_log_on_each_node] [--logging_dir LOGGING_DIR] [--logging_strategy {no,steps,epoch}] [--logging_first_step [LOGGING_FIRST_STEP]] [--logging_steps LOGGING_STEPS] [--logging_nan_inf_filter [LOGGING_NAN_INF_FILTER]] [--no_logging_nan_inf_filter] [--save_strategy {no,steps,epoch}] [--save_steps SAVE_STEPS] [--save_total_limit SAVE_TOTAL_LIMIT] [--save_safetensors [SAVE_SAFETENSORS]] [--no_save_safetensors] [--save_on_each_node [SAVE_ON_EACH_NODE]] [--save_only_model [SAVE_ONLY_MODEL]] [--restore_callback_states_from_checkpoint [RESTORE_CALLBACK_STATES_FROM_CHECKPOINT]] [--no_cuda [NO_CUDA]] [--use_cpu [USE_CPU]] [--use_mps_device [USE_MPS_DEVICE]] [--seed SEED] [--data_seed DATA_SEED] [--jit_mode_eval [JIT_MODE_EVAL]] [--use_ipex [USE_IPEX]] [--bf16 [BF16]] [--fp16 [FP16]] [--fp16_opt_level FP16_OPT_LEVEL] [--half_precision_backend {auto,apex,cpu_amp}] [--bf16_full_eval [BF16_FULL_EVAL]] [--fp16_full_eval [FP16_FULL_EVAL]] [--tf32 TF32] [--local_rank LOCAL_RANK] [--ddp_backend {nccl,gloo,mpi,ccl,hccl,cncl}] [--tpu_num_cores TPU_NUM_CORES] [--tpu_metrics_debug [TPU_METRICS_DEBUG]] [--debug DEBUG [DEBUG ...]] [--dataloader_drop_last [DATALOADER_DROP_LAST]] [--eval_steps EVAL_STEPS] [--dataloader_num_workers DATALOADER_NUM_WORKERS] [--dataloader_prefetch_factor DATALOADER_PREFETCH_FACTOR] [--past_index PAST_INDEX] [--run_name RUN_NAME] [--disable_tqdm DISABLE_TQDM] [--remove_unused_columns [REMOVE_UNUSED_COLUMNS]] [--no_remove_unused_columns] [--label_names LABEL_NAMES [LABEL_NAMES ...]] [--load_best_model_at_end [LOAD_BEST_MODEL_AT_END]] [--metric_for_best_model METRIC_FOR_BEST_MODEL] [--greater_is_better GREATER_IS_BETTER] [--ignore_data_skip [IGNORE_DATA_SKIP]] [--fsdp FSDP] [--fsdp_min_num_params FSDP_MIN_NUM_PARAMS] [--fsdp_config FSDP_CONFIG] [--fsdp_transformer_layer_cls_to_wrap FSDP_TRANSFORMER_LAYER_CLS_TO_WRAP] [--accelerator_config ACCELERATOR_CONFIG] [--deepspeed DEEPSPEED] [--label_smoothing_factor LABEL_SMOOTHING_FACTOR] [--optim {adamw_hf,adamw_torch,adamw_torch_fused,adamw_torch_xla,adamw_torch_npu_fused,adamw_apex_fused,adafactor,adamw_anyprecision,sgd,adagrad,adamw_bnb_8bit,adamw_8bit,lion_8bit,lion_32bit,paged_adamw_32bit,paged_adamw_8bit,paged_lion_32bit,paged_lion_8bit,rmsprop,rmsprop_bnb,rmsprop_bnb_8bit,rmsprop_bnb_32bit,galore_adamw,galore_adamw_8bit,galore_adafactor,galore_adamw_layerwise,galore_adamw_8bit_layerwise,galore_adafactor_layerwise,lomo,adalomo}] [--optim_args OPTIM_ARGS] [--adafactor [ADAFACTOR]] [--group_by_length [GROUP_BY_LENGTH]] [--length_column_name LENGTH_COLUMN_NAME] [--report_to REPORT_TO] [--ddp_find_unused_parameters DDP_FIND_UNUSED_PARAMETERS] [--ddp_bucket_cap_mb DDP_BUCKET_CAP_MB] [--ddp_broadcast_buffers DDP_BROADCAST_BUFFERS] [--dataloader_pin_memory [DATALOADER_PIN_MEMORY]] [--no_dataloader_pin_memory] [--dataloader_persistent_workers [DATALOADER_PERSISTENT_WORKERS]] [--skip_memory_metrics [SKIP_MEMORY_METRICS]] [--no_skip_memory_metrics] [--use_legacy_prediction_loop [USE_LEGACY_PREDICTION_LOOP]] [--push_to_hub [PUSH_TO_HUB]] [--resume_from_checkpoint RESUME_FROM_CHECKPOINT] [--hub_model_id HUB_MODEL_ID] [--hub_strategy {end,every_save,checkpoint,all_checkpoints}] [--hub_token HUB_TOKEN] [--hub_private_repo [HUB_PRIVATE_REPO]] [--hub_always_push [HUB_ALWAYS_PUSH]] [--gradient_checkpointing [GRADIENT_CHECKPOINTING]] [--gradient_checkpointing_kwargs GRADIENT_CHECKPOINTING_KWARGS] [--include_inputs_for_metrics [INCLUDE_INPUTS_FOR_METRICS]] [--eval_do_concat_batches [EVAL_DO_CONCAT_BATCHES]] [--no_eval_do_concat_batches] [--fp16_backend {auto,apex,cpu_amp}] [--evaluation_strategy {no,steps,epoch}] [--push_to_hub_model_id PUSH_TO_HUB_MODEL_ID] [--push_to_hub_organization PUSH_TO_HUB_ORGANIZATION] [--push_to_hub_token PUSH_TO_HUB_TOKEN] [--mp_parameters MP_PARAMETERS] [--auto_find_batch_size [AUTO_FIND_BATCH_SIZE]] [--full_determinism [FULL_DETERMINISM]] [--torchdynamo TORCHDYNAMO] [--ray_scope RAY_SCOPE] [--ddp_timeout DDP_TIMEOUT] [--torch_compile [TORCH_COMPILE]] [--torch_compile_backend TORCH_COMPILE_BACKEND] [--torch_compile_mode TORCH_COMPILE_MODE] [--dispatch_batches DISPATCH_BATCHES] [--split_batches SPLIT_BATCHES] [--include_tokens_per_second [INCLUDE_TOKENS_PER_SECOND]] [--include_num_input_tokens_seen [INCLUDE_NUM_INPUT_TOKENS_SEEN]] [--neftune_noise_alpha NEFTUNE_NOISE_ALPHA] [--optim_target_modules OPTIM_TARGET_MODULES] [--batch_eval_metrics [BATCH_EVAL_METRICS]] [--eval_on_start [EVAL_ON_START]] [--eval_use_gather_object [EVAL_USE_GATHER_OBJECT]] [--sortish_sampler [SORTISH_SAMPLER]] [--predict_with_generate [PREDICT_WITH_GENERATE]] [--generation_max_length GENERATION_MAX_LENGTH] [--generation_num_beams GENERATION_NUM_BEAMS] [--generation_config GENERATION_CONFIG] [--use_badam [USE_BADAM]] [--badam_mode {layer,ratio}] [--badam_start_block BADAM_START_BLOCK] [--badam_switch_mode {ascending,descending,random,fixed}] [--badam_switch_interval BADAM_SWITCH_INTERVAL] [--badam_update_ratio BADAM_UPDATE_RATIO] [--badam_mask_mode {adjacent,scatter}] [--badam_verbose BADAM_VERBOSE] [--use_galore [USE_GALORE]] [--galore_target GALORE_TARGET] [--galore_rank GALORE_RANK] [--galore_update_interval GALORE_UPDATE_INTERVAL] [--galore_scale GALORE_SCALE] [--galore_proj_type {std,reverse_std,right,left,full}] [--galore_layerwise [GALORE_LAYERWISE]] [--pref_beta PREF_BETA] [--pref_ftx PREF_FTX] [--pref_loss {sigmoid,hinge,ipo,kto_pair,orpo,simpo}] [--dpo_label_smoothing DPO_LABEL_SMOOTHING] [--kto_chosen_weight KTO_CHOSEN_WEIGHT] [--kto_rejected_weight KTO_REJECTED_WEIGHT] [--simpo_gamma SIMPO_GAMMA] [--ppo_buffer_size PPO_BUFFER_SIZE] [--ppo_epochs PPO_EPOCHS] [--ppo_score_norm [PPO_SCORE_NORM]] [--ppo_target PPO_TARGET] [--ppo_whiten_rewards [PPO_WHITEN_REWARDS]] [--ref_model REF_MODEL] [--ref_model_adapters REF_MODEL_ADAPTERS] [--ref_model_quantization_bit REF_MODEL_QUANTIZATION_BIT] [--reward_model REWARD_MODEL] [--reward_model_adapters REWARD_MODEL_ADAPTERS] [--reward_model_quantization_bit REWARD_MODEL_QUANTIZATION_BIT] [--reward_model_type {lora,full,api}] [--additional_target ADDITIONAL_TARGET] [--lora_alpha LORA_ALPHA] [--lora_dropout LORA_DROPOUT] [--lora_rank LORA_RANK] [--lora_target LORA_TARGET] [--loraplus_lr_ratio LORAPLUS_LR_RATIO] [--loraplus_lr_embedding LORAPLUS_LR_EMBEDDING] [--use_rslora [USE_RSLORA]] [--use_dora [USE_DORA]] [--pissa_init [PISSA_INIT]] [--pissa_iter PISSA_ITER] [--pissa_convert [PISSA_CONVERT]] [--create_new_adapter [CREATE_NEW_ADAPTER]] [--freeze_trainable_layers FREEZE_TRAINABLE_LAYERS] [--freeze_trainable_modules FREEZE_TRAINABLE_MODULES] [--freeze_extra_modules FREEZE_EXTRA_MODULES] [--pure_bf16 [PURE_BF16]] [--stage {pt,sft,rm,ppo,dpo,kto}] [--finetuning_type {lora,freeze,full}] [--use_llama_pro [USE_LLAMA_PRO]] [--use_adam_mini [USE_ADAM_MINI]] [--freeze_vision_tower [FREEZE_VISION_TOWER]] [--no_freeze_vision_tower] [--train_mm_proj_only [TRAIN_MM_PROJ_ONLY]] [--compute_accuracy [COMPUTE_ACCURACY]] [--plot_loss [PLOT_LOSS]] [--do_sample [DO_SAMPLE]] [--no_do_sample] [--temperature TEMPERATURE] [--top_p TOP_P] [--top_k TOP_K] [--num_beams NUM_BEAMS] [--max_length MAX_LENGTH] [--max_new_tokens MAX_NEW_TOKENS] [--repetition_penalty REPETITION_PENALTY] [--length_penalty LENGTH_PENALTY] [--default_system DEFAULT_SYSTEM] optional arguments: -h, --help show this help message and exit --model_name_or_path MODEL_NAME_OR_PATH Path to the model weight or identifier from huggingface.co/models or modelscope.cn/models. (default: None) --adapter_name_or_path ADAPTER_NAME_OR_PATH Path to the adapter weight or identifier from huggingface.co/models. Use commas to separate multiple adapters. (default: None) --adapter_folder ADAPTER_FOLDER The folder containing the adapter weights to load. (default: None) --cache_dir CACHE_DIR Where to store the pre-trained models downloaded from huggingface.co or modelscope.cn. (default: None) --use_fast_tokenizer [USE_FAST_TOKENIZER] Whether or not to use one of the fast tokenizer (backed by the tokenizers library). (default: True) --no_use_fast_tokenizer Whether or not to use one of the fast tokenizer (backed by the tokenizers library). (default: False) --resize_vocab [RESIZE_VOCAB] Whether or not to resize the tokenizer vocab and the embedding layers. (default: False) --split_special_tokens [SPLIT_SPECIAL_TOKENS] Whether or not the special tokens should be split during the tokenization process. (default: False) --new_special_tokens NEW_SPECIAL_TOKENS Special tokens to be added into the tokenizer. Use commas to separate multiple tokens. (default: None) --model_revision MODEL_REVISION The specific model version to use (can be a branch name, tag name or commit id). (default: main) --low_cpu_mem_usage [LOW_CPU_MEM_USAGE] Whether or not to use memory-efficient model loading. (default: True) --no_low_cpu_mem_usage Whether or not to use memory-efficient model loading. (default: False) --quantization_method {bitsandbytes,hqq,eetq} Quantization method to use for on-the-fly quantization. (default: bitsandbytes) --quantization_bit QUANTIZATION_BIT The number of bits to quantize the model using bitsandbytes. (default: None) --quantization_type {fp4,nf4} Quantization data type to use in int4 training. (default: nf4) --double_quantization [DOUBLE_QUANTIZATION] Whether or not to use double quantization in int4 training. (default: True) --no_double_quantization Whether or not to use double quantization in int4 training. (default: False) --quantization_device_map {auto} Device map used to infer the 4-bit quantized model, needs bitsandbytes>=0.43.0. (default: None) --rope_scaling {linear,dynamic} Which scaling strategy should be adopted for the RoPE embeddings. (default: None) --flash_attn {auto,disabled,sdpa,fa2} Enable FlashAttention for faster training and inference. (default: auto) --shift_attn [SHIFT_ATTN] Enable shift short attention (S^2-Attn) proposed by LongLoRA. (default: False) --mixture_of_depths {convert,load} Convert the model to mixture-of-depths (MoD) or load the MoD model. (default: None) --use_unsloth [USE_UNSLOTH] Whether or not to use unsloth's optimization for the LoRA training. (default: False) --visual_inputs [VISUAL_INPUTS] Whethor or not to use multimodal LLM that accepts visual inputs. (default: False) --moe_aux_loss_coef MOE_AUX_LOSS_COEF Coefficient of the auxiliary router loss in mixture- of-experts model. (default: None) --disable_gradient_checkpointing [DISABLE_GRADIENT_CHECKPOINTING] Whether or not to disable gradient checkpointing. (default: False) --upcast_layernorm [UPCAST_LAYERNORM] Whether or not to upcast the layernorm weights in fp32. (default: False) --upcast_lmhead_output [UPCAST_LMHEAD_OUTPUT] Whether or not to upcast the output of lm_head in fp32. (default: False) --train_from_scratch [TRAIN_FROM_SCRATCH] Whether or not to randomly initialize the model weights. (default: False) --infer_backend {huggingface,vllm} Backend engine used at inference. (default: huggingface) --vllm_maxlen VLLM_MAXLEN Maximum sequence (prompt + response) length of the vLLM engine. (default: 2048) --vllm_gpu_util VLLM_GPU_UTIL The fraction of GPU memory in (0,1) to be used for the vLLM engine. (default: 0.9) --vllm_enforce_eager [VLLM_ENFORCE_EAGER] Whether or not to disable CUDA graph in the vLLM engine. (default: False) --vllm_max_lora_rank VLLM_MAX_LORA_RANK Maximum rank of all LoRAs in the vLLM engine. (default: 32) --offload_folder OFFLOAD_FOLDER Path to offload model weights. (default: offload) --use_cache [USE_CACHE] Whether or not to use KV cache in generation. (default: True) --no_use_cache Whether or not to use KV cache in generation. (default: False) --infer_dtype {auto,float16,bfloat16,float32} Data type for model weights and activations at inference. (default: auto) --hf_hub_token HF_HUB_TOKEN Auth token to log in with Hugging Face Hub. (default: None) --ms_hub_token MS_HUB_TOKEN Auth token to log in with ModelScope Hub. (default: None) --export_dir EXPORT_DIR Path to the directory to save the exported model. (default: None) --export_size EXPORT_SIZE The file shard size (in GB) of the exported model. (default: 1) --export_device {cpu,auto} The device used in model export, use `auto` to accelerate exporting. (default: cpu) --export_quantization_bit EXPORT_QUANTIZATION_BIT The number of bits to quantize the exported model. (default: None) --export_quantization_dataset EXPORT_QUANTIZATION_DATASET Path to the dataset or dataset name to use in quantizing the exported model. (default: None) --export_quantization_nsamples EXPORT_QUANTIZATION_NSAMPLES The number of samples used for quantization. (default: 128) --export_quantization_maxlen EXPORT_QUANTIZATION_MAXLEN The maximum length of the model inputs used for quantization. (default: 1024) --export_legacy_format [EXPORT_LEGACY_FORMAT] Whether or not to save the `.bin` files instead of `.safetensors`. (default: False) --export_hub_model_id EXPORT_HUB_MODEL_ID The name of the repository if push the model to the Hugging Face hub. (default: None) --print_param_status [PRINT_PARAM_STATUS] For debugging purposes, print the status of the parameters in the model. (default: False) --template TEMPLATE Which template to use for constructing prompts in training and inference. (default: None) --dataset DATASET The name of dataset(s) to use for training. Use commas to separate multiple datasets. (default: None) --eval_dataset EVAL_DATASET The name of dataset(s) to use for evaluation. Use commas to separate multiple datasets. (default: None) --dataset_dir DATASET_DIR Path to the folder containing the datasets. (default: data) --cutoff_len CUTOFF_LEN The cutoff length of the tokenized inputs in the dataset. (default: 1024) --train_on_prompt [TRAIN_ON_PROMPT] Whether or not to disable the mask on the prompt. (default: False) --mask_history [MASK_HISTORY] Whether or not to mask the history and train on the last turn only. (default: False) --streaming [STREAMING] Enable dataset streaming. (default: False) --buffer_size BUFFER_SIZE Size of the buffer to randomly sample examples from in dataset streaming. (default: 16384) --mix_strategy {concat,interleave_under,interleave_over} Strategy to use in dataset mixing (concat/interleave) (undersampling/oversampling). (default: concat) --interleave_probs INTERLEAVE_PROBS Probabilities to sample data from datasets. Use commas to separate multiple datasets. (default: None) --overwrite_cache [OVERWRITE_CACHE] Overwrite the cached training and evaluation sets. (default: False) --preprocessing_num_workers PREPROCESSING_NUM_WORKERS The number of processes to use for the pre-processing. (default: None) --max_samples MAX_SAMPLES For debugging purposes, truncate the number of examples for each dataset. (default: None) --eval_num_beams EVAL_NUM_BEAMS Number of beams to use for evaluation. This argument will be passed to `model.generate` (default: None) --ignore_pad_token_for_loss [IGNORE_PAD_TOKEN_FOR_LOSS] Whether or not to ignore the tokens corresponding to the pad label in loss computation. (default: True) --no_ignore_pad_token_for_loss Whether or not to ignore the tokens corresponding to the pad label in loss computation. (default: False) --val_size VAL_SIZE Size of the development set, should be an integer or a float in range `[0,1)`. (default: 0.0) --packing PACKING Enable sequences packing in training. Will automatically enable in pre-training. (default: None) --neat_packing [NEAT_PACKING] Enable sequence packing without cross-attention. (default: False) --tool_format TOOL_FORMAT Tool format to use for constructing function calling examples. (default: None) --tokenized_path TOKENIZED_PATH Path to save or load the tokenized datasets. (default: None) --output_dir OUTPUT_DIR The output directory where the model predictions and checkpoints will be written. (default: None) --overwrite_output_dir [OVERWRITE_OUTPUT_DIR] Overwrite the content of the output directory. Use this to continue training if output_dir points to a checkpoint directory. (default: False) --do_train [DO_TRAIN] Whether to run training. (default: False) --do_eval [DO_EVAL] Whether to run eval on the dev set. (default: False) --do_predict [DO_PREDICT] Whether to run predictions on the test set. (default: False) --eval_strategy {no,steps,epoch} The evaluation strategy to use. (default: no) --prediction_loss_only [PREDICTION_LOSS_ONLY] When performing evaluation and predictions, only returns the loss. (default: False) --per_device_train_batch_size PER_DEVICE_TRAIN_BATCH_SIZE Batch size per GPU/TPU/MPS/NPU core/CPU for training. (default: 8) --per_device_eval_batch_size PER_DEVICE_EVAL_BATCH_SIZE Batch size per GPU/TPU/MPS/NPU core/CPU for evaluation. (default: 8) --per_gpu_train_batch_size PER_GPU_TRAIN_BATCH_SIZE Deprecated, the use of `--per_device_train_batch_size` is preferred. Batch size per GPU/TPU core/CPU for training. (default: None) --per_gpu_eval_batch_size PER_GPU_EVAL_BATCH_SIZE Deprecated, the use of `--per_device_eval_batch_size` is preferred. Batch size per GPU/TPU core/CPU for evaluation. (default: None) --gradient_accumulation_steps GRADIENT_ACCUMULATION_STEPS Number of updates steps to accumulate before performing a backward/update pass. (default: 1) --eval_accumulation_steps EVAL_ACCUMULATION_STEPS Number of predictions steps to accumulate before moving the tensors to the CPU. (default: None) --eval_delay EVAL_DELAY Number of epochs or steps to wait for before the first evaluation can be performed, depending on the eval_strategy. (default: 0) --torch_empty_cache_steps TORCH_EMPTY_CACHE_STEPS Number of steps to wait before calling `torch..empty_cache()`.This can help avoid CUDA out-of-memory errors by lowering peak VRAM usage at a cost of about [10{'option_strings': ['-- torch_empty_cache_steps'], 'dest': 'torch_empty_cache_steps', 'nargs': None, 'const': None, 'default': None, 'type': 'int', 'choices': None, 'required': False, 'help': 'Number of steps to wait before calling `torch..empty_cache()`.This can help avoid CUDA out-of-memory errors by lowering peak VRAM usage at a cost of about [10% slower performance] (https://github.com/huggingface/transformers/issues/31 372).If left unset or set to None, cache will not be emptied.', 'metavar': None, 'container': , 'prog': 'launcher.py'}lower performance](https://githu b.com/huggingface/transformers/issues/31372).If left unset or set to None, cache will not be emptied. (default: None) --learning_rate LEARNING_RATE The initial learning rate for AdamW. (default: 5e-05) --weight_decay WEIGHT_DECAY Weight decay for AdamW if we apply some. (default: 0.0) --adam_beta1 ADAM_BETA1 Beta1 for AdamW optimizer (default: 0.9) --adam_beta2 ADAM_BETA2 Beta2 for AdamW optimizer (default: 0.999) --adam_epsilon ADAM_EPSILON Epsilon for AdamW optimizer. (default: 1e-08) --max_grad_norm MAX_GRAD_NORM Max gradient norm. (default: 1.0) --num_train_epochs NUM_TRAIN_EPOCHS Total number of training epochs to perform. (default: 3.0) --max_steps MAX_STEPS If > 0: set total number of training steps to perform. Override num_train_epochs. (default: -1) --lr_scheduler_type {linear,cosine,cosine_with_restarts,polynomial,constant,constant_with_warmup,inverse_sqrt,reduce_lr_on_plateau,cosine_with_min_lr,warmup_stable_decay} The scheduler type to use. (default: linear) --lr_scheduler_kwargs LR_SCHEDULER_KWARGS Extra parameters for the lr_scheduler such as {'num_cycles': 1} for the cosine with hard restarts. (default: {}) --warmup_ratio WARMUP_RATIO Linear warmup over warmup_ratio fraction of total steps. (default: 0.0) --warmup_steps WARMUP_STEPS Linear warmup over warmup_steps. (default: 0) --log_level {detail,debug,info,warning,error,critical,passive} Logger log level to use on the main node. Possible choices are the log levels as strings: 'debug', 'info', 'warning', 'error' and 'critical', plus a 'passive' level which doesn't set anything and lets the application set the level. Defaults to 'passive'. (default: passive) --log_level_replica {detail,debug,info,warning,error,critical,passive} Logger log level to use on replica nodes. Same choices and defaults as ``log_level`` (default: warning) --log_on_each_node [LOG_ON_EACH_NODE] When doing a multinode distributed training, whether to log once per node or just once on the main node. (default: True) --no_log_on_each_node When doing a multinode distributed training, whether to log once per node or just once on the main node. (default: False) --logging_dir LOGGING_DIR Tensorboard log dir. (default: None) --logging_strategy {no,steps,epoch} The logging strategy to use. (default: steps) --logging_first_step [LOGGING_FIRST_STEP] Log the first global_step (default: False) --logging_steps LOGGING_STEPS Log every X updates steps. Should be an integer or a float in range `[0,1)`. If smaller than 1, will be interpreted as ratio of total training steps. (default: 500) --logging_nan_inf_filter [LOGGING_NAN_INF_FILTER] Filter nan and inf losses for logging. (default: True) --no_logging_nan_inf_filter Filter nan and inf losses for logging. (default: False) --save_strategy {no,steps,epoch} The checkpoint save strategy to use. (default: steps) --save_steps SAVE_STEPS Save checkpoint every X updates steps. Should be an integer or a float in range `[0,1)`. If smaller than 1, will be interpreted as ratio of total training steps. (default: 500) --save_total_limit SAVE_TOTAL_LIMIT If a value is passed, will limit the total amount of checkpoints. Deletes the older checkpoints in `output_dir`. When `load_best_model_at_end` is enabled, the 'best' checkpoint according to `metric_for_best_model` will always be retained in addition to the most recent ones. For example, for `save_total_limit=5` and `load_best_model_at_end=True`, the four last checkpoints will always be retained alongside the best model. When `save_total_limit=1` and `load_best_model_at_end=True`, it is possible that two checkpoints are saved: the last one and the best one (if they are different). Default is unlimited checkpoints (default: None) --save_safetensors [SAVE_SAFETENSORS] Use safetensors saving and loading for state dicts instead of default torch.load and torch.save. (default: True) --no_save_safetensors Use safetensors saving and loading for state dicts instead of default torch.load and torch.save. (default: False) --save_on_each_node [SAVE_ON_EACH_NODE] When doing multi-node distributed training, whether to save models and checkpoints on each node, or only on the main one (default: False) --save_only_model [SAVE_ONLY_MODEL] When checkpointing, whether to only save the model, or also the optimizer, scheduler & rng state.Note that when this is true, you won't be able to resume training from checkpoint.This enables you to save storage by not storing the optimizer, scheduler & rng state.You can only load the model using from_pretrained with this option set to True. (default: False) --restore_callback_states_from_checkpoint [RESTORE_CALLBACK_STATES_FROM_CHECKPOINT] Whether to restore the callback states from the checkpoint. If `True`, will override callbacks passed to the `Trainer` if they exist in the checkpoint. (default: False) --no_cuda [NO_CUDA] This argument is deprecated. It will be removed in version 5.0 of 🤗 Transformers. (default: False) --use_cpu [USE_CPU] Whether or not to use cpu. If set to False, we will use cuda/tpu/mps/npu device if available. (default: False) --use_mps_device [USE_MPS_DEVICE] This argument is deprecated. `mps` device will be used if available similar to `cuda` device. It will be removed in version 5.0 of 🤗 Transformers (default: False) --seed SEED Random seed that will be set at the beginning of training. (default: 42) --data_seed DATA_SEED Random seed to be used with data samplers. (default: None) --jit_mode_eval [JIT_MODE_EVAL] Whether or not to use PyTorch jit trace for inference (default: False) --use_ipex [USE_IPEX] Use Intel extension for PyTorch when it is available, installation: 'https://github.com/intel/intel- extension-for-pytorch' (default: False) --bf16 [BF16] Whether to use bf16 (mixed) precision instead of 32-bit. Requires Ampere or higher NVIDIA architecture or using CPU (use_cpu) or Ascend NPU. This is an experimental API and it may change. (default: False) --fp16 [FP16] Whether to use fp16 (mixed) precision instead of 32-bit (default: False) --fp16_opt_level FP16_OPT_LEVEL For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']. See details at https://nvidia.github.io/apex/amp.html (default: O1) --half_precision_backend {auto,apex,cpu_amp} The backend to be used for half precision. (default: auto) --bf16_full_eval [BF16_FULL_EVAL] Whether to use full bfloat16 evaluation instead of 32-bit. This is an experimental API and it may change. (default: False) --fp16_full_eval [FP16_FULL_EVAL] Whether to use full float16 evaluation instead of 32-bit (default: False) --tf32 TF32 Whether to enable tf32 mode, available in Ampere and newer GPU architectures. This is an experimental API and it may change. (default: None) --local_rank LOCAL_RANK For distributed training: local_rank (default: -1) --ddp_backend {nccl,gloo,mpi,ccl,hccl,cncl} The backend to be used for distributed training (default: None) --tpu_num_cores TPU_NUM_CORES TPU: Number of TPU cores (automatically passed by launcher script) (default: None) --tpu_metrics_debug [TPU_METRICS_DEBUG] Deprecated, the use of `--debug tpu_metrics_debug` is preferred. TPU: Whether to print debug metrics (default: False) --debug DEBUG [DEBUG ...] Whether or not to enable debug mode. Current options: `underflow_overflow` (Detect underflow and overflow in activations and weights), `tpu_metrics_debug` (print debug metrics on TPU). (default: None) --dataloader_drop_last [DATALOADER_DROP_LAST] Drop the last incomplete batch if it is not divisible by the batch size. (default: False) --eval_steps EVAL_STEPS Run an evaluation every X steps. Should be an integer or a float in range `[0,1)`. If smaller than 1, will be interpreted as ratio of total training steps. (default: None) --dataloader_num_workers DATALOADER_NUM_WORKERS Number of subprocesses to use for data loading (PyTorch only). 0 means that the data will be loaded in the main process. (default: 0) --dataloader_prefetch_factor DATALOADER_PREFETCH_FACTOR Number of batches loaded in advance by each worker. 2 means there will be a total of 2 * num_workers batches prefetched across all workers. Default is 2 for PyTorch < 2.0.0 and otherwise None. (default: None) --past_index PAST_INDEX If >=0, uses the corresponding part of the output as the past state for next step. (default: -1) --run_name RUN_NAME An optional descriptor for the run. Notably used for wandb, mlflow and comet logging. (default: None) --disable_tqdm DISABLE_TQDM Whether or not to disable the tqdm progress bars. (default: None) --remove_unused_columns [REMOVE_UNUSED_COLUMNS] Remove columns not required by the model when using an nlp.Dataset. (default: True) --no_remove_unused_columns Remove columns not required by the model when using an nlp.Dataset. (default: False) --label_names LABEL_NAMES [LABEL_NAMES ...] The list of keys in your dictionary of inputs that correspond to the labels. (default: None) --load_best_model_at_end [LOAD_BEST_MODEL_AT_END] Whether or not to load the best model found during training at the end of training. When this option is enabled, the best checkpoint will always be saved. See `save_total_limit` for more. (default: False) --metric_for_best_model METRIC_FOR_BEST_MODEL The metric to use to compare two different models. (default: None) --greater_is_better GREATER_IS_BETTER Whether the `metric_for_best_model` should be maximized or not. (default: None) --ignore_data_skip [IGNORE_DATA_SKIP] When resuming training, whether or not to skip the first epochs and batches to get to the same training data. (default: False) --fsdp FSDP Whether or not to use PyTorch Fully Sharded Data Parallel (FSDP) training (in distributed training only). The base option should be `full_shard`, `shard_grad_op` or `no_shard` and you can add CPU- offload to `full_shard` or `shard_grad_op` like this: full_shard offload` or `shard_grad_op offload`. You can add auto-wrap to `full_shard` or `shard_grad_op` with the same syntax: full_shard auto_wrap` or `shard_grad_op auto_wrap`. (default: ) --fsdp_min_num_params FSDP_MIN_NUM_PARAMS This parameter is deprecated. FSDP's minimum number of parameters for Default Auto Wrapping. (useful only when `fsdp` field is passed). (default: 0) --fsdp_config FSDP_CONFIG Config to be used with FSDP (Pytorch Fully Sharded Data Parallel). The value is either a fsdp json config file (e.g., `fsdp_config.json`) or an already loaded json file as `dict`. (default: None) --fsdp_transformer_layer_cls_to_wrap FSDP_TRANSFORMER_LAYER_CLS_TO_WRAP This parameter is deprecated. Transformer layer class name (case-sensitive) to wrap, e.g, `BertLayer`, `GPTJBlock`, `T5Block` .... (useful only when `fsdp` flag is passed). (default: None) --accelerator_config ACCELERATOR_CONFIG Config to be used with the internal Accelerator object initializtion. The value is either a accelerator json config file (e.g., `accelerator_config.json`) or an already loaded json file as `dict`. (default: None) --deepspeed DEEPSPEED Enable deepspeed and pass the path to deepspeed json config file (e.g. `ds_config.json`) or an already loaded json file as a dict (default: None) --label_smoothing_factor LABEL_SMOOTHING_FACTOR The label smoothing epsilon to apply (zero means no label smoothing). (default: 0.0) --optim {adamw_hf,adamw_torch,adamw_torch_fused,adamw_torch_xla,adamw_torch_npu_fused,adamw_apex_fused,adafactor,adamw_anyprecision,sgd,adagrad,adamw_bnb_8bit,adamw_8bit,lion_8bit,lion_32bit,paged_adamw_32bit,paged_adamw_8bit,paged_lion_32bit,paged_lion_8bit,rmsprop,rmsprop_bnb,rmsprop_bnb_8bit,rmsprop_bnb_32bit,galore_adamw,galore_adamw_8bit,galore_adafactor,galore_adamw_layerwise,galore_adamw_8bit_layerwise,galore_adafactor_layerwise,lomo,adalomo} The optimizer to use. (default: adamw_torch) --optim_args OPTIM_ARGS Optional arguments to supply to optimizer. (default: None) --adafactor [ADAFACTOR] Whether or not to replace AdamW by Adafactor. (default: False) --group_by_length [GROUP_BY_LENGTH] Whether or not to group samples of roughly the same length together when batching. (default: False) --length_column_name LENGTH_COLUMN_NAME Column name with precomputed lengths to use when grouping by length. (default: length) --report_to REPORT_TO The list of integrations to report the results and logs to. (default: None) --ddp_find_unused_parameters DDP_FIND_UNUSED_PARAMETERS When using distributed training, the value of the flag `find_unused_parameters` passed to `DistributedDataParallel`. (default: None) --ddp_bucket_cap_mb DDP_BUCKET_CAP_MB When using distributed training, the value of the flag `bucket_cap_mb` passed to `DistributedDataParallel`. (default: None) --ddp_broadcast_buffers DDP_BROADCAST_BUFFERS When using distributed training, the value of the flag `broadcast_buffers` passed to `DistributedDataParallel`. (default: None) --dataloader_pin_memory [DATALOADER_PIN_MEMORY] Whether or not to pin memory for DataLoader. (default: True) --no_dataloader_pin_memory Whether or not to pin memory for DataLoader. (default: False) --dataloader_persistent_workers [DATALOADER_PERSISTENT_WORKERS] If True, the data loader will not shut down the worker processes after a dataset has been consumed once. This allows to maintain the workers Dataset instances alive. Can potentially speed up training, but will increase RAM usage. (default: False) --skip_memory_metrics [SKIP_MEMORY_METRICS] Whether or not to skip adding of memory profiler reports to metrics. (default: True) --no_skip_memory_metrics Whether or not to skip adding of memory profiler reports to metrics. (default: False) --use_legacy_prediction_loop [USE_LEGACY_PREDICTION_LOOP] Whether or not to use the legacy prediction_loop in the Trainer. (default: False) --push_to_hub [PUSH_TO_HUB] Whether or not to upload the trained model to the model hub after training. (default: False) --resume_from_checkpoint RESUME_FROM_CHECKPOINT The path to a folder with a valid checkpoint for your model. (default: None) --hub_model_id HUB_MODEL_ID The name of the repository to keep in sync with the local `output_dir`. (default: None) --hub_strategy {end,every_save,checkpoint,all_checkpoints} The hub strategy to use when `--push_to_hub` is activated. (default: every_save) --hub_token HUB_TOKEN The token to use to push to the Model Hub. (default: None) --hub_private_repo [HUB_PRIVATE_REPO] Whether the model repository is private or not. (default: False) --hub_always_push [HUB_ALWAYS_PUSH] Unless `True`, the Trainer will skip pushes if the previous one wasn't finished yet. (default: False) --gradient_checkpointing [GRADIENT_CHECKPOINTING] If True, use gradient checkpointing to save memory at the expense of slower backward pass. (default: False) --gradient_checkpointing_kwargs GRADIENT_CHECKPOINTING_KWARGS Gradient checkpointing key word arguments such as `use_reentrant`. Will be passed to `torch.utils.checkpoint.checkpoint` through `model.gradient_checkpointing_enable`. (default: None) --include_inputs_for_metrics [INCLUDE_INPUTS_FOR_METRICS] Whether or not the inputs will be passed to the `compute_metrics` function. (default: False) --eval_do_concat_batches [EVAL_DO_CONCAT_BATCHES] Whether to recursively concat inputs/losses/labels/predictions across batches. If `False`, will instead store them as lists, with each batch kept separate. (default: True) --no_eval_do_concat_batches Whether to recursively concat inputs/losses/labels/predictions across batches. If `False`, will instead store them as lists, with each batch kept separate. (default: False) --fp16_backend {auto,apex,cpu_amp} Deprecated. Use half_precision_backend instead (default: auto) --evaluation_strategy {no,steps,epoch} Deprecated. Use `eval_strategy` instead (default: None) --push_to_hub_model_id PUSH_TO_HUB_MODEL_ID The name of the repository to which push the `Trainer`. (default: None) --push_to_hub_organization PUSH_TO_HUB_ORGANIZATION The name of the organization in with to which push the `Trainer`. (default: None) --push_to_hub_token PUSH_TO_HUB_TOKEN The token to use to push to the Model Hub. (default: None) --mp_parameters MP_PARAMETERS Used by the SageMaker launcher to send mp-specific args. Ignored in Trainer (default: ) --auto_find_batch_size [AUTO_FIND_BATCH_SIZE] Whether to automatically decrease the batch size in half and rerun the training loop again each time a CUDA Out-of-Memory was reached (default: False) --full_determinism [FULL_DETERMINISM] Whether to call enable_full_determinism instead of set_seed for reproducibility in distributed training. Important: this will negatively impact the performance, so only use it for debugging. (default: False) --torchdynamo TORCHDYNAMO This argument is deprecated, use `--torch_compile_backend` instead. (default: None) --ray_scope RAY_SCOPE The scope to use when doing hyperparameter search with Ray. By default, `"last"` will be used. Ray will then use the last checkpoint of all trials, compare those, and select the best one. However, other options are also available. See the Ray documentation (https://doc s.ray.io/en/latest/tune/api_docs/analysis.html#ray.tun e.ExperimentAnalysis.get_best_trial) for more options. (default: last) --ddp_timeout DDP_TIMEOUT Overrides the default timeout for distributed training (value should be given in seconds). (default: 1800) --torch_compile [TORCH_COMPILE] If set to `True`, the model will be wrapped in `torch.compile`. (default: False) --torch_compile_backend TORCH_COMPILE_BACKEND Which backend to use with `torch.compile`, passing one will trigger a model compilation. (default: None) --torch_compile_mode TORCH_COMPILE_MODE Which mode to use with `torch.compile`, passing one will trigger a model compilation. (default: None) --dispatch_batches DISPATCH_BATCHES Deprecated. Pass {'dispatch_batches':VALUE} to `accelerator_config`. (default: None) --split_batches SPLIT_BATCHES Deprecated. Pass {'split_batches':True} to `accelerator_config`. (default: None) --include_tokens_per_second [INCLUDE_TOKENS_PER_SECOND] If set to `True`, the speed metrics will include `tgs` (tokens per second per device). (default: False) --include_num_input_tokens_seen [INCLUDE_NUM_INPUT_TOKENS_SEEN] If set to `True`, will track the number of input tokens seen throughout training. (May be slower in distributed training) (default: False) --neftune_noise_alpha NEFTUNE_NOISE_ALPHA Activates neftune noise embeddings into the model. NEFTune has been proven to drastically improve model performances for instrcution fine-tuning. Check out the original paper here: https://arxiv.org/abs/2310.05914 and the original code here: https://github.com/neelsjain/NEFTune. Only supported for `PreTrainedModel` and `PeftModel` classes. (default: None) --optim_target_modules OPTIM_TARGET_MODULES Target modules for the optimizer defined in the `optim` argument. Only used for the GaLore optimizer at the moment. (default: None) --batch_eval_metrics [BATCH_EVAL_METRICS] Break eval metrics calculation into batches to save memory. (default: False) --eval_on_start [EVAL_ON_START] Whether to run through the entire `evaluation` step at the very beginning of training as a sanity check. (default: False) --eval_use_gather_object [EVAL_USE_GATHER_OBJECT] Whether to run recursively gather object in a nested list/tuple/dictionary of objects from all devices. (default: False) --sortish_sampler [SORTISH_SAMPLER] Whether to use SortishSampler or not. (default: False) --predict_with_generate [PREDICT_WITH_GENERATE] Whether to use generate to calculate generative metrics (ROUGE, BLEU). (default: False) --generation_max_length GENERATION_MAX_LENGTH The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default to the `max_length` value of the model configuration. (default: None) --generation_num_beams GENERATION_NUM_BEAMS The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default to the `num_beams` value of the model configuration. (default: None) --generation_config GENERATION_CONFIG Model id, file path or url pointing to a GenerationConfig json file, to use during prediction. (default: None) --use_badam [USE_BADAM] Whether or not to use the BAdam optimizer. (default: False) --badam_mode {layer,ratio} Whether to use layer-wise or ratio-wise BAdam optimizer. (default: layer) --badam_start_block BADAM_START_BLOCK The starting block index for layer-wise BAdam. (default: None) --badam_switch_mode {ascending,descending,random,fixed} the strategy of picking block to update for layer-wise BAdam. (default: ascending) --badam_switch_interval BADAM_SWITCH_INTERVAL Number of steps to update the block for layer-wise BAdam. Use -1 to disable the block update. (default: 50) --badam_update_ratio BADAM_UPDATE_RATIO The ratio of the update for ratio-wise BAdam. (default: 0.05) --badam_mask_mode {adjacent,scatter} The mode of the mask for BAdam optimizer. `adjacent` means that the trainable parameters are adjacent to each other, `scatter` means that trainable parameters are randomly choosed from the weight. (default: adjacent) --badam_verbose BADAM_VERBOSE The verbosity level of BAdam optimizer. 0 for no print, 1 for print the block prefix, 2 for print trainable parameters. (default: 0) --use_galore [USE_GALORE] Whether or not to use the gradient low-Rank projection (GaLore). (default: False) --galore_target GALORE_TARGET Name(s) of modules to apply GaLore. Use commas to separate multiple modules. Use `all` to specify all the linear modules. (default: all) --galore_rank GALORE_RANK The rank of GaLore gradients. (default: 16) --galore_update_interval GALORE_UPDATE_INTERVAL Number of steps to update the GaLore projection. (default: 200) --galore_scale GALORE_SCALE GaLore scaling coefficient. (default: 0.25) --galore_proj_type {std,reverse_std,right,left,full} Type of GaLore projection. (default: std) --galore_layerwise [GALORE_LAYERWISE] Whether or not to enable layer-wise update to further save memory. (default: False) --pref_beta PREF_BETA The beta parameter in the preference loss. (default: 0.1) --pref_ftx PREF_FTX The supervised fine-tuning loss coefficient in DPO training. (default: 0.0) --pref_loss {sigmoid,hinge,ipo,kto_pair,orpo,simpo} The type of DPO loss to use. (default: sigmoid) --dpo_label_smoothing DPO_LABEL_SMOOTHING The robust DPO label smoothing parameter in cDPO that should be between 0 and 0.5. (default: 0.0) --kto_chosen_weight KTO_CHOSEN_WEIGHT The weight factor of the desirable losses in KTO training. (default: 1.0) --kto_rejected_weight KTO_REJECTED_WEIGHT The weight factor of the undesirable losses in KTO training. (default: 1.0) --simpo_gamma SIMPO_GAMMA The target reward margin term in SimPO loss. (default: 0.5) --ppo_buffer_size PPO_BUFFER_SIZE The number of mini-batches to make experience buffer in a PPO optimization step. (default: 1) --ppo_epochs PPO_EPOCHS The number of epochs to perform in a PPO optimization step. (default: 4) --ppo_score_norm [PPO_SCORE_NORM] Use score normalization in PPO training. (default: False) --ppo_target PPO_TARGET Target KL value for adaptive KL control in PPO training. (default: 6.0) --ppo_whiten_rewards [PPO_WHITEN_REWARDS] Whiten the rewards before compute advantages in PPO training. (default: False) --ref_model REF_MODEL Path to the reference model used for the PPO or DPO training. (default: None) --ref_model_adapters REF_MODEL_ADAPTERS Path to the adapters of the reference model. (default: None) --ref_model_quantization_bit REF_MODEL_QUANTIZATION_BIT The number of bits to quantize the reference model. (default: None) --reward_model REWARD_MODEL Path to the reward model used for the PPO training. (default: None) --reward_model_adapters REWARD_MODEL_ADAPTERS Path to the adapters of the reward model. (default: None) --reward_model_quantization_bit REWARD_MODEL_QUANTIZATION_BIT The number of bits to quantize the reward model. (default: None) --reward_model_type {lora,full,api} The type of the reward model in PPO training. Lora model only supports lora training. (default: lora) --additional_target ADDITIONAL_TARGET Name(s) of modules apart from LoRA layers to be set as trainable and saved in the final checkpoint. Use commas to separate multiple modules. (default: None) --lora_alpha LORA_ALPHA The scale factor for LoRA fine-tuning (default: lora_rank * 2). (default: None) --lora_dropout LORA_DROPOUT Dropout rate for the LoRA fine-tuning. (default: 0.0) --lora_rank LORA_RANK The intrinsic dimension for LoRA fine-tuning. (default: 8) --lora_target LORA_TARGET Name(s) of target modules to apply LoRA. Use commas to separate multiple modules. Use `all` to specify all the linear modules. (default: all) --loraplus_lr_ratio LORAPLUS_LR_RATIO LoRA plus learning rate ratio (lr_B / lr_A). (default: None) --loraplus_lr_embedding LORAPLUS_LR_EMBEDDING LoRA plus learning rate for lora embedding layers. (default: 1e-06) --use_rslora [USE_RSLORA] Whether or not to use the rank stabilization scaling factor for LoRA layer. (default: False) --use_dora [USE_DORA] Whether or not to use the weight-decomposed lora method (DoRA). (default: False) --pissa_init [PISSA_INIT] Whether or not to initialize a PiSSA adapter. (default: False) --pissa_iter PISSA_ITER The number of iteration steps performed by FSVD in PiSSA. Use -1 to disable it. (default: 16) --pissa_convert [PISSA_CONVERT] Whether or not to convert the PiSSA adapter to a normal LoRA adapter. (default: False) --create_new_adapter [CREATE_NEW_ADAPTER] Whether or not to create a new adapter with randomly initialized weight. (default: False) --freeze_trainable_layers FREEZE_TRAINABLE_LAYERS The number of trainable layers for freeze (partial- parameter) fine-tuning. Positive numbers mean the last n layers are set as trainable, negative numbers mean the first n layers are set as trainable. (default: 2) --freeze_trainable_modules FREEZE_TRAINABLE_MODULES Name(s) of trainable modules for freeze (partial- parameter) fine-tuning. Use commas to separate multiple modules. Use `all` to specify all the available modules. (default: all) --freeze_extra_modules FREEZE_EXTRA_MODULES Name(s) of modules apart from hidden layers to be set as trainable for freeze (partial-parameter) fine- tuning. Use commas to separate multiple modules. (default: None) --pure_bf16 [PURE_BF16] Whether or not to train model in purely bf16 precision (without AMP). (default: False) --stage {pt,sft,rm,ppo,dpo,kto} Which stage will be performed in training. (default: sft) --finetuning_type {lora,freeze,full} Which fine-tuning method to use. (default: lora) --use_llama_pro [USE_LLAMA_PRO] Whether or not to make only the parameters in the expanded blocks trainable. (default: False) --use_adam_mini [USE_ADAM_MINI] Whether or not to use the Adam-mini optimizer. (default: False) --freeze_vision_tower [FREEZE_VISION_TOWER] Whether ot not to freeze vision tower in MLLM training. (default: True) --no_freeze_vision_tower Whether ot not to freeze vision tower in MLLM training. (default: False) --train_mm_proj_only [TRAIN_MM_PROJ_ONLY] Whether or not to train the multimodal projector for MLLM only. (default: False) --compute_accuracy [COMPUTE_ACCURACY] Whether or not to compute the token-level accuracy at evaluation. (default: False) --plot_loss [PLOT_LOSS] Whether or not to save the training loss curves. (default: False) --do_sample [DO_SAMPLE] Whether or not to use sampling, use greedy decoding otherwise. (default: True) --no_do_sample Whether or not to use sampling, use greedy decoding otherwise. (default: False) --temperature TEMPERATURE The value used to modulate the next token probabilities. (default: 0.95) --top_p TOP_P The smallest set of most probable tokens with probabilities that add up to top_p or higher are kept. (default: 0.7) --top_k TOP_K The number of highest probability vocabulary tokens to keep for top-k filtering. (default: 50) --num_beams NUM_BEAMS Number of beams for beam search. 1 means no beam search. (default: 1) --max_length MAX_LENGTH The maximum length the generated tokens can have. It can be overridden by max_new_tokens. (default: 1024) --max_new_tokens MAX_NEW_TOKENS The maximum numbers of tokens to generate, ignoring the number of tokens in the prompt. (default: 1024) --repetition_penalty REPETITION_PENALTY The parameter for repetition penalty. 1.0 means no penalty. (default: 1.0) --length_penalty LENGTH_PENALTY Exponential penalty to the length that is used with beam-based generation. (default: 1.0) --default_system DEFAULT_SYSTEM Default system message to use in chat completion. (default: None) usage: launcher.py [-h] --model_name_or_path MODEL_NAME_OR_PATH [--adapter_name_or_path ADAPTER_NAME_OR_PATH] [--adapter_folder ADAPTER_FOLDER] [--cache_dir CACHE_DIR] [--use_fast_tokenizer [USE_FAST_TOKENIZER]] [--no_use_fast_tokenizer] [--resize_vocab [RESIZE_VOCAB]] [--split_special_tokens [SPLIT_SPECIAL_TOKENS]] [--new_special_tokens NEW_SPECIAL_TOKENS] [--model_revision MODEL_REVISION] [--low_cpu_mem_usage [LOW_CPU_MEM_USAGE]] [--no_low_cpu_mem_usage] [--quantization_method {bitsandbytes,hqq,eetq}] [--quantization_bit QUANTIZATION_BIT] [--quantization_type {fp4,nf4}] [--double_quantization [DOUBLE_QUANTIZATION]] [--no_double_quantization] [--quantization_device_map {auto}] [--rope_scaling {linear,dynamic}] [--flash_attn {auto,disabled,sdpa,fa2}] [--shift_attn [SHIFT_ATTN]] [--mixture_of_depths {convert,load}] [--use_unsloth [USE_UNSLOTH]] [--visual_inputs [VISUAL_INPUTS]] [--moe_aux_loss_coef MOE_AUX_LOSS_COEF] [--disable_gradient_checkpointing [DISABLE_GRADIENT_CHECKPOINTING]] [--upcast_layernorm [UPCAST_LAYERNORM]] [--upcast_lmhead_output [UPCAST_LMHEAD_OUTPUT]] [--train_from_scratch [TRAIN_FROM_SCRATCH]] [--infer_backend {huggingface,vllm}] [--vllm_maxlen VLLM_MAXLEN] [--vllm_gpu_util VLLM_GPU_UTIL] [--vllm_enforce_eager [VLLM_ENFORCE_EAGER]] [--vllm_max_lora_rank VLLM_MAX_LORA_RANK] [--offload_folder OFFLOAD_FOLDER] [--use_cache [USE_CACHE]] [--no_use_cache] [--infer_dtype {auto,float16,bfloat16,float32}] [--hf_hub_token HF_HUB_TOKEN] [--ms_hub_token MS_HUB_TOKEN] [--export_dir EXPORT_DIR] [--export_size EXPORT_SIZE] [--export_device {cpu,auto}] [--export_quantization_bit EXPORT_QUANTIZATION_BIT] [--export_quantization_dataset EXPORT_QUANTIZATION_DATASET] [--export_quantization_nsamples EXPORT_QUANTIZATION_NSAMPLES] [--export_quantization_maxlen EXPORT_QUANTIZATION_MAXLEN] [--export_legacy_format [EXPORT_LEGACY_FORMAT]] [--export_hub_model_id EXPORT_HUB_MODEL_ID] [--print_param_status [PRINT_PARAM_STATUS]] [--template TEMPLATE] [--dataset DATASET] [--eval_dataset EVAL_DATASET] [--dataset_dir DATASET_DIR] [--cutoff_len CUTOFF_LEN] [--train_on_prompt [TRAIN_ON_PROMPT]] [--mask_history [MASK_HISTORY]] [--streaming [STREAMING]] [--buffer_size BUFFER_SIZE] [--mix_strategy {concat,interleave_under,interleave_over}] [--interleave_probs INTERLEAVE_PROBS] [--overwrite_cache [OVERWRITE_CACHE]] [--preprocessing_num_workers PREPROCESSING_NUM_WORKERS] [--max_samples MAX_SAMPLES] [--eval_num_beams EVAL_NUM_BEAMS] [--ignore_pad_token_for_loss [IGNORE_PAD_TOKEN_FOR_LOSS]] [--no_ignore_pad_token_for_loss] [--val_size VAL_SIZE] [--packing PACKING] [--neat_packing [NEAT_PACKING]] [--tool_format TOOL_FORMAT] [--tokenized_path TOKENIZED_PATH] --output_dir OUTPUT_DIR [--overwrite_output_dir [OVERWRITE_OUTPUT_DIR]] [--do_train [DO_TRAIN]] [--do_eval [DO_EVAL]] [--do_predict [DO_PREDICT]] [--eval_strategy {no,steps,epoch}] [--prediction_loss_only [PREDICTION_LOSS_ONLY]] [--per_device_train_batch_size PER_DEVICE_TRAIN_BATCH_SIZE] [--per_device_eval_batch_size PER_DEVICE_EVAL_BATCH_SIZE] [--per_gpu_train_batch_size PER_GPU_TRAIN_BATCH_SIZE] [--per_gpu_eval_batch_size PER_GPU_EVAL_BATCH_SIZE] [--gradient_accumulation_steps GRADIENT_ACCUMULATION_STEPS] [--eval_accumulation_steps EVAL_ACCUMULATION_STEPS] [--eval_delay EVAL_DELAY] [--torch_empty_cache_steps TORCH_EMPTY_CACHE_STEPS] [--learning_rate LEARNING_RATE] [--weight_decay WEIGHT_DECAY] [--adam_beta1 ADAM_BETA1] [--adam_beta2 ADAM_BETA2] [--adam_epsilon ADAM_EPSILON] [--max_grad_norm MAX_GRAD_NORM] [--num_train_epochs NUM_TRAIN_EPOCHS] [--max_steps MAX_STEPS] [--lr_scheduler_type {linear,cosine,cosine_with_restarts,polynomial,constant,constant_with_warmup,inverse_sqrt,reduce_lr_on_plateau,cosine_with_min_lr,warmup_stable_decay}] [--lr_scheduler_kwargs LR_SCHEDULER_KWARGS] [--warmup_ratio WARMUP_RATIO] [--warmup_steps WARMUP_STEPS] [--log_level {detail,debug,info,warning,error,critical,passive}] [--log_level_replica {detail,debug,info,warning,error,critical,passive}] [--log_on_each_node [LOG_ON_EACH_NODE]] [--no_log_on_each_node] [--logging_dir LOGGING_DIR] [--logging_strategy {no,steps,epoch}] [--logging_first_step [LOGGING_FIRST_STEP]] [--logging_steps LOGGING_STEPS] [--logging_nan_inf_filter [LOGGING_NAN_INF_FILTER]] [--no_logging_nan_inf_filter] [--save_strategy {no,steps,epoch}] [--save_steps SAVE_STEPS] [--save_total_limit SAVE_TOTAL_LIMIT] [--save_safetensors [SAVE_SAFETENSORS]] [--no_save_safetensors] [--save_on_each_node [SAVE_ON_EACH_NODE]] [--save_only_model [SAVE_ONLY_MODEL]] [--restore_callback_states_from_checkpoint [RESTORE_CALLBACK_STATES_FROM_CHECKPOINT]] [--no_cuda [NO_CUDA]] [--use_cpu [USE_CPU]] [--use_mps_device [USE_MPS_DEVICE]] [--seed SEED] [--data_seed DATA_SEED] [--jit_mode_eval [JIT_MODE_EVAL]] [--use_ipex [USE_IPEX]] [--bf16 [BF16]] [--fp16 [FP16]] [--fp16_opt_level FP16_OPT_LEVEL] [--half_precision_backend {auto,apex,cpu_amp}] [--bf16_full_eval [BF16_FULL_EVAL]] [--fp16_full_eval [FP16_FULL_EVAL]] [--tf32 TF32] [--local_rank LOCAL_RANK] [--ddp_backend {nccl,gloo,mpi,ccl,hccl,cncl}] [--tpu_num_cores TPU_NUM_CORES] [--tpu_metrics_debug [TPU_METRICS_DEBUG]] [--debug DEBUG [DEBUG ...]] [--dataloader_drop_last [DATALOADER_DROP_LAST]] [--eval_steps EVAL_STEPS] [--dataloader_num_workers DATALOADER_NUM_WORKERS] [--dataloader_prefetch_factor DATALOADER_PREFETCH_FACTOR] [--past_index PAST_INDEX] [--run_name RUN_NAME] [--disable_tqdm DISABLE_TQDM] [--remove_unused_columns [REMOVE_UNUSED_COLUMNS]] [--no_remove_unused_columns] [--label_names LABEL_NAMES [LABEL_NAMES ...]] [--load_best_model_at_end [LOAD_BEST_MODEL_AT_END]] [--metric_for_best_model METRIC_FOR_BEST_MODEL] [--greater_is_better GREATER_IS_BETTER] [--ignore_data_skip [IGNORE_DATA_SKIP]] [--fsdp FSDP] [--fsdp_min_num_params FSDP_MIN_NUM_PARAMS] [--fsdp_config FSDP_CONFIG] [--fsdp_transformer_layer_cls_to_wrap FSDP_TRANSFORMER_LAYER_CLS_TO_WRAP] [--accelerator_config ACCELERATOR_CONFIG] [--deepspeed DEEPSPEED] [--label_smoothing_factor LABEL_SMOOTHING_FACTOR] [--optim {adamw_hf,adamw_torch,adamw_torch_fused,adamw_torch_xla,adamw_torch_npu_fused,adamw_apex_fused,adafactor,adamw_anyprecision,sgd,adagrad,adamw_bnb_8bit,adamw_8bit,lion_8bit,lion_32bit,paged_adamw_32bit,paged_adamw_8bit,paged_lion_32bit,paged_lion_8bit,rmsprop,rmsprop_bnb,rmsprop_bnb_8bit,rmsprop_bnb_32bit,galore_adamw,galore_adamw_8bit,galore_adafactor,galore_adamw_layerwise,galore_adamw_8bit_layerwise,galore_adafactor_layerwise,lomo,adalomo}] [--optim_args OPTIM_ARGS] [--adafactor [ADAFACTOR]] [--group_by_length [GROUP_BY_LENGTH]] [--length_column_name LENGTH_COLUMN_NAME] [--report_to REPORT_TO] [--ddp_find_unused_parameters DDP_FIND_UNUSED_PARAMETERS] [--ddp_bucket_cap_mb DDP_BUCKET_CAP_MB] [--ddp_broadcast_buffers DDP_BROADCAST_BUFFERS] [--dataloader_pin_memory [DATALOADER_PIN_MEMORY]] [--no_dataloader_pin_memory] [--dataloader_persistent_workers [DATALOADER_PERSISTENT_WORKERS]] [--skip_memory_metrics [SKIP_MEMORY_METRICS]] [--no_skip_memory_metrics] [--use_legacy_prediction_loop [USE_LEGACY_PREDICTION_LOOP]] [--push_to_hub [PUSH_TO_HUB]] [--resume_from_checkpoint RESUME_FROM_CHECKPOINT] [--hub_model_id HUB_MODEL_ID] [--hub_strategy {end,every_save,checkpoint,all_checkpoints}] [--hub_token HUB_TOKEN] [--hub_private_repo [HUB_PRIVATE_REPO]] [--hub_always_push [HUB_ALWAYS_PUSH]] [--gradient_checkpointing [GRADIENT_CHECKPOINTING]] [--gradient_checkpointing_kwargs GRADIENT_CHECKPOINTING_KWARGS] [--include_inputs_for_metrics [INCLUDE_INPUTS_FOR_METRICS]] [--eval_do_concat_batches [EVAL_DO_CONCAT_BATCHES]] [--no_eval_do_concat_batches] [--fp16_backend {auto,apex,cpu_amp}] [--evaluation_strategy {no,steps,epoch}] [--push_to_hub_model_id PUSH_TO_HUB_MODEL_ID] [--push_to_hub_organization PUSH_TO_HUB_ORGANIZATION] [--push_to_hub_token PUSH_TO_HUB_TOKEN] [--mp_parameters MP_PARAMETERS] [--auto_find_batch_size [AUTO_FIND_BATCH_SIZE]] [--full_determinism [FULL_DETERMINISM]] [--torchdynamo TORCHDYNAMO] [--ray_scope RAY_SCOPE] [--ddp_timeout DDP_TIMEOUT] [--torch_compile [TORCH_COMPILE]] [--torch_compile_backend TORCH_COMPILE_BACKEND] [--torch_compile_mode TORCH_COMPILE_MODE] [--dispatch_batches DISPATCH_BATCHES] [--split_batches SPLIT_BATCHES] [--include_tokens_per_second [INCLUDE_TOKENS_PER_SECOND]] [--include_num_input_tokens_seen [INCLUDE_NUM_INPUT_TOKENS_SEEN]] [--neftune_noise_alpha NEFTUNE_NOISE_ALPHA] [--optim_target_modules OPTIM_TARGET_MODULES] [--batch_eval_metrics [BATCH_EVAL_METRICS]] [--eval_on_start [EVAL_ON_START]] [--eval_use_gather_object [EVAL_USE_GATHER_OBJECT]] [--sortish_sampler [SORTISH_SAMPLER]] [--predict_with_generate [PREDICT_WITH_GENERATE]] [--generation_max_length GENERATION_MAX_LENGTH] [--generation_num_beams GENERATION_NUM_BEAMS] [--generation_config GENERATION_CONFIG] [--use_badam [USE_BADAM]] [--badam_mode {layer,ratio}] [--badam_start_block BADAM_START_BLOCK] [--badam_switch_mode {ascending,descending,random,fixed}] [--badam_switch_interval BADAM_SWITCH_INTERVAL] [--badam_update_ratio BADAM_UPDATE_RATIO] [--badam_mask_mode {adjacent,scatter}] [--badam_verbose BADAM_VERBOSE] [--use_galore [USE_GALORE]] [--galore_target GALORE_TARGET] [--galore_rank GALORE_RANK] [--galore_update_interval GALORE_UPDATE_INTERVAL] [--galore_scale GALORE_SCALE] [--galore_proj_type {std,reverse_std,right,left,full}] [--galore_layerwise [GALORE_LAYERWISE]] [--pref_beta PREF_BETA] [--pref_ftx PREF_FTX] [--pref_loss {sigmoid,hinge,ipo,kto_pair,orpo,simpo}] [--dpo_label_smoothing DPO_LABEL_SMOOTHING] [--kto_chosen_weight KTO_CHOSEN_WEIGHT] [--kto_rejected_weight KTO_REJECTED_WEIGHT] [--simpo_gamma SIMPO_GAMMA] [--ppo_buffer_size PPO_BUFFER_SIZE] [--ppo_epochs PPO_EPOCHS] [--ppo_score_norm [PPO_SCORE_NORM]] [--ppo_target PPO_TARGET] [--ppo_whiten_rewards [PPO_WHITEN_REWARDS]] [--ref_model REF_MODEL] [--ref_model_adapters REF_MODEL_ADAPTERS] [--ref_model_quantization_bit REF_MODEL_QUANTIZATION_BIT] [--reward_model REWARD_MODEL] [--reward_model_adapters REWARD_MODEL_ADAPTERS] [--reward_model_quantization_bit REWARD_MODEL_QUANTIZATION_BIT] [--reward_model_type {lora,full,api}] [--additional_target ADDITIONAL_TARGET] [--lora_alpha LORA_ALPHA] [--lora_dropout LORA_DROPOUT] [--lora_rank LORA_RANK] [--lora_target LORA_TARGET] [--loraplus_lr_ratio LORAPLUS_LR_RATIO] [--loraplus_lr_embedding LORAPLUS_LR_EMBEDDING] [--use_rslora [USE_RSLORA]] [--use_dora [USE_DORA]] [--pissa_init [PISSA_INIT]] [--pissa_iter PISSA_ITER] [--pissa_convert [PISSA_CONVERT]] [--create_new_adapter [CREATE_NEW_ADAPTER]] [--freeze_trainable_layers FREEZE_TRAINABLE_LAYERS] [--freeze_trainable_modules FREEZE_TRAINABLE_MODULES] [--freeze_extra_modules FREEZE_EXTRA_MODULES] [--pure_bf16 [PURE_BF16]] [--stage {pt,sft,rm,ppo,dpo,kto}] [--finetuning_type {lora,freeze,full}] [--use_llama_pro [USE_LLAMA_PRO]] [--use_adam_mini [USE_ADAM_MINI]] [--freeze_vision_tower [FREEZE_VISION_TOWER]] [--no_freeze_vision_tower] [--train_mm_proj_only [TRAIN_MM_PROJ_ONLY]] [--compute_accuracy [COMPUTE_ACCURACY]] [--plot_loss [PLOT_LOSS]] [--do_sample [DO_SAMPLE]] [--no_do_sample] [--temperature TEMPERATURE] [--top_p TOP_P] [--top_k TOP_K] [--num_beams NUM_BEAMS] [--max_length MAX_LENGTH] [--max_new_tokens MAX_NEW_TOKENS] [--repetition_penalty REPETITION_PENALTY] [--length_penalty LENGTH_PENALTY] [--default_system DEFAULT_SYSTEM] optional arguments: -h, --help show this help message and exit --model_name_or_path MODEL_NAME_OR_PATH Path to the model weight or identifier from huggingface.co/models or modelscope.cn/models. (default: None) --adapter_name_or_path ADAPTER_NAME_OR_PATH Path to the adapter weight or identifier from huggingface.co/models. Use commas to separate multiple adapters. (default: None) --adapter_folder ADAPTER_FOLDER The folder containing the adapter weights to load. (default: None) --cache_dir CACHE_DIR Where to store the pre-trained models downloaded from huggingface.co or modelscope.cn. (default: None) --use_fast_tokenizer [USE_FAST_TOKENIZER] Whether or not to use one of the fast tokenizer (backed by the tokenizers library). (default: True) --no_use_fast_tokenizer Whether or not to use one of the fast tokenizer (backed by the tokenizers library). (default: False) --resize_vocab [RESIZE_VOCAB] Whether or not to resize the tokenizer vocab and the embedding layers. (default: False) --split_special_tokens [SPLIT_SPECIAL_TOKENS] Whether or not the special tokens should be split during the tokenization process. (default: False) --new_special_tokens NEW_SPECIAL_TOKENS Special tokens to be added into the tokenizer. Use commas to separate multiple tokens. (default: None) --model_revision MODEL_REVISION The specific model version to use (can be a branch name, tag name or commit id). (default: main) --low_cpu_mem_usage [LOW_CPU_MEM_USAGE] Whether or not to use memory-efficient model loading. (default: True) --no_low_cpu_mem_usage Whether or not to use memory-efficient model loading. (default: False) --quantization_method {bitsandbytes,hqq,eetq} Quantization method to use for on-the-fly quantization. (default: bitsandbytes) --quantization_bit QUANTIZATION_BIT The number of bits to quantize the model using bitsandbytes. (default: None) --quantization_type {fp4,nf4} Quantization data type to use in int4 training. (default: nf4) --double_quantization [DOUBLE_QUANTIZATION] Whether or not to use double quantization in int4 training. (default: True) --no_double_quantization Whether or not to use double quantization in int4 training. (default: False) --quantization_device_map {auto} Device map used to infer the 4-bit quantized model, needs bitsandbytes>=0.43.0. (default: None) --rope_scaling {linear,dynamic} Which scaling strategy should be adopted for the RoPE embeddings. (default: None) --flash_attn {auto,disabled,sdpa,fa2} Enable FlashAttention for faster training and inference. (default: auto) --shift_attn [SHIFT_ATTN] Enable shift short attention (S^2-Attn) proposed by LongLoRA. (default: False) --mixture_of_depths {convert,load} Convert the model to mixture-of-depths (MoD) or load the MoD model. (default: None) --use_unsloth [USE_UNSLOTH] Whether or not to use unsloth's optimization for the LoRA training. (default: False) --visual_inputs [VISUAL_INPUTS] Whethor or not to use multimodal LLM that accepts visual inputs. (default: False) --moe_aux_loss_coef MOE_AUX_LOSS_COEF Coefficient of the auxiliary router loss in mixture- of-experts model. (default: None) --disable_gradient_checkpointing [DISABLE_GRADIENT_CHECKPOINTING] Whether or not to disable gradient checkpointing. (default: False) --upcast_layernorm [UPCAST_LAYERNORM] Whether or not to upcast the layernorm weights in fp32. (default: False) --upcast_lmhead_output [UPCAST_LMHEAD_OUTPUT] Whether or not to upcast the output of lm_head in fp32. (default: False) --train_from_scratch [TRAIN_FROM_SCRATCH] Whether or not to randomly initialize the model weights. (default: False) --infer_backend {huggingface,vllm} Backend engine used at inference. (default: huggingface) --vllm_maxlen VLLM_MAXLEN Maximum sequence (prompt + response) length of the vLLM engine. (default: 2048) --vllm_gpu_util VLLM_GPU_UTIL The fraction of GPU memory in (0,1) to be used for the vLLM engine. (default: 0.9) --vllm_enforce_eager [VLLM_ENFORCE_EAGER] Whether or not to disable CUDA graph in the vLLM engine. (default: False) --vllm_max_lora_rank VLLM_MAX_LORA_RANK Maximum rank of all LoRAs in the vLLM engine. (default: 32) --offload_folder OFFLOAD_FOLDER Path to offload model weights. (default: offload) --use_cache [USE_CACHE] Whether or not to use KV cache in generation. (default: True) --no_use_cache Whether or not to use KV cache in generation. (default: False) --infer_dtype {auto,float16,bfloat16,float32} Data type for model weights and activations at inference. (default: auto) --hf_hub_token HF_HUB_TOKEN Auth token to log in with Hugging Face Hub. (default: None) --ms_hub_token MS_HUB_TOKEN Auth token to log in with ModelScope Hub. (default: None) --export_dir EXPORT_DIR Path to the directory to save the exported model. (default: None) --export_size EXPORT_SIZE The file shard size (in GB) of the exported model. (default: 1) --export_device {cpu,auto} The device used in model export, use `auto` to accelerate exporting. (default: cpu) --export_quantization_bit EXPORT_QUANTIZATION_BIT The number of bits to quantize the exported model. (default: None) --export_quantization_dataset EXPORT_QUANTIZATION_DATASET Path to the dataset or dataset name to use in quantizing the exported model. (default: None) --export_quantization_nsamples EXPORT_QUANTIZATION_NSAMPLES The number of samples used for quantization. (default: 128) --export_quantization_maxlen EXPORT_QUANTIZATION_MAXLEN The maximum length of the model inputs used for quantization. (default: 1024) --export_legacy_format [EXPORT_LEGACY_FORMAT] Whether or not to save the `.bin` files instead of `.safetensors`. (default: False) --export_hub_model_id EXPORT_HUB_MODEL_ID The name of the repository if push the model to the Hugging Face hub. (default: None) --print_param_status [PRINT_PARAM_STATUS] For debugging purposes, print the status of the parameters in the model. (default: False) --template TEMPLATE Which template to use for constructing prompts in training and inference. (default: None) --dataset DATASET The name of dataset(s) to use for training. Use commas to separate multiple datasets. (default: None) --eval_dataset EVAL_DATASET The name of dataset(s) to use for evaluation. Use commas to separate multiple datasets. (default: None) --dataset_dir DATASET_DIR Path to the folder containing the datasets. (default: data) --cutoff_len CUTOFF_LEN The cutoff length of the tokenized inputs in the dataset. (default: 1024) --train_on_prompt [TRAIN_ON_PROMPT] Whether or not to disable the mask on the prompt. (default: False) --mask_history [MASK_HISTORY] Whether or not to mask the history and train on the last turn only. (default: False) --streaming [STREAMING] Enable dataset streaming. (default: False) --buffer_size BUFFER_SIZE Size of the buffer to randomly sample examples from in dataset streaming. (default: 16384) --mix_strategy {concat,interleave_under,interleave_over} Strategy to use in dataset mixing (concat/interleave) (undersampling/oversampling). (default: concat) --interleave_probs INTERLEAVE_PROBS Probabilities to sample data from datasets. Use commas to separate multiple datasets. (default: None) --overwrite_cache [OVERWRITE_CACHE] Overwrite the cached training and evaluation sets. (default: False) --preprocessing_num_workers PREPROCESSING_NUM_WORKERS The number of processes to use for the pre-processing. (default: None) --max_samples MAX_SAMPLES For debugging purposes, truncate the number of examples for each dataset. (default: None) --eval_num_beams EVAL_NUM_BEAMS Number of beams to use for evaluation. This argument will be passed to `model.generate` (default: None) --ignore_pad_token_for_loss [IGNORE_PAD_TOKEN_FOR_LOSS] Whether or not to ignore the tokens corresponding to the pad label in loss computation. (default: True) --no_ignore_pad_token_for_loss Whether or not to ignore the tokens corresponding to the pad label in loss computation. (default: False) --val_size VAL_SIZE Size of the development set, should be an integer or a float in range `[0,1)`. (default: 0.0) --packing PACKING Enable sequences packing in training. Will automatically enable in pre-training. (default: None) --neat_packing [NEAT_PACKING] Enable sequence packing without cross-attention. (default: False) --tool_format TOOL_FORMAT Tool format to use for constructing function calling examples. (default: None) --tokenized_path TOKENIZED_PATH Path to save or load the tokenized datasets. (default: None) --output_dir OUTPUT_DIR The output directory where the model predictions and checkpoints will be written. (default: None) --overwrite_output_dir [OVERWRITE_OUTPUT_DIR] Overwrite the content of the output directory. Use this to continue training if output_dir points to a checkpoint directory. (default: False) --do_train [DO_TRAIN] Whether to run training. (default: False) --do_eval [DO_EVAL] Whether to run eval on the dev set. (default: False) --do_predict [DO_PREDICT] Whether to run predictions on the test set. (default: False) --eval_strategy {no,steps,epoch} The evaluation strategy to use. (default: no) --prediction_loss_only [PREDICTION_LOSS_ONLY] When performing evaluation and predictions, only returns the loss. (default: False) --per_device_train_batch_size PER_DEVICE_TRAIN_BATCH_SIZE Batch size per GPU/TPU/MPS/NPU core/CPU for training. (default: 8) --per_device_eval_batch_size PER_DEVICE_EVAL_BATCH_SIZE Batch size per GPU/TPU/MPS/NPU core/CPU for evaluation. (default: 8) --per_gpu_train_batch_size PER_GPU_TRAIN_BATCH_SIZE Deprecated, the use of `--per_device_train_batch_size` is preferred. Batch size per GPU/TPU core/CPU for training. (default: None) --per_gpu_eval_batch_size PER_GPU_EVAL_BATCH_SIZE Deprecated, the use of `--per_device_eval_batch_size` is preferred. Batch size per GPU/TPU core/CPU for evaluation. (default: None) --gradient_accumulation_steps GRADIENT_ACCUMULATION_STEPS Number of updates steps to accumulate before performing a backward/update pass. (default: 1) --eval_accumulation_steps EVAL_ACCUMULATION_STEPS Number of predictions steps to accumulate before moving the tensors to the CPU. (default: None) --eval_delay EVAL_DELAY Number of epochs or steps to wait for before the first evaluation can be performed, depending on the eval_strategy. (default: 0) --torch_empty_cache_steps TORCH_EMPTY_CACHE_STEPS Number of steps to wait before calling `torch..empty_cache()`.This can help avoid CUDA out-of-memory errors by lowering peak VRAM usage at a cost of about [10{'option_strings': ['-- torch_empty_cache_steps'], 'dest': 'torch_empty_cache_steps', 'nargs': None, 'const': None, 'default': None, 'type': 'int', 'choices': None, 'required': False, 'help': 'Number of steps to wait before calling `torch..empty_cache()`.This can help avoid CUDA out-of-memory errors by lowering peak VRAM usage at a cost of about [10% slower performance] (https://github.com/huggingface/transformers/issues/31 372).If left unset or set to None, cache will not be emptied.', 'metavar': None, 'container': , 'prog': 'launcher.py'}lower performance](https://githu b.com/huggingface/transformers/issues/31372).If left unset or set to None, cache will not be emptied. (default: None) --learning_rate LEARNING_RATE The initial learning rate for AdamW. (default: 5e-05) --weight_decay WEIGHT_DECAY Weight decay for AdamW if we apply some. (default: 0.0) --adam_beta1 ADAM_BETA1 Beta1 for AdamW optimizer (default: 0.9) --adam_beta2 ADAM_BETA2 Beta2 for AdamW optimizer (default: 0.999) --adam_epsilon ADAM_EPSILON Epsilon for AdamW optimizer. (default: 1e-08) --max_grad_norm MAX_GRAD_NORM Max gradient norm. (default: 1.0) --num_train_epochs NUM_TRAIN_EPOCHS Total number of training epochs to perform. (default: 3.0) --max_steps MAX_STEPS If > 0: set total number of training steps to perform. Override num_train_epochs. (default: -1) --lr_scheduler_type {linear,cosine,cosine_with_restarts,polynomial,constant,constant_with_warmup,inverse_sqrt,reduce_lr_on_plateau,cosine_with_min_lr,warmup_stable_decay} The scheduler type to use. (default: linear) --lr_scheduler_kwargs LR_SCHEDULER_KWARGS Extra parameters for the lr_scheduler such as {'num_cycles': 1} for the cosine with hard restarts. (default: {}) --warmup_ratio WARMUP_RATIO Linear warmup over warmup_ratio fraction of total steps. (default: 0.0) --warmup_steps WARMUP_STEPS Linear warmup over warmup_steps. (default: 0) --log_level {detail,debug,info,warning,error,critical,passive} Logger log level to use on the main node. Possible choices are the log levels as strings: 'debug', 'info', 'warning', 'error' and 'critical', plus a 'passive' level which doesn't set anything and lets the application set the level. Defaults to 'passive'. (default: passive) --log_level_replica {detail,debug,info,warning,error,critical,passive} Logger log level to use on replica nodes. Same choices and defaults as ``log_level`` (default: warning) --log_on_each_node [LOG_ON_EACH_NODE] When doing a multinode distributed training, whether to log once per node or just once on the main node. (default: True) --no_log_on_each_node When doing a multinode distributed training, whether to log once per node or just once on the main node. (default: False) --logging_dir LOGGING_DIR Tensorboard log dir. (default: None) --logging_strategy {no,steps,epoch} The logging strategy to use. (default: steps) --logging_first_step [LOGGING_FIRST_STEP] Log the first global_step (default: False) --logging_steps LOGGING_STEPS Log every X updates steps. Should be an integer or a float in range `[0,1)`. If smaller than 1, will be interpreted as ratio of total training steps. (default: 500) --logging_nan_inf_filter [LOGGING_NAN_INF_FILTER] Filter nan and inf losses for logging. (default: True) --no_logging_nan_inf_filter Filter nan and inf losses for logging. (default: False) --save_strategy {no,steps,epoch} The checkpoint save strategy to use. (default: steps) --save_steps SAVE_STEPS Save checkpoint every X updates steps. Should be an integer or a float in range `[0,1)`. If smaller than 1, will be interpreted as ratio of total training steps. (default: 500) --save_total_limit SAVE_TOTAL_LIMIT If a value is passed, will limit the total amount of checkpoints. Deletes the older checkpoints in `output_dir`. When `load_best_model_at_end` is enabled, the 'best' checkpoint according to `metric_for_best_model` will always be retained in addition to the most recent ones. For example, for `save_total_limit=5` and `load_best_model_at_end=True`, the four last checkpoints will always be retained alongside the best model. When `save_total_limit=1` and `load_best_model_at_end=True`, it is possible that two checkpoints are saved: the last one and the best one (if they are different). Default is unlimited checkpoints (default: None) --save_safetensors [SAVE_SAFETENSORS] Use safetensors saving and loading for state dicts instead of default torch.load and torch.save. (default: True) --no_save_safetensors Use safetensors saving and loading for state dicts instead of default torch.load and torch.save. (default: False) --save_on_each_node [SAVE_ON_EACH_NODE] When doing multi-node distributed training, whether to save models and checkpoints on each node, or only on the main one (default: False) --save_only_model [SAVE_ONLY_MODEL] When checkpointing, whether to only save the model, or also the optimizer, scheduler & rng state.Note that when this is true, you won't be able to resume training from checkpoint.This enables you to save storage by not storing the optimizer, scheduler & rng state.You can only load the model using from_pretrained with this option set to True. (default: False) --restore_callback_states_from_checkpoint [RESTORE_CALLBACK_STATES_FROM_CHECKPOINT] Whether to restore the callback states from the checkpoint. If `True`, will override callbacks passed to the `Trainer` if they exist in the checkpoint. (default: False) --no_cuda [NO_CUDA] This argument is deprecated. It will be removed in version 5.0 of 🤗 Transformers. (default: False) --use_cpu [USE_CPU] Whether or not to use cpu. If set to False, we will use cuda/tpu/mps/npu device if available. (default: False) --use_mps_device [USE_MPS_DEVICE] This argument is deprecated. `mps` device will be used if available similar to `cuda` device. It will be removed in version 5.0 of 🤗 Transformers (default: False) --seed SEED Random seed that will be set at the beginning of training. (default: 42) --data_seed DATA_SEED Random seed to be used with data samplers. (default: None) --jit_mode_eval [JIT_MODE_EVAL] Whether or not to use PyTorch jit trace for inference (default: False) --use_ipex [USE_IPEX] Use Intel extension for PyTorch when it is available, installation: 'https://github.com/intel/intel- extension-for-pytorch' (default: False) --bf16 [BF16] Whether to use bf16 (mixed) precision instead of 32-bit. Requires Ampere or higher NVIDIA architecture or using CPU (use_cpu) or Ascend NPU. This is an experimental API and it may change. (default: False) --fp16 [FP16] Whether to use fp16 (mixed) precision instead of 32-bit (default: False) --fp16_opt_level FP16_OPT_LEVEL For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']. See details at https://nvidia.github.io/apex/amp.html (default: O1) --half_precision_backend {auto,apex,cpu_amp} The backend to be used for half precision. (default: auto) --bf16_full_eval [BF16_FULL_EVAL] Whether to use full bfloat16 evaluation instead of 32-bit. This is an experimental API and it may change. (default: False) --fp16_full_eval [FP16_FULL_EVAL] Whether to use full float16 evaluation instead of 32-bit (default: False) --tf32 TF32 Whether to enable tf32 mode, available in Ampere and newer GPU architectures. This is an experimental API and it may change. (default: None) --local_rank LOCAL_RANK For distributed training: local_rank (default: -1) --ddp_backend {nccl,gloo,mpi,ccl,hccl,cncl} The backend to be used for distributed training (default: None) --tpu_num_cores TPU_NUM_CORES TPU: Number of TPU cores (automatically passed by launcher script) (default: None) --tpu_metrics_debug [TPU_METRICS_DEBUG] Deprecated, the use of `--debug tpu_metrics_debug` is preferred. TPU: Whether to print debug metrics (default: False) --debug DEBUG [DEBUG ...] Whether or not to enable debug mode. Current options: `underflow_overflow` (Detect underflow and overflow in activations and weights), `tpu_metrics_debug` (print debug metrics on TPU). (default: None) --dataloader_drop_last [DATALOADER_DROP_LAST] Drop the last incomplete batch if it is not divisible by the batch size. (default: False) --eval_steps EVAL_STEPS Run an evaluation every X steps. Should be an integer or a float in range `[0,1)`. If smaller than 1, will be interpreted as ratio of total training steps. (default: None) --dataloader_num_workers DATALOADER_NUM_WORKERS Number of subprocesses to use for data loading (PyTorch only). 0 means that the data will be loaded in the main process. (default: 0) --dataloader_prefetch_factor DATALOADER_PREFETCH_FACTOR Number of batches loaded in advance by each worker. 2 means there will be a total of 2 * num_workers batches prefetched across all workers. Default is 2 for PyTorch < 2.0.0 and otherwise None. (default: None) --past_index PAST_INDEX If >=0, uses the corresponding part of the output as the past state for next step. (default: -1) --run_name RUN_NAME An optional descriptor for the run. Notably used for wandb, mlflow and comet logging. (default: None) --disable_tqdm DISABLE_TQDM Whether or not to disable the tqdm progress bars. (default: None) --remove_unused_columns [REMOVE_UNUSED_COLUMNS] Remove columns not required by the model when using an nlp.Dataset. (default: True) --no_remove_unused_columns Remove columns not required by the model when using an nlp.Dataset. (default: False) --label_names LABEL_NAMES [LABEL_NAMES ...] The list of keys in your dictionary of inputs that correspond to the labels. (default: None) --load_best_model_at_end [LOAD_BEST_MODEL_AT_END] Whether or not to load the best model found during training at the end of training. When this option is enabled, the best checkpoint will always be saved. See `save_total_limit` for more. (default: False) --metric_for_best_model METRIC_FOR_BEST_MODEL The metric to use to compare two different models. (default: None) --greater_is_better GREATER_IS_BETTER Whether the `metric_for_best_model` should be maximized or not. (default: None) --ignore_data_skip [IGNORE_DATA_SKIP] When resuming training, whether or not to skip the first epochs and batches to get to the same training data. (default: False) --fsdp FSDP Whether or not to use PyTorch Fully Sharded Data Parallel (FSDP) training (in distributed training only). The base option should be `full_shard`, `shard_grad_op` or `no_shard` and you can add CPU- offload to `full_shard` or `shard_grad_op` like this: full_shard offload` or `shard_grad_op offload`. You can add auto-wrap to `full_shard` or `shard_grad_op` with the same syntax: full_shard auto_wrap` or `shard_grad_op auto_wrap`. (default: ) --fsdp_min_num_params FSDP_MIN_NUM_PARAMS This parameter is deprecated. FSDP's minimum number of parameters for Default Auto Wrapping. (useful only when `fsdp` field is passed). (default: 0) --fsdp_config FSDP_CONFIG Config to be used with FSDP (Pytorch Fully Sharded Data Parallel). The value is either a fsdp json config file (e.g., `fsdp_config.json`) or an already loaded json file as `dict`. (default: None) --fsdp_transformer_layer_cls_to_wrap FSDP_TRANSFORMER_LAYER_CLS_TO_WRAP This parameter is deprecated. Transformer layer class name (case-sensitive) to wrap, e.g, `BertLayer`, `GPTJBlock`, `T5Block` .... (useful only when `fsdp` flag is passed). (default: None) --accelerator_config ACCELERATOR_CONFIG Config to be used with the internal Accelerator object initializtion. The value is either a accelerator json config file (e.g., `accelerator_config.json`) or an already loaded json file as `dict`. (default: None) --deepspeed DEEPSPEED Enable deepspeed and pass the path to deepspeed json config file (e.g. `ds_config.json`) or an already loaded json file as a dict (default: None) --label_smoothing_factor LABEL_SMOOTHING_FACTOR The label smoothing epsilon to apply (zero means no label smoothing). (default: 0.0) --optim {adamw_hf,adamw_torch,adamw_torch_fused,adamw_torch_xla,adamw_torch_npu_fused,adamw_apex_fused,adafactor,adamw_anyprecision,sgd,adagrad,adamw_bnb_8bit,adamw_8bit,lion_8bit,lion_32bit,paged_adamw_32bit,paged_adamw_8bit,paged_lion_32bit,paged_lion_8bit,rmsprop,rmsprop_bnb,rmsprop_bnb_8bit,rmsprop_bnb_32bit,galore_adamw,galore_adamw_8bit,galore_adafactor,galore_adamw_layerwise,galore_adamw_8bit_layerwise,galore_adafactor_layerwise,lomo,adalomo} The optimizer to use. (default: adamw_torch) --optim_args OPTIM_ARGS Optional arguments to supply to optimizer. (default: None) --adafactor [ADAFACTOR] Whether or not to replace AdamW by Adafactor. (default: False) --group_by_length [GROUP_BY_LENGTH] Whether or not to group samples of roughly the same length together when batching. (default: False) --length_column_name LENGTH_COLUMN_NAME Column name with precomputed lengths to use when grouping by length. (default: length) --report_to REPORT_TO The list of integrations to report the results and logs to. (default: None) --ddp_find_unused_parameters DDP_FIND_UNUSED_PARAMETERS When using distributed training, the value of the flag `find_unused_parameters` passed to `DistributedDataParallel`. (default: None) --ddp_bucket_cap_mb DDP_BUCKET_CAP_MB When using distributed training, the value of the flag `bucket_cap_mb` passed to `DistributedDataParallel`. (default: None) --ddp_broadcast_buffers DDP_BROADCAST_BUFFERS When using distributed training, the value of the flag `broadcast_buffers` passed to `DistributedDataParallel`. (default: None) --dataloader_pin_memory [DATALOADER_PIN_MEMORY] Whether or not to pin memory for DataLoader. (default: True) --no_dataloader_pin_memory Whether or not to pin memory for DataLoader. (default: False) --dataloader_persistent_workers [DATALOADER_PERSISTENT_WORKERS] If True, the data loader will not shut down the worker processes after a dataset has been consumed once. This allows to maintain the workers Dataset instances alive. Can potentially speed up training, but will increase RAM usage. (default: False) --skip_memory_metrics [SKIP_MEMORY_METRICS] Whether or not to skip adding of memory profiler reports to metrics. (default: True) --no_skip_memory_metrics Whether or not to skip adding of memory profiler reports to metrics. (default: False) --use_legacy_prediction_loop [USE_LEGACY_PREDICTION_LOOP] Whether or not to use the legacy prediction_loop in the Trainer. (default: False) --push_to_hub [PUSH_TO_HUB] Whether or not to upload the trained model to the model hub after training. (default: False) --resume_from_checkpoint RESUME_FROM_CHECKPOINT The path to a folder with a valid checkpoint for your model. (default: None) --hub_model_id HUB_MODEL_ID The name of the repository to keep in sync with the local `output_dir`. (default: None) --hub_strategy {end,every_save,checkpoint,all_checkpoints} The hub strategy to use when `--push_to_hub` is activated. (default: every_save) --hub_token HUB_TOKEN The token to use to push to the Model Hub. (default: None) --hub_private_repo [HUB_PRIVATE_REPO] Whether the model repository is private or not. (default: False) --hub_always_push [HUB_ALWAYS_PUSH] Unless `True`, the Trainer will skip pushes if the previous one wasn't finished yet. (default: False) --gradient_checkpointing [GRADIENT_CHECKPOINTING] If True, use gradient checkpointing to save memory at the expense of slower backward pass. (default: False) --gradient_checkpointing_kwargs GRADIENT_CHECKPOINTING_KWARGS Gradient checkpointing key word arguments such as `use_reentrant`. Will be passed to `torch.utils.checkpoint.checkpoint` through `model.gradient_checkpointing_enable`. (default: None) --include_inputs_for_metrics [INCLUDE_INPUTS_FOR_METRICS] Whether or not the inputs will be passed to the `compute_metrics` function. (default: False) --eval_do_concat_batches [EVAL_DO_CONCAT_BATCHES] Whether to recursively concat inputs/losses/labels/predictions across batches. If `False`, will instead store them as lists, with each batch kept separate. (default: True) --no_eval_do_concat_batches Whether to recursively concat inputs/losses/labels/predictions across batches. If `False`, will instead store them as lists, with each batch kept separate. (default: False) --fp16_backend {auto,apex,cpu_amp} Deprecated. Use half_precision_backend instead (default: auto) --evaluation_strategy {no,steps,epoch} Deprecated. Use `eval_strategy` instead (default: None) --push_to_hub_model_id PUSH_TO_HUB_MODEL_ID The name of the repository to which push the `Trainer`. (default: None) --push_to_hub_organization PUSH_TO_HUB_ORGANIZATION The name of the organization in with to which push the `Trainer`. (default: None) --push_to_hub_token PUSH_TO_HUB_TOKEN The token to use to push to the Model Hub. (default: None) --mp_parameters MP_PARAMETERS Used by the SageMaker launcher to send mp-specific args. Ignored in Trainer (default: ) --auto_find_batch_size [AUTO_FIND_BATCH_SIZE] Whether to automatically decrease the batch size in half and rerun the training loop again each time a CUDA Out-of-Memory was reached (default: False) --full_determinism [FULL_DETERMINISM] Whether to call enable_full_determinism instead of set_seed for reproducibility in distributed training. Important: this will negatively impact the performance, so only use it for debugging. (default: False) --torchdynamo TORCHDYNAMO This argument is deprecated, use `--torch_compile_backend` instead. (default: None) --ray_scope RAY_SCOPE The scope to use when doing hyperparameter search with Ray. By default, `"last"` will be used. Ray will then use the last checkpoint of all trials, compare those, and select the best one. However, other options are also available. See the Ray documentation (https://doc s.ray.io/en/latest/tune/api_docs/analysis.html#ray.tun e.ExperimentAnalysis.get_best_trial) for more options. (default: last) --ddp_timeout DDP_TIMEOUT Overrides the default timeout for distributed training (value should be given in seconds). (default: 1800) --torch_compile [TORCH_COMPILE] If set to `True`, the model will be wrapped in `torch.compile`. (default: False) --torch_compile_backend TORCH_COMPILE_BACKEND Which backend to use with `torch.compile`, passing one will trigger a model compilation. (default: None) --torch_compile_mode TORCH_COMPILE_MODE Which mode to use with `torch.compile`, passing one will trigger a model compilation. (default: None) --dispatch_batches DISPATCH_BATCHES Deprecated. Pass {'dispatch_batches':VALUE} to `accelerator_config`. (default: None) --split_batches SPLIT_BATCHES Deprecated. Pass {'split_batches':True} to `accelerator_config`. (default: None) --include_tokens_per_second [INCLUDE_TOKENS_PER_SECOND] If set to `True`, the speed metrics will include `tgs` (tokens per second per device). (default: False) --include_num_input_tokens_seen [INCLUDE_NUM_INPUT_TOKENS_SEEN] If set to `True`, will track the number of input tokens seen throughout training. (May be slower in distributed training) (default: False) --neftune_noise_alpha NEFTUNE_NOISE_ALPHA Activates neftune noise embeddings into the model. NEFTune has been proven to drastically improve model performances for instrcution fine-tuning. Check out the original paper here: https://arxiv.org/abs/2310.05914 and the original code here: https://github.com/neelsjain/NEFTune. Only supported for `PreTrainedModel` and `PeftModel` classes. (default: None) --optim_target_modules OPTIM_TARGET_MODULES Target modules for the optimizer defined in the `optim` argument. Only used for the GaLore optimizer at the moment. (default: None) --batch_eval_metrics [BATCH_EVAL_METRICS] Break eval metrics calculation into batches to save memory. (default: False) --eval_on_start [EVAL_ON_START] Whether to run through the entire `evaluation` step at the very beginning of training as a sanity check. (default: False) --eval_use_gather_object [EVAL_USE_GATHER_OBJECT] Whether to run recursively gather object in a nested list/tuple/dictionary of objects from all devices. (default: False) --sortish_sampler [SORTISH_SAMPLER] Whether to use SortishSampler or not. (default: False) --predict_with_generate [PREDICT_WITH_GENERATE] Whether to use generate to calculate generative metrics (ROUGE, BLEU). (default: False) --generation_max_length GENERATION_MAX_LENGTH The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default to the `max_length` value of the model configuration. (default: None) --generation_num_beams GENERATION_NUM_BEAMS The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default to the `num_beams` value of the model configuration. (default: None) --generation_config GENERATION_CONFIG Model id, file path or url pointing to a GenerationConfig json file, to use during prediction. (default: None) --use_badam [USE_BADAM] Whether or not to use the BAdam optimizer. (default: False) --badam_mode {layer,ratio} Whether to use layer-wise or ratio-wise BAdam optimizer. (default: layer) --badam_start_block BADAM_START_BLOCK The starting block index for layer-wise BAdam. (default: None) --badam_switch_mode {ascending,descending,random,fixed} the strategy of picking block to update for layer-wise BAdam. (default: ascending) --badam_switch_interval BADAM_SWITCH_INTERVAL Number of steps to update the block for layer-wise BAdam. Use -1 to disable the block update. (default: 50) --badam_update_ratio BADAM_UPDATE_RATIO The ratio of the update for ratio-wise BAdam. (default: 0.05) --badam_mask_mode {adjacent,scatter} The mode of the mask for BAdam optimizer. `adjacent` means that the trainable parameters are adjacent to each other, `scatter` means that trainable parameters are randomly choosed from the weight. (default: adjacent) --badam_verbose BADAM_VERBOSE The verbosity level of BAdam optimizer. 0 for no print, 1 for print the block prefix, 2 for print trainable parameters. (default: 0) --use_galore [USE_GALORE] Whether or not to use the gradient low-Rank projection (GaLore). (default: False) --galore_target GALORE_TARGET Name(s) of modules to apply GaLore. Use commas to separate multiple modules. Use `all` to specify all the linear modules. (default: all) --galore_rank GALORE_RANK The rank of GaLore gradients. (default: 16) --galore_update_interval GALORE_UPDATE_INTERVAL Number of steps to update the GaLore projection. (default: 200) --galore_scale GALORE_SCALE GaLore scaling coefficient. (default: 0.25) --galore_proj_type {std,reverse_std,right,left,full} Type of GaLore projection. (default: std) --galore_layerwise [GALORE_LAYERWISE] Whether or not to enable layer-wise update to further save memory. (default: False) --pref_beta PREF_BETA The beta parameter in the preference loss. (default: 0.1) --pref_ftx PREF_FTX The supervised fine-tuning loss coefficient in DPO training. (default: 0.0) --pref_loss {sigmoid,hinge,ipo,kto_pair,orpo,simpo} The type of DPO loss to use. (default: sigmoid) --dpo_label_smoothing DPO_LABEL_SMOOTHING The robust DPO label smoothing parameter in cDPO that should be between 0 and 0.5. (default: 0.0) --kto_chosen_weight KTO_CHOSEN_WEIGHT The weight factor of the desirable losses in KTO training. (default: 1.0) --kto_rejected_weight KTO_REJECTED_WEIGHT The weight factor of the undesirable losses in KTO training. (default: 1.0) --simpo_gamma SIMPO_GAMMA The target reward margin term in SimPO loss. (default: 0.5) --ppo_buffer_size PPO_BUFFER_SIZE The number of mini-batches to make experience buffer in a PPO optimization step. (default: 1) --ppo_epochs PPO_EPOCHS The number of epochs to perform in a PPO optimization step. (default: 4) --ppo_score_norm [PPO_SCORE_NORM] Use score normalization in PPO training. (default: False) --ppo_target PPO_TARGET Target KL value for adaptive KL control in PPO training. (default: 6.0) --ppo_whiten_rewards [PPO_WHITEN_REWARDS] Whiten the rewards before compute advantages in PPO training. (default: False) --ref_model REF_MODEL Path to the reference model used for the PPO or DPO training. (default: None) --ref_model_adapters REF_MODEL_ADAPTERS Path to the adapters of the reference model. (default: None) --ref_model_quantization_bit REF_MODEL_QUANTIZATION_BIT The number of bits to quantize the reference model. (default: None) --reward_model REWARD_MODEL Path to the reward model used for the PPO training. (default: None) --reward_model_adapters REWARD_MODEL_ADAPTERS Path to the adapters of the reward model. (default: None) --reward_model_quantization_bit REWARD_MODEL_QUANTIZATION_BIT The number of bits to quantize the reward model. (default: None) --reward_model_type {lora,full,api} The type of the reward model in PPO training. Lora model only supports lora training. (default: lora) --additional_target ADDITIONAL_TARGET Name(s) of modules apart from LoRA layers to be set as trainable and saved in the final checkpoint. Use commas to separate multiple modules. (default: None) --lora_alpha LORA_ALPHA The scale factor for LoRA fine-tuning (default: lora_rank * 2). (default: None) --lora_dropout LORA_DROPOUT Dropout rate for the LoRA fine-tuning. (default: 0.0) --lora_rank LORA_RANK The intrinsic dimension for LoRA fine-tuning. (default: 8) --lora_target LORA_TARGET Name(s) of target modules to apply LoRA. Use commas to separate multiple modules. Use `all` to specify all the linear modules. (default: all) --loraplus_lr_ratio LORAPLUS_LR_RATIO LoRA plus learning rate ratio (lr_B / lr_A). (default: None) --loraplus_lr_embedding LORAPLUS_LR_EMBEDDING LoRA plus learning rate for lora embedding layers. (default: 1e-06) --use_rslora [USE_RSLORA] Whether or not to use the rank stabilization scaling factor for LoRA layer. (default: False) --use_dora [USE_DORA] Whether or not to use the weight-decomposed lora method (DoRA). (default: False) --pissa_init [PISSA_INIT] Whether or not to initialize a PiSSA adapter. (default: False) --pissa_iter PISSA_ITER The number of iteration steps performed by FSVD in PiSSA. Use -1 to disable it. (default: 16) --pissa_convert [PISSA_CONVERT] Whether or not to convert the PiSSA adapter to a normal LoRA adapter. (default: False) --create_new_adapter [CREATE_NEW_ADAPTER] Whether or not to create a new adapter with randomly initialized weight. (default: False) --freeze_trainable_layers FREEZE_TRAINABLE_LAYERS The number of trainable layers for freeze (partial- parameter) fine-tuning. Positive numbers mean the last n layers are set as trainable, negative numbers mean the first n layers are set as trainable. (default: 2) --freeze_trainable_modules FREEZE_TRAINABLE_MODULES Name(s) of trainable modules for freeze (partial- parameter) fine-tuning. Use commas to separate multiple modules. Use `all` to specify all the available modules. (default: all) --freeze_extra_modules FREEZE_EXTRA_MODULES Name(s) of modules apart from hidden layers to be set as trainable for freeze (partial-parameter) fine- tuning. Use commas to separate multiple modules. (default: None) --pure_bf16 [PURE_BF16] Whether or not to train model in purely bf16 precision (without AMP). (default: False) --stage {pt,sft,rm,ppo,dpo,kto} Which stage will be performed in training. (default: sft) --finetuning_type {lora,freeze,full} Which fine-tuning method to use. (default: lora) --use_llama_pro [USE_LLAMA_PRO] Whether or not to make only the parameters in the expanded blocks trainable. (default: False) --use_adam_mini [USE_ADAM_MINI] Whether or not to use the Adam-mini optimizer. (default: False) --freeze_vision_tower [FREEZE_VISION_TOWER] Whether ot not to freeze vision tower in MLLM training. (default: True) --no_freeze_vision_tower Whether ot not to freeze vision tower in MLLM training. (default: False) --train_mm_proj_only [TRAIN_MM_PROJ_ONLY] Whether or not to train the multimodal projector for MLLM only. (default: False) --compute_accuracy [COMPUTE_ACCURACY] Whether or not to compute the token-level accuracy at evaluation. (default: False) --plot_loss [PLOT_LOSS] Whether or not to save the training loss curves. (default: False) --do_sample [DO_SAMPLE] Whether or not to use sampling, use greedy decoding otherwise. (default: True) --no_do_sample Whether or not to use sampling, use greedy decoding otherwise. (default: False) --temperature TEMPERATURE The value used to modulate the next token probabilities. (default: 0.95) --top_p TOP_P The smallest set of most probable tokens with probabilities that add up to top_p or higher are kept. (default: 0.7) --top_k TOP_K The number of highest probability vocabulary tokens to keep for top-k filtering. (default: 50) --num_beams NUM_BEAMS Number of beams for beam search. 1 means no beam search. (default: 1) --max_length MAX_LENGTH The maximum length the generated tokens can have. It can be overridden by max_new_tokens. (default: 1024) --max_new_tokens MAX_NEW_TOKENS The maximum numbers of tokens to generate, ignoring the number of tokens in the prompt. (default: 1024) --repetition_penalty REPETITION_PENALTY The parameter for repetition penalty. 1.0 means no penalty. (default: 1.0) --length_penalty LENGTH_PENALTY Exponential penalty to the length that is used with beam-based generation. (default: 1.0) --default_system DEFAULT_SYSTEM Default system message to use in chat completion. (default: None) usage: launcher.py [-h] --model_name_or_path MODEL_NAME_OR_PATH [--adapter_name_or_path ADAPTER_NAME_OR_PATH] [--adapter_folder ADAPTER_FOLDER] [--cache_dir CACHE_DIR] [--use_fast_tokenizer [USE_FAST_TOKENIZER]] [--no_use_fast_tokenizer] [--resize_vocab [RESIZE_VOCAB]] [--split_special_tokens [SPLIT_SPECIAL_TOKENS]] [--new_special_tokens NEW_SPECIAL_TOKENS] [--model_revision MODEL_REVISION] [--low_cpu_mem_usage [LOW_CPU_MEM_USAGE]] [--no_low_cpu_mem_usage] [--quantization_method {bitsandbytes,hqq,eetq}] [--quantization_bit QUANTIZATION_BIT] [--quantization_type {fp4,nf4}] [--double_quantization [DOUBLE_QUANTIZATION]] [--no_double_quantization] [--quantization_device_map {auto}] [--rope_scaling {linear,dynamic}] [--flash_attn {auto,disabled,sdpa,fa2}] [--shift_attn [SHIFT_ATTN]] [--mixture_of_depths {convert,load}] [--use_unsloth [USE_UNSLOTH]] [--visual_inputs [VISUAL_INPUTS]] [--moe_aux_loss_coef MOE_AUX_LOSS_COEF] [--disable_gradient_checkpointing [DISABLE_GRADIENT_CHECKPOINTING]] [--upcast_layernorm [UPCAST_LAYERNORM]] [--upcast_lmhead_output [UPCAST_LMHEAD_OUTPUT]] [--train_from_scratch [TRAIN_FROM_SCRATCH]] [--infer_backend {huggingface,vllm}] [--vllm_maxlen VLLM_MAXLEN] [--vllm_gpu_util VLLM_GPU_UTIL] [--vllm_enforce_eager [VLLM_ENFORCE_EAGER]] [--vllm_max_lora_rank VLLM_MAX_LORA_RANK] [--offload_folder OFFLOAD_FOLDER] [--use_cache [USE_CACHE]] [--no_use_cache] [--infer_dtype {auto,float16,bfloat16,float32}] [--hf_hub_token HF_HUB_TOKEN] [--ms_hub_token MS_HUB_TOKEN] [--export_dir EXPORT_DIR] [--export_size EXPORT_SIZE] [--export_device {cpu,auto}] [--export_quantization_bit EXPORT_QUANTIZATION_BIT] [--export_quantization_dataset EXPORT_QUANTIZATION_DATASET] [--export_quantization_nsamples EXPORT_QUANTIZATION_NSAMPLES] [--export_quantization_maxlen EXPORT_QUANTIZATION_MAXLEN] [--export_legacy_format [EXPORT_LEGACY_FORMAT]] [--export_hub_model_id EXPORT_HUB_MODEL_ID] [--print_param_status [PRINT_PARAM_STATUS]] [--template TEMPLATE] [--dataset DATASET] [--eval_dataset EVAL_DATASET] [--dataset_dir DATASET_DIR] [--cutoff_len CUTOFF_LEN] [--train_on_prompt [TRAIN_ON_PROMPT]] [--mask_history [MASK_HISTORY]] [--streaming [STREAMING]] [--buffer_size BUFFER_SIZE] [--mix_strategy {concat,interleave_under,interleave_over}] [--interleave_probs INTERLEAVE_PROBS] [--overwrite_cache [OVERWRITE_CACHE]] [--preprocessing_num_workers PREPROCESSING_NUM_WORKERS] [--max_samples MAX_SAMPLES] [--eval_num_beams EVAL_NUM_BEAMS] [--ignore_pad_token_for_loss [IGNORE_PAD_TOKEN_FOR_LOSS]] [--no_ignore_pad_token_for_loss] [--val_size VAL_SIZE] [--packing PACKING] [--neat_packing [NEAT_PACKING]] [--tool_format TOOL_FORMAT] [--tokenized_path TOKENIZED_PATH] --output_dir OUTPUT_DIR [--overwrite_output_dir [OVERWRITE_OUTPUT_DIR]] [--do_train [DO_TRAIN]] [--do_eval [DO_EVAL]] [--do_predict [DO_PREDICT]] [--eval_strategy {no,steps,epoch}] [--prediction_loss_only [PREDICTION_LOSS_ONLY]] [--per_device_train_batch_size PER_DEVICE_TRAIN_BATCH_SIZE] [--per_device_eval_batch_size PER_DEVICE_EVAL_BATCH_SIZE] [--per_gpu_train_batch_size PER_GPU_TRAIN_BATCH_SIZE] [--per_gpu_eval_batch_size PER_GPU_EVAL_BATCH_SIZE] [--gradient_accumulation_steps GRADIENT_ACCUMULATION_STEPS] [--eval_accumulation_steps EVAL_ACCUMULATION_STEPS] [--eval_delay EVAL_DELAY] [--torch_empty_cache_steps TORCH_EMPTY_CACHE_STEPS] [--learning_rate LEARNING_RATE] [--weight_decay WEIGHT_DECAY] [--adam_beta1 ADAM_BETA1] [--adam_beta2 ADAM_BETA2] [--adam_epsilon ADAM_EPSILON] [--max_grad_norm MAX_GRAD_NORM] [--num_train_epochs NUM_TRAIN_EPOCHS] [--max_steps MAX_STEPS] [--lr_scheduler_type {linear,cosine,cosine_with_restarts,polynomial,constant,constant_with_warmup,inverse_sqrt,reduce_lr_on_plateau,cosine_with_min_lr,warmup_stable_decay}] [--lr_scheduler_kwargs LR_SCHEDULER_KWARGS] [--warmup_ratio WARMUP_RATIO] [--warmup_steps WARMUP_STEPS] [--log_level {detail,debug,info,warning,error,critical,passive}] [--log_level_replica {detail,debug,info,warning,error,critical,passive}] [--log_on_each_node [LOG_ON_EACH_NODE]] [--no_log_on_each_node] [--logging_dir LOGGING_DIR] [--logging_strategy {no,steps,epoch}] [--logging_first_step [LOGGING_FIRST_STEP]] [--logging_steps LOGGING_STEPS] [--logging_nan_inf_filter [LOGGING_NAN_INF_FILTER]] [--no_logging_nan_inf_filter] [--save_strategy {no,steps,epoch}] [--save_steps SAVE_STEPS] [--save_total_limit SAVE_TOTAL_LIMIT] [--save_safetensors [SAVE_SAFETENSORS]] [--no_save_safetensors] [--save_on_each_node [SAVE_ON_EACH_NODE]] [--save_only_model [SAVE_ONLY_MODEL]] [--restore_callback_states_from_checkpoint [RESTORE_CALLBACK_STATES_FROM_CHECKPOINT]] [--no_cuda [NO_CUDA]] [--use_cpu [USE_CPU]] [--use_mps_device [USE_MPS_DEVICE]] [--seed SEED] [--data_seed DATA_SEED] [--jit_mode_eval [JIT_MODE_EVAL]] [--use_ipex [USE_IPEX]] [--bf16 [BF16]] [--fp16 [FP16]] [--fp16_opt_level FP16_OPT_LEVEL] [--half_precision_backend {auto,apex,cpu_amp}] [--bf16_full_eval [BF16_FULL_EVAL]] [--fp16_full_eval [FP16_FULL_EVAL]] [--tf32 TF32] [--local_rank LOCAL_RANK] [--ddp_backend {nccl,gloo,mpi,ccl,hccl,cncl}] [--tpu_num_cores TPU_NUM_CORES] [--tpu_metrics_debug [TPU_METRICS_DEBUG]] [--debug DEBUG [DEBUG ...]] [--dataloader_drop_last [DATALOADER_DROP_LAST]] [--eval_steps EVAL_STEPS] [--dataloader_num_workers DATALOADER_NUM_WORKERS] [--dataloader_prefetch_factor DATALOADER_PREFETCH_FACTOR] [--past_index PAST_INDEX] [--run_name RUN_NAME] [--disable_tqdm DISABLE_TQDM] [--remove_unused_columns [REMOVE_UNUSED_COLUMNS]] [--no_remove_unused_columns] [--label_names LABEL_NAMES [LABEL_NAMES ...]] [--load_best_model_at_end [LOAD_BEST_MODEL_AT_END]] [--metric_for_best_model METRIC_FOR_BEST_MODEL] [--greater_is_better GREATER_IS_BETTER] [--ignore_data_skip [IGNORE_DATA_SKIP]] [--fsdp FSDP] [--fsdp_min_num_params FSDP_MIN_NUM_PARAMS] [--fsdp_config FSDP_CONFIG] [--fsdp_transformer_layer_cls_to_wrap FSDP_TRANSFORMER_LAYER_CLS_TO_WRAP] [--accelerator_config ACCELERATOR_CONFIG] [--deepspeed DEEPSPEED] [--label_smoothing_factor LABEL_SMOOTHING_FACTOR] [--optim {adamw_hf,adamw_torch,adamw_torch_fused,adamw_torch_xla,adamw_torch_npu_fused,adamw_apex_fused,adafactor,adamw_anyprecision,sgd,adagrad,adamw_bnb_8bit,adamw_8bit,lion_8bit,lion_32bit,paged_adamw_32bit,paged_adamw_8bit,paged_lion_32bit,paged_lion_8bit,rmsprop,rmsprop_bnb,rmsprop_bnb_8bit,rmsprop_bnb_32bit,galore_adamw,galore_adamw_8bit,galore_adafactor,galore_adamw_layerwise,galore_adamw_8bit_layerwise,galore_adafactor_layerwise,lomo,adalomo}] [--optim_args OPTIM_ARGS] [--adafactor [ADAFACTOR]] [--group_by_length [GROUP_BY_LENGTH]] [--length_column_name LENGTH_COLUMN_NAME] [--report_to REPORT_TO] [--ddp_find_unused_parameters DDP_FIND_UNUSED_PARAMETERS] [--ddp_bucket_cap_mb DDP_BUCKET_CAP_MB] [--ddp_broadcast_buffers DDP_BROADCAST_BUFFERS] [--dataloader_pin_memory [DATALOADER_PIN_MEMORY]] [--no_dataloader_pin_memory] [--dataloader_persistent_workers [DATALOADER_PERSISTENT_WORKERS]] [--skip_memory_metrics [SKIP_MEMORY_METRICS]] [--no_skip_memory_metrics] [--use_legacy_prediction_loop [USE_LEGACY_PREDICTION_LOOP]] [--push_to_hub [PUSH_TO_HUB]] [--resume_from_checkpoint RESUME_FROM_CHECKPOINT] [--hub_model_id HUB_MODEL_ID] [--hub_strategy {end,every_save,checkpoint,all_checkpoints}] [--hub_token HUB_TOKEN] [--hub_private_repo [HUB_PRIVATE_REPO]] [--hub_always_push [HUB_ALWAYS_PUSH]] [--gradient_checkpointing [GRADIENT_CHECKPOINTING]] [--gradient_checkpointing_kwargs GRADIENT_CHECKPOINTING_KWARGS] [--include_inputs_for_metrics [INCLUDE_INPUTS_FOR_METRICS]] [--eval_do_concat_batches [EVAL_DO_CONCAT_BATCHES]] [--no_eval_do_concat_batches] [--fp16_backend {auto,apex,cpu_amp}] [--evaluation_strategy {no,steps,epoch}] [--push_to_hub_model_id PUSH_TO_HUB_MODEL_ID] [--push_to_hub_organization PUSH_TO_HUB_ORGANIZATION] [--push_to_hub_token PUSH_TO_HUB_TOKEN] [--mp_parameters MP_PARAMETERS] [--auto_find_batch_size [AUTO_FIND_BATCH_SIZE]] [--full_determinism [FULL_DETERMINISM]] [--torchdynamo TORCHDYNAMO] [--ray_scope RAY_SCOPE] [--ddp_timeout DDP_TIMEOUT] [--torch_compile [TORCH_COMPILE]] [--torch_compile_backend TORCH_COMPILE_BACKEND] [--torch_compile_mode TORCH_COMPILE_MODE] [--dispatch_batches DISPATCH_BATCHES] [--split_batches SPLIT_BATCHES] [--include_tokens_per_second [INCLUDE_TOKENS_PER_SECOND]] [--include_num_input_tokens_seen [INCLUDE_NUM_INPUT_TOKENS_SEEN]] [--neftune_noise_alpha NEFTUNE_NOISE_ALPHA] [--optim_target_modules OPTIM_TARGET_MODULES] [--batch_eval_metrics [BATCH_EVAL_METRICS]] [--eval_on_start [EVAL_ON_START]] [--eval_use_gather_object [EVAL_USE_GATHER_OBJECT]] [--sortish_sampler [SORTISH_SAMPLER]] [--predict_with_generate [PREDICT_WITH_GENERATE]] [--generation_max_length GENERATION_MAX_LENGTH] [--generation_num_beams GENERATION_NUM_BEAMS] [--generation_config GENERATION_CONFIG] [--use_badam [USE_BADAM]] [--badam_mode {layer,ratio}] [--badam_start_block BADAM_START_BLOCK] [--badam_switch_mode {ascending,descending,random,fixed}] [--badam_switch_interval BADAM_SWITCH_INTERVAL] [--badam_update_ratio BADAM_UPDATE_RATIO] [--badam_mask_mode {adjacent,scatter}] [--badam_verbose BADAM_VERBOSE] [--use_galore [USE_GALORE]] [--galore_target GALORE_TARGET] [--galore_rank GALORE_RANK] [--galore_update_interval GALORE_UPDATE_INTERVAL] [--galore_scale GALORE_SCALE] [--galore_proj_type {std,reverse_std,right,left,full}] [--galore_layerwise [GALORE_LAYERWISE]] [--pref_beta PREF_BETA] [--pref_ftx PREF_FTX] [--pref_loss {sigmoid,hinge,ipo,kto_pair,orpo,simpo}] [--dpo_label_smoothing DPO_LABEL_SMOOTHING] [--kto_chosen_weight KTO_CHOSEN_WEIGHT] [--kto_rejected_weight KTO_REJECTED_WEIGHT] [--simpo_gamma SIMPO_GAMMA] [--ppo_buffer_size PPO_BUFFER_SIZE] [--ppo_epochs PPO_EPOCHS] [--ppo_score_norm [PPO_SCORE_NORM]] [--ppo_target PPO_TARGET] [--ppo_whiten_rewards [PPO_WHITEN_REWARDS]] [--ref_model REF_MODEL] [--ref_model_adapters REF_MODEL_ADAPTERS] [--ref_model_quantization_bit REF_MODEL_QUANTIZATION_BIT] [--reward_model REWARD_MODEL] [--reward_model_adapters REWARD_MODEL_ADAPTERS] [--reward_model_quantization_bit REWARD_MODEL_QUANTIZATION_BIT] [--reward_model_type {lora,full,api}] [--additional_target ADDITIONAL_TARGET] [--lora_alpha LORA_ALPHA] [--lora_dropout LORA_DROPOUT] [--lora_rank LORA_RANK] [--lora_target LORA_TARGET] [--loraplus_lr_ratio LORAPLUS_LR_RATIO] [--loraplus_lr_embedding LORAPLUS_LR_EMBEDDING] [--use_rslora [USE_RSLORA]] [--use_dora [USE_DORA]] [--pissa_init [PISSA_INIT]] [--pissa_iter PISSA_ITER] [--pissa_convert [PISSA_CONVERT]] [--create_new_adapter [CREATE_NEW_ADAPTER]] [--freeze_trainable_layers FREEZE_TRAINABLE_LAYERS] [--freeze_trainable_modules FREEZE_TRAINABLE_MODULES] [--freeze_extra_modules FREEZE_EXTRA_MODULES] [--pure_bf16 [PURE_BF16]] [--stage {pt,sft,rm,ppo,dpo,kto}] [--finetuning_type {lora,freeze,full}] [--use_llama_pro [USE_LLAMA_PRO]] [--use_adam_mini [USE_ADAM_MINI]] [--freeze_vision_tower [FREEZE_VISION_TOWER]] [--no_freeze_vision_tower] [--train_mm_proj_only [TRAIN_MM_PROJ_ONLY]] [--compute_accuracy [COMPUTE_ACCURACY]] [--plot_loss [PLOT_LOSS]] [--do_sample [DO_SAMPLE]] [--no_do_sample] [--temperature TEMPERATURE] [--top_p TOP_P] [--top_k TOP_K] [--num_beams NUM_BEAMS] [--max_length MAX_LENGTH] [--max_new_tokens MAX_NEW_TOKENS] [--repetition_penalty REPETITION_PENALTY] [--length_penalty LENGTH_PENALTY] [--default_system DEFAULT_SYSTEM] optional arguments: -h, --help show this help message and exit --model_name_or_path MODEL_NAME_OR_PATH Path to the model weight or identifier from huggingface.co/models or modelscope.cn/models. (default: None) --adapter_name_or_path ADAPTER_NAME_OR_PATH Path to the adapter weight or identifier from huggingface.co/models. Use commas to separate multiple adapters. (default: None) --adapter_folder ADAPTER_FOLDER The folder containing the adapter weights to load. (default: None) --cache_dir CACHE_DIR Where to store the pre-trained models downloaded from huggingface.co or modelscope.cn. (default: None) --use_fast_tokenizer [USE_FAST_TOKENIZER] Whether or not to use one of the fast tokenizer (backed by the tokenizers library). (default: True) --no_use_fast_tokenizer Whether or not to use one of the fast tokenizer (backed by the tokenizers library). (default: False) --resize_vocab [RESIZE_VOCAB] Whether or not to resize the tokenizer vocab and the embedding layers. (default: False) --split_special_tokens [SPLIT_SPECIAL_TOKENS] Whether or not the special tokens should be split during the tokenization process. (default: False) --new_special_tokens NEW_SPECIAL_TOKENS Special tokens to be added into the tokenizer. Use commas to separate multiple tokens. (default: None) --model_revision MODEL_REVISION The specific model version to use (can be a branch name, tag name or commit id). (default: main) --low_cpu_mem_usage [LOW_CPU_MEM_USAGE] Whether or not to use memory-efficient model loading. (default: True) --no_low_cpu_mem_usage Whether or not to use memory-efficient model loading. (default: False) --quantization_method {bitsandbytes,hqq,eetq} Quantization method to use for on-the-fly quantization. (default: bitsandbytes) --quantization_bit QUANTIZATION_BIT The number of bits to quantize the model using bitsandbytes. (default: None) --quantization_type {fp4,nf4} Quantization data type to use in int4 training. (default: nf4) --double_quantization [DOUBLE_QUANTIZATION] Whether or not to use double quantization in int4 training. (default: True) --no_double_quantization Whether or not to use double quantization in int4 training. (default: False) --quantization_device_map {auto} Device map used to infer the 4-bit quantized model, needs bitsandbytes>=0.43.0. (default: None) --rope_scaling {linear,dynamic} Which scaling strategy should be adopted for the RoPE embeddings. (default: None) --flash_attn {auto,disabled,sdpa,fa2} Enable FlashAttention for faster training and inference. (default: auto) --shift_attn [SHIFT_ATTN] Enable shift short attention (S^2-Attn) proposed by LongLoRA. (default: False) --mixture_of_depths {convert,load} Convert the model to mixture-of-depths (MoD) or load the MoD model. (default: None) --use_unsloth [USE_UNSLOTH] Whether or not to use unsloth's optimization for the LoRA training. (default: False) --visual_inputs [VISUAL_INPUTS] Whethor or not to use multimodal LLM that accepts visual inputs. (default: False) --moe_aux_loss_coef MOE_AUX_LOSS_COEF Coefficient of the auxiliary router loss in mixture- of-experts model. (default: None) --disable_gradient_checkpointing [DISABLE_GRADIENT_CHECKPOINTING] Whether or not to disable gradient checkpointing. (default: False) --upcast_layernorm [UPCAST_LAYERNORM] Whether or not to upcast the layernorm weights in fp32. (default: False) --upcast_lmhead_output [UPCAST_LMHEAD_OUTPUT] Whether or not to upcast the output of lm_head in fp32. (default: False) --train_from_scratch [TRAIN_FROM_SCRATCH] Whether or not to randomly initialize the model weights. (default: False) --infer_backend {huggingface,vllm} Backend engine used at inference. (default: huggingface) --vllm_maxlen VLLM_MAXLEN Maximum sequence (prompt + response) length of the vLLM engine. (default: 2048) --vllm_gpu_util VLLM_GPU_UTIL The fraction of GPU memory in (0,1) to be used for the vLLM engine. (default: 0.9) --vllm_enforce_eager [VLLM_ENFORCE_EAGER] Whether or not to disable CUDA graph in the vLLM engine. (default: False) --vllm_max_lora_rank VLLM_MAX_LORA_RANK Maximum rank of all LoRAs in the vLLM engine. (default: 32) --offload_folder OFFLOAD_FOLDER Path to offload model weights. (default: offload) --use_cache [USE_CACHE] Whether or not to use KV cache in generation. (default: True) --no_use_cache Whether or not to use KV cache in generation. (default: False) --infer_dtype {auto,float16,bfloat16,float32} Data type for model weights and activations at inference. (default: auto) --hf_hub_token HF_HUB_TOKEN Auth token to log in with Hugging Face Hub. (default: None) --ms_hub_token MS_HUB_TOKEN Auth token to log in with ModelScope Hub. (default: None) --export_dir EXPORT_DIR Path to the directory to save the exported model. (default: None) --export_size EXPORT_SIZE The file shard size (in GB) of the exported model. (default: 1) --export_device {cpu,auto} The device used in model export, use `auto` to accelerate exporting. (default: cpu) --export_quantization_bit EXPORT_QUANTIZATION_BIT The number of bits to quantize the exported model. (default: None) --export_quantization_dataset EXPORT_QUANTIZATION_DATASET Path to the dataset or dataset name to use in quantizing the exported model. (default: None) --export_quantization_nsamples EXPORT_QUANTIZATION_NSAMPLES The number of samples used for quantization. (default: 128) --export_quantization_maxlen EXPORT_QUANTIZATION_MAXLEN The maximum length of the model inputs used for quantization. (default: 1024) --export_legacy_format [EXPORT_LEGACY_FORMAT] Whether or not to save the `.bin` files instead of `.safetensors`. (default: False) --export_hub_model_id EXPORT_HUB_MODEL_ID The name of the repository if push the model to the Hugging Face hub. (default: None) --print_param_status [PRINT_PARAM_STATUS] For debugging purposes, print the status of the parameters in the model. (default: False) --template TEMPLATE Which template to use for constructing prompts in training and inference. (default: None) --dataset DATASET The name of dataset(s) to use for training. Use commas to separate multiple datasets. (default: None) --eval_dataset EVAL_DATASET The name of dataset(s) to use for evaluation. Use commas to separate multiple datasets. (default: None) --dataset_dir DATASET_DIR Path to the folder containing the datasets. (default: data) --cutoff_len CUTOFF_LEN The cutoff length of the tokenized inputs in the dataset. (default: 1024) --train_on_prompt [TRAIN_ON_PROMPT] Whether or not to disable the mask on the prompt. (default: False) --mask_history [MASK_HISTORY] Whether or not to mask the history and train on the last turn only. (default: False) --streaming [STREAMING] Enable dataset streaming. (default: False) --buffer_size BUFFER_SIZE Size of the buffer to randomly sample examples from in dataset streaming. (default: 16384) --mix_strategy {concat,interleave_under,interleave_over} Strategy to use in dataset mixing (concat/interleave) (undersampling/oversampling). (default: concat) --interleave_probs INTERLEAVE_PROBS Probabilities to sample data from datasets. Use commas to separate multiple datasets. (default: None) --overwrite_cache [OVERWRITE_CACHE] Overwrite the cached training and evaluation sets. (default: False) --preprocessing_num_workers PREPROCESSING_NUM_WORKERS The number of processes to use for the pre-processing. (default: None) --max_samples MAX_SAMPLES For debugging purposes, truncate the number of examples for each dataset. (default: None) --eval_num_beams EVAL_NUM_BEAMS Number of beams to use for evaluation. This argument will be passed to `model.generate` (default: None) --ignore_pad_token_for_loss [IGNORE_PAD_TOKEN_FOR_LOSS] Whether or not to ignore the tokens corresponding to the pad label in loss computation. (default: True) --no_ignore_pad_token_for_loss Whether or not to ignore the tokens corresponding to the pad label in loss computation. (default: False) --val_size VAL_SIZE Size of the development set, should be an integer or a float in range `[0,1)`. (default: 0.0) --packing PACKING Enable sequences packing in training. Will automatically enable in pre-training. (default: None) --neat_packing [NEAT_PACKING] Enable sequence packing without cross-attention. (default: False) --tool_format TOOL_FORMAT Tool format to use for constructing function calling examples. (default: None) --tokenized_path TOKENIZED_PATH Path to save or load the tokenized datasets. (default: None) --output_dir OUTPUT_DIR The output directory where the model predictions and checkpoints will be written. (default: None) --overwrite_output_dir [OVERWRITE_OUTPUT_DIR] Overwrite the content of the output directory. Use this to continue training if output_dir points to a checkpoint directory. (default: False) --do_train [DO_TRAIN] Whether to run training. (default: False) --do_eval [DO_EVAL] Whether to run eval on the dev set. (default: False) --do_predict [DO_PREDICT] Whether to run predictions on the test set. (default: False) --eval_strategy {no,steps,epoch} The evaluation strategy to use. (default: no) --prediction_loss_only [PREDICTION_LOSS_ONLY] When performing evaluation and predictions, only returns the loss. (default: False) --per_device_train_batch_size PER_DEVICE_TRAIN_BATCH_SIZE Batch size per GPU/TPU/MPS/NPU core/CPU for training. (default: 8) --per_device_eval_batch_size PER_DEVICE_EVAL_BATCH_SIZE Batch size per GPU/TPU/MPS/NPU core/CPU for evaluation. (default: 8) --per_gpu_train_batch_size PER_GPU_TRAIN_BATCH_SIZE Deprecated, the use of `--per_device_train_batch_size` is preferred. Batch size per GPU/TPU core/CPU for training. (default: None) --per_gpu_eval_batch_size PER_GPU_EVAL_BATCH_SIZE Deprecated, the use of `--per_device_eval_batch_size` is preferred. Batch size per GPU/TPU core/CPU for evaluation. (default: None) --gradient_accumulation_steps GRADIENT_ACCUMULATION_STEPS Number of updates steps to accumulate before performing a backward/update pass. (default: 1) --eval_accumulation_steps EVAL_ACCUMULATION_STEPS Number of predictions steps to accumulate before moving the tensors to the CPU. (default: None) --eval_delay EVAL_DELAY Number of epochs or steps to wait for before the first evaluation can be performed, depending on the eval_strategy. (default: 0) --torch_empty_cache_steps TORCH_EMPTY_CACHE_STEPS Number of steps to wait before calling `torch..empty_cache()`.This can help avoid CUDA out-of-memory errors by lowering peak VRAM usage at a cost of about [10{'option_strings': ['-- torch_empty_cache_steps'], 'dest': 'torch_empty_cache_steps', 'nargs': None, 'const': None, 'default': None, 'type': 'int', 'choices': None, 'required': False, 'help': 'Number of steps to wait before calling `torch..empty_cache()`.This can help avoid CUDA out-of-memory errors by lowering peak VRAM usage at a cost of about [10% slower performance] (https://github.com/huggingface/transformers/issues/31 372).If left unset or set to None, cache will not be emptied.', 'metavar': None, 'container': , 'prog': 'launcher.py'}lower performance](https://githu b.com/huggingface/transformers/issues/31372).If left unset or set to None, cache will not be emptied. (default: None) --learning_rate LEARNING_RATE The initial learning rate for AdamW. (default: 5e-05) --weight_decay WEIGHT_DECAY Weight decay for AdamW if we apply some. (default: 0.0) --adam_beta1 ADAM_BETA1 Beta1 for AdamW optimizer (default: 0.9) --adam_beta2 ADAM_BETA2 Beta2 for AdamW optimizer (default: 0.999) --adam_epsilon ADAM_EPSILON Epsilon for AdamW optimizer. (default: 1e-08) --max_grad_norm MAX_GRAD_NORM Max gradient norm. (default: 1.0) --num_train_epochs NUM_TRAIN_EPOCHS Total number of training epochs to perform. (default: 3.0) --max_steps MAX_STEPS If > 0: set total number of training steps to perform. Override num_train_epochs. (default: -1) --lr_scheduler_type {linear,cosine,cosine_with_restarts,polynomial,constant,constant_with_warmup,inverse_sqrt,reduce_lr_on_plateau,cosine_with_min_lr,warmup_stable_decay} The scheduler type to use. (default: linear) --lr_scheduler_kwargs LR_SCHEDULER_KWARGS Extra parameters for the lr_scheduler such as {'num_cycles': 1} for the cosine with hard restarts. (default: {}) --warmup_ratio WARMUP_RATIO Linear warmup over warmup_ratio fraction of total steps. (default: 0.0) --warmup_steps WARMUP_STEPS Linear warmup over warmup_steps. (default: 0) --log_level {detail,debug,info,warning,error,critical,passive} Logger log level to use on the main node. Possible choices are the log levels as strings: 'debug', 'info', 'warning', 'error' and 'critical', plus a 'passive' level which doesn't set anything and lets the application set the level. Defaults to 'passive'. (default: passive) --log_level_replica {detail,debug,info,warning,error,critical,passive} Logger log level to use on replica nodes. Same choices and defaults as ``log_level`` (default: warning) --log_on_each_node [LOG_ON_EACH_NODE] When doing a multinode distributed training, whether to log once per node or just once on the main node. (default: True) --no_log_on_each_node When doing a multinode distributed training, whether to log once per node or just once on the main node. (default: False) --logging_dir LOGGING_DIR Tensorboard log dir. (default: None) --logging_strategy {no,steps,epoch} The logging strategy to use. (default: steps) --logging_first_step [LOGGING_FIRST_STEP] Log the first global_step (default: False) --logging_steps LOGGING_STEPS Log every X updates steps. Should be an integer or a float in range `[0,1)`. If smaller than 1, will be interpreted as ratio of total training steps. (default: 500) --logging_nan_inf_filter [LOGGING_NAN_INF_FILTER] Filter nan and inf losses for logging. (default: True) --no_logging_nan_inf_filter Filter nan and inf losses for logging. (default: False) --save_strategy {no,steps,epoch} The checkpoint save strategy to use. (default: steps) --save_steps SAVE_STEPS Save checkpoint every X updates steps. Should be an integer or a float in range `[0,1)`. If smaller than 1, will be interpreted as ratio of total training steps. (default: 500) --save_total_limit SAVE_TOTAL_LIMIT If a value is passed, will limit the total amount of checkpoints. Deletes the older checkpoints in `output_dir`. When `load_best_model_at_end` is enabled, the 'best' checkpoint according to `metric_for_best_model` will always be retained in addition to the most recent ones. For example, for `save_total_limit=5` and `load_best_model_at_end=True`, the four last checkpoints will always be retained alongside the best model. When `save_total_limit=1` and `load_best_model_at_end=True`, it is possible that two checkpoints are saved: the last one and the best one (if they are different). Default is unlimited checkpoints (default: None) --save_safetensors [SAVE_SAFETENSORS] Use safetensors saving and loading for state dicts instead of default torch.load and torch.save. (default: True) --no_save_safetensors Use safetensors saving and loading for state dicts instead of default torch.load and torch.save. (default: False) --save_on_each_node [SAVE_ON_EACH_NODE] When doing multi-node distributed training, whether to save models and checkpoints on each node, or only on the main one (default: False) --save_only_model [SAVE_ONLY_MODEL] When checkpointing, whether to only save the model, or also the optimizer, scheduler & rng state.Note that when this is true, you won't be able to resume training from checkpoint.This enables you to save storage by not storing the optimizer, scheduler & rng state.You can only load the model using from_pretrained with this option set to True. (default: False) --restore_callback_states_from_checkpoint [RESTORE_CALLBACK_STATES_FROM_CHECKPOINT] Whether to restore the callback states from the checkpoint. If `True`, will override callbacks passed to the `Trainer` if they exist in the checkpoint. (default: False) --no_cuda [NO_CUDA] This argument is deprecated. It will be removed in version 5.0 of 🤗 Transformers. (default: False) --use_cpu [USE_CPU] Whether or not to use cpu. If set to False, we will use cuda/tpu/mps/npu device if available. (default: False) --use_mps_device [USE_MPS_DEVICE] This argument is deprecated. `mps` device will be used if available similar to `cuda` device. It will be removed in version 5.0 of 🤗 Transformers (default: False) --seed SEED Random seed that will be set at the beginning of training. (default: 42) --data_seed DATA_SEED Random seed to be used with data samplers. (default: None) --jit_mode_eval [JIT_MODE_EVAL] Whether or not to use PyTorch jit trace for inference (default: False) --use_ipex [USE_IPEX] Use Intel extension for PyTorch when it is available, installation: 'https://github.com/intel/intel- extension-for-pytorch' (default: False) --bf16 [BF16] Whether to use bf16 (mixed) precision instead of 32-bit. Requires Ampere or higher NVIDIA architecture or using CPU (use_cpu) or Ascend NPU. This is an experimental API and it may change. (default: False) --fp16 [FP16] Whether to use fp16 (mixed) precision instead of 32-bit (default: False) --fp16_opt_level FP16_OPT_LEVEL For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']. See details at https://nvidia.github.io/apex/amp.html (default: O1) --half_precision_backend {auto,apex,cpu_amp} The backend to be used for half precision. (default: auto) --bf16_full_eval [BF16_FULL_EVAL] Whether to use full bfloat16 evaluation instead of 32-bit. This is an experimental API and it may change. (default: False) --fp16_full_eval [FP16_FULL_EVAL] Whether to use full float16 evaluation instead of 32-bit (default: False) --tf32 TF32 Whether to enable tf32 mode, available in Ampere and newer GPU architectures. This is an experimental API and it may change. (default: None) --local_rank LOCAL_RANK For distributed training: local_rank (default: -1) --ddp_backend {nccl,gloo,mpi,ccl,hccl,cncl} The backend to be used for distributed training (default: None) --tpu_num_cores TPU_NUM_CORES TPU: Number of TPU cores (automatically passed by launcher script) (default: None) --tpu_metrics_debug [TPU_METRICS_DEBUG] Deprecated, the use of `--debug tpu_metrics_debug` is preferred. TPU: Whether to print debug metrics (default: False) --debug DEBUG [DEBUG ...] Whether or not to enable debug mode. Current options: `underflow_overflow` (Detect underflow and overflow in activations and weights), `tpu_metrics_debug` (print debug metrics on TPU). (default: None) --dataloader_drop_last [DATALOADER_DROP_LAST] Drop the last incomplete batch if it is not divisible by the batch size. (default: False) --eval_steps EVAL_STEPS Run an evaluation every X steps. Should be an integer or a float in range `[0,1)`. If smaller than 1, will be interpreted as ratio of total training steps. (default: None) --dataloader_num_workers DATALOADER_NUM_WORKERS Number of subprocesses to use for data loading (PyTorch only). 0 means that the data will be loaded in the main process. (default: 0) --dataloader_prefetch_factor DATALOADER_PREFETCH_FACTOR Number of batches loaded in advance by each worker. 2 means there will be a total of 2 * num_workers batches prefetched across all workers. Default is 2 for PyTorch < 2.0.0 and otherwise None. (default: None) --past_index PAST_INDEX If >=0, uses the corresponding part of the output as the past state for next step. (default: -1) --run_name RUN_NAME An optional descriptor for the run. Notably used for wandb, mlflow and comet logging. (default: None) --disable_tqdm DISABLE_TQDM Whether or not to disable the tqdm progress bars. (default: None) --remove_unused_columns [REMOVE_UNUSED_COLUMNS] Remove columns not required by the model when using an nlp.Dataset. (default: True) --no_remove_unused_columns Remove columns not required by the model when using an nlp.Dataset. (default: False) --label_names LABEL_NAMES [LABEL_NAMES ...] The list of keys in your dictionary of inputs that correspond to the labels. (default: None) --load_best_model_at_end [LOAD_BEST_MODEL_AT_END] Whether or not to load the best model found during training at the end of training. When this option is enabled, the best checkpoint will always be saved. See `save_total_limit` for more. (default: False) --metric_for_best_model METRIC_FOR_BEST_MODEL The metric to use to compare two different models. (default: None) --greater_is_better GREATER_IS_BETTER Whether the `metric_for_best_model` should be maximized or not. (default: None) --ignore_data_skip [IGNORE_DATA_SKIP] When resuming training, whether or not to skip the first epochs and batches to get to the same training data. (default: False) --fsdp FSDP Whether or not to use PyTorch Fully Sharded Data Parallel (FSDP) training (in distributed training only). The base option should be `full_shard`, `shard_grad_op` or `no_shard` and you can add CPU- offload to `full_shard` or `shard_grad_op` like this: full_shard offload` or `shard_grad_op offload`. You can add auto-wrap to `full_shard` or `shard_grad_op` with the same syntax: full_shard auto_wrap` or `shard_grad_op auto_wrap`. (default: ) --fsdp_min_num_params FSDP_MIN_NUM_PARAMS This parameter is deprecated. FSDP's minimum number of parameters for Default Auto Wrapping. (useful only when `fsdp` field is passed). (default: 0) --fsdp_config FSDP_CONFIG Config to be used with FSDP (Pytorch Fully Sharded Data Parallel). The value is either a fsdp json config file (e.g., `fsdp_config.json`) or an already loaded json file as `dict`. (default: None) --fsdp_transformer_layer_cls_to_wrap FSDP_TRANSFORMER_LAYER_CLS_TO_WRAP This parameter is deprecated. Transformer layer class name (case-sensitive) to wrap, e.g, `BertLayer`, `GPTJBlock`, `T5Block` .... (useful only when `fsdp` flag is passed). (default: None) --accelerator_config ACCELERATOR_CONFIG Config to be used with the internal Accelerator object initializtion. The value is either a accelerator json config file (e.g., `accelerator_config.json`) or an already loaded json file as `dict`. (default: None) --deepspeed DEEPSPEED Enable deepspeed and pass the path to deepspeed json config file (e.g. `ds_config.json`) or an already loaded json file as a dict (default: None) --label_smoothing_factor LABEL_SMOOTHING_FACTOR The label smoothing epsilon to apply (zero means no label smoothing). (default: 0.0) --optim {adamw_hf,adamw_torch,adamw_torch_fused,adamw_torch_xla,adamw_torch_npu_fused,adamw_apex_fused,adafactor,adamw_anyprecision,sgd,adagrad,adamw_bnb_8bit,adamw_8bit,lion_8bit,lion_32bit,paged_adamw_32bit,paged_adamw_8bit,paged_lion_32bit,paged_lion_8bit,rmsprop,rmsprop_bnb,rmsprop_bnb_8bit,rmsprop_bnb_32bit,galore_adamw,galore_adamw_8bit,galore_adafactor,galore_adamw_layerwise,galore_adamw_8bit_layerwise,galore_adafactor_layerwise,lomo,adalomo} The optimizer to use. (default: adamw_torch) --optim_args OPTIM_ARGS Optional arguments to supply to optimizer. (default: None) --adafactor [ADAFACTOR] Whether or not to replace AdamW by Adafactor. (default: False) --group_by_length [GROUP_BY_LENGTH] Whether or not to group samples of roughly the same length together when batching. (default: False) --length_column_name LENGTH_COLUMN_NAME Column name with precomputed lengths to use when grouping by length. (default: length) --report_to REPORT_TO The list of integrations to report the results and logs to. (default: None) --ddp_find_unused_parameters DDP_FIND_UNUSED_PARAMETERS When using distributed training, the value of the flag `find_unused_parameters` passed to `DistributedDataParallel`. (default: None) --ddp_bucket_cap_mb DDP_BUCKET_CAP_MB When using distributed training, the value of the flag `bucket_cap_mb` passed to `DistributedDataParallel`. (default: None) --ddp_broadcast_buffers DDP_BROADCAST_BUFFERS When using distributed training, the value of the flag `broadcast_buffers` passed to `DistributedDataParallel`. (default: None) --dataloader_pin_memory [DATALOADER_PIN_MEMORY] Whether or not to pin memory for DataLoader. (default: True) --no_dataloader_pin_memory Whether or not to pin memory for DataLoader. (default: False) --dataloader_persistent_workers [DATALOADER_PERSISTENT_WORKERS] If True, the data loader will not shut down the worker processes after a dataset has been consumed once. This allows to maintain the workers Dataset instances alive. Can potentially speed up training, but will increase RAM usage. (default: False) --skip_memory_metrics [SKIP_MEMORY_METRICS] Whether or not to skip adding of memory profiler reports to metrics. (default: True) --no_skip_memory_metrics Whether or not to skip adding of memory profiler reports to metrics. (default: False) --use_legacy_prediction_loop [USE_LEGACY_PREDICTION_LOOP] Whether or not to use the legacy prediction_loop in the Trainer. (default: False) --push_to_hub [PUSH_TO_HUB] Whether or not to upload the trained model to the model hub after training. (default: False) --resume_from_checkpoint RESUME_FROM_CHECKPOINT The path to a folder with a valid checkpoint for your model. (default: None) --hub_model_id HUB_MODEL_ID The name of the repository to keep in sync with the local `output_dir`. (default: None) --hub_strategy {end,every_save,checkpoint,all_checkpoints} The hub strategy to use when `--push_to_hub` is activated. (default: every_save) --hub_token HUB_TOKEN The token to use to push to the Model Hub. (default: None) --hub_private_repo [HUB_PRIVATE_REPO] Whether the model repository is private or not. (default: False) --hub_always_push [HUB_ALWAYS_PUSH] Unless `True`, the Trainer will skip pushes if the previous one wasn't finished yet. (default: False) --gradient_checkpointing [GRADIENT_CHECKPOINTING] If True, use gradient checkpointing to save memory at the expense of slower backward pass. (default: False) --gradient_checkpointing_kwargs GRADIENT_CHECKPOINTING_KWARGS Gradient checkpointing key word arguments such as `use_reentrant`. Will be passed to `torch.utils.checkpoint.checkpoint` through `model.gradient_checkpointing_enable`. (default: None) --include_inputs_for_metrics [INCLUDE_INPUTS_FOR_METRICS] Whether or not the inputs will be passed to the `compute_metrics` function. (default: False) --eval_do_concat_batches [EVAL_DO_CONCAT_BATCHES] Whether to recursively concat inputs/losses/labels/predictions across batches. If `False`, will instead store them as lists, with each batch kept separate. (default: True) --no_eval_do_concat_batches Whether to recursively concat inputs/losses/labels/predictions across batches. If `False`, will instead store them as lists, with each batch kept separate. (default: False) --fp16_backend {auto,apex,cpu_amp} Deprecated. Use half_precision_backend instead (default: auto) --evaluation_strategy {no,steps,epoch} Deprecated. Use `eval_strategy` instead (default: None) --push_to_hub_model_id PUSH_TO_HUB_MODEL_ID The name of the repository to which push the `Trainer`. (default: None) --push_to_hub_organization PUSH_TO_HUB_ORGANIZATION The name of the organization in with to which push the `Trainer`. (default: None) --push_to_hub_token PUSH_TO_HUB_TOKEN The token to use to push to the Model Hub. (default: None) --mp_parameters MP_PARAMETERS Used by the SageMaker launcher to send mp-specific args. Ignored in Trainer (default: ) --auto_find_batch_size [AUTO_FIND_BATCH_SIZE] Whether to automatically decrease the batch size in half and rerun the training loop again each time a CUDA Out-of-Memory was reached (default: False) --full_determinism [FULL_DETERMINISM] Whether to call enable_full_determinism instead of set_seed for reproducibility in distributed training. Important: this will negatively impact the performance, so only use it for debugging. (default: False) --torchdynamo TORCHDYNAMO This argument is deprecated, use `--torch_compile_backend` instead. (default: None) --ray_scope RAY_SCOPE The scope to use when doing hyperparameter search with Ray. By default, `"last"` will be used. Ray will then use the last checkpoint of all trials, compare those, and select the best one. However, other options are also available. See the Ray documentation (https://doc s.ray.io/en/latest/tune/api_docs/analysis.html#ray.tun e.ExperimentAnalysis.get_best_trial) for more options. (default: last) --ddp_timeout DDP_TIMEOUT Overrides the default timeout for distributed training (value should be given in seconds). (default: 1800) --torch_compile [TORCH_COMPILE] If set to `True`, the model will be wrapped in `torch.compile`. (default: False) --torch_compile_backend TORCH_COMPILE_BACKEND Which backend to use with `torch.compile`, passing one will trigger a model compilation. (default: None) --torch_compile_mode TORCH_COMPILE_MODE Which mode to use with `torch.compile`, passing one will trigger a model compilation. (default: None) --dispatch_batches DISPATCH_BATCHES Deprecated. Pass {'dispatch_batches':VALUE} to `accelerator_config`. (default: None) --split_batches SPLIT_BATCHES Deprecated. Pass {'split_batches':True} to `accelerator_config`. (default: None) --include_tokens_per_second [INCLUDE_TOKENS_PER_SECOND] If set to `True`, the speed metrics will include `tgs` (tokens per second per device). (default: False) --include_num_input_tokens_seen [INCLUDE_NUM_INPUT_TOKENS_SEEN] If set to `True`, will track the number of input tokens seen throughout training. (May be slower in distributed training) (default: False) --neftune_noise_alpha NEFTUNE_NOISE_ALPHA Activates neftune noise embeddings into the model. NEFTune has been proven to drastically improve model performances for instrcution fine-tuning. Check out the original paper here: https://arxiv.org/abs/2310.05914 and the original code here: https://github.com/neelsjain/NEFTune. Only supported for `PreTrainedModel` and `PeftModel` classes. (default: None) --optim_target_modules OPTIM_TARGET_MODULES Target modules for the optimizer defined in the `optim` argument. Only used for the GaLore optimizer at the moment. (default: None) --batch_eval_metrics [BATCH_EVAL_METRICS] Break eval metrics calculation into batches to save memory. (default: False) --eval_on_start [EVAL_ON_START] Whether to run through the entire `evaluation` step at the very beginning of training as a sanity check. (default: False) --eval_use_gather_object [EVAL_USE_GATHER_OBJECT] Whether to run recursively gather object in a nested list/tuple/dictionary of objects from all devices. (default: False) --sortish_sampler [SORTISH_SAMPLER] Whether to use SortishSampler or not. (default: False) --predict_with_generate [PREDICT_WITH_GENERATE] Whether to use generate to calculate generative metrics (ROUGE, BLEU). (default: False) --generation_max_length GENERATION_MAX_LENGTH The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default to the `max_length` value of the model configuration. (default: None) --generation_num_beams GENERATION_NUM_BEAMS The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default to the `num_beams` value of the model configuration. (default: None) --generation_config GENERATION_CONFIG Model id, file path or url pointing to a GenerationConfig json file, to use during prediction. (default: None) --use_badam [USE_BADAM] Whether or not to use the BAdam optimizer. (default: False) --badam_mode {layer,ratio} Whether to use layer-wise or ratio-wise BAdam optimizer. (default: layer) --badam_start_block BADAM_START_BLOCK The starting block index for layer-wise BAdam. (default: None) --badam_switch_mode {ascending,descending,random,fixed} the strategy of picking block to update for layer-wise BAdam. (default: ascending) --badam_switch_interval BADAM_SWITCH_INTERVAL Number of steps to update the block for layer-wise BAdam. Use -1 to disable the block update. (default: 50) --badam_update_ratio BADAM_UPDATE_RATIO The ratio of the update for ratio-wise BAdam. (default: 0.05) --badam_mask_mode {adjacent,scatter} The mode of the mask for BAdam optimizer. `adjacent` means that the trainable parameters are adjacent to each other, `scatter` means that trainable parameters are randomly choosed from the weight. (default: adjacent) --badam_verbose BADAM_VERBOSE The verbosity level of BAdam optimizer. 0 for no print, 1 for print the block prefix, 2 for print trainable parameters. (default: 0) --use_galore [USE_GALORE] Whether or not to use the gradient low-Rank projection (GaLore). (default: False) --galore_target GALORE_TARGET Name(s) of modules to apply GaLore. Use commas to separate multiple modules. Use `all` to specify all the linear modules. (default: all) --galore_rank GALORE_RANK The rank of GaLore gradients. (default: 16) --galore_update_interval GALORE_UPDATE_INTERVAL Number of steps to update the GaLore projection. (default: 200) --galore_scale GALORE_SCALE GaLore scaling coefficient. (default: 0.25) --galore_proj_type {std,reverse_std,right,left,full} Type of GaLore projection. (default: std) --galore_layerwise [GALORE_LAYERWISE] Whether or not to enable layer-wise update to further save memory. (default: False) --pref_beta PREF_BETA The beta parameter in the preference loss. (default: 0.1) --pref_ftx PREF_FTX The supervised fine-tuning loss coefficient in DPO training. (default: 0.0) --pref_loss {sigmoid,hinge,ipo,kto_pair,orpo,simpo} The type of DPO loss to use. (default: sigmoid) --dpo_label_smoothing DPO_LABEL_SMOOTHING The robust DPO label smoothing parameter in cDPO that should be between 0 and 0.5. (default: 0.0) --kto_chosen_weight KTO_CHOSEN_WEIGHT The weight factor of the desirable losses in KTO training. (default: 1.0) --kto_rejected_weight KTO_REJECTED_WEIGHT The weight factor of the undesirable losses in KTO training. (default: 1.0) --simpo_gamma SIMPO_GAMMA The target reward margin term in SimPO loss. (default: 0.5) --ppo_buffer_size PPO_BUFFER_SIZE The number of mini-batches to make experience buffer in a PPO optimization step. (default: 1) --ppo_epochs PPO_EPOCHS The number of epochs to perform in a PPO optimization step. (default: 4) --ppo_score_norm [PPO_SCORE_NORM] Use score normalization in PPO training. (default: False) --ppo_target PPO_TARGET Target KL value for adaptive KL control in PPO training. (default: 6.0) --ppo_whiten_rewards [PPO_WHITEN_REWARDS] Whiten the rewards before compute advantages in PPO training. (default: False) --ref_model REF_MODEL Path to the reference model used for the PPO or DPO training. (default: None) --ref_model_adapters REF_MODEL_ADAPTERS Path to the adapters of the reference model. (default: None) --ref_model_quantization_bit REF_MODEL_QUANTIZATION_BIT The number of bits to quantize the reference model. (default: None) --reward_model REWARD_MODEL Path to the reward model used for the PPO training. (default: None) --reward_model_adapters REWARD_MODEL_ADAPTERS Path to the adapters of the reward model. (default: None) --reward_model_quantization_bit REWARD_MODEL_QUANTIZATION_BIT The number of bits to quantize the reward model. (default: None) --reward_model_type {lora,full,api} The type of the reward model in PPO training. Lora model only supports lora training. (default: lora) --additional_target ADDITIONAL_TARGET Name(s) of modules apart from LoRA layers to be set as trainable and saved in the final checkpoint. Use commas to separate multiple modules. (default: None) --lora_alpha LORA_ALPHA The scale factor for LoRA fine-tuning (default: lora_rank * 2). (default: None) --lora_dropout LORA_DROPOUT Dropout rate for the LoRA fine-tuning. (default: 0.0) --lora_rank LORA_RANK The intrinsic dimension for LoRA fine-tuning. (default: 8) --lora_target LORA_TARGET Name(s) of target modules to apply LoRA. Use commas to separate multiple modules. Use `all` to specify all the linear modules. (default: all) --loraplus_lr_ratio LORAPLUS_LR_RATIO LoRA plus learning rate ratio (lr_B / lr_A). (default: None) --loraplus_lr_embedding LORAPLUS_LR_EMBEDDING LoRA plus learning rate for lora embedding layers. (default: 1e-06) --use_rslora [USE_RSLORA] Whether or not to use the rank stabilization scaling factor for LoRA layer. (default: False) --use_dora [USE_DORA] Whether or not to use the weight-decomposed lora method (DoRA). (default: False) --pissa_init [PISSA_INIT] Whether or not to initialize a PiSSA adapter. (default: False) --pissa_iter PISSA_ITER The number of iteration steps performed by FSVD in PiSSA. Use -1 to disable it. (default: 16) --pissa_convert [PISSA_CONVERT] Whether or not to convert the PiSSA adapter to a normal LoRA adapter. (default: False) --create_new_adapter [CREATE_NEW_ADAPTER] Whether or not to create a new adapter with randomly initialized weight. (default: False) --freeze_trainable_layers FREEZE_TRAINABLE_LAYERS The number of trainable layers for freeze (partial- parameter) fine-tuning. Positive numbers mean the last n layers are set as trainable, negative numbers mean the first n layers are set as trainable. (default: 2) --freeze_trainable_modules FREEZE_TRAINABLE_MODULES Name(s) of trainable modules for freeze (partial- parameter) fine-tuning. Use commas to separate multiple modules. Use `all` to specify all the available modules. (default: all) --freeze_extra_modules FREEZE_EXTRA_MODULES Name(s) of modules apart from hidden layers to be set as trainable for freeze (partial-parameter) fine- tuning. Use commas to separate multiple modules. (default: None) --pure_bf16 [PURE_BF16] Whether or not to train model in purely bf16 precision (without AMP). (default: False) --stage {pt,sft,rm,ppo,dpo,kto} Which stage will be performed in training. (default: sft) --finetuning_type {lora,freeze,full} Which fine-tuning method to use. (default: lora) --use_llama_pro [USE_LLAMA_PRO] Whether or not to make only the parameters in the expanded blocks trainable. (default: False) --use_adam_mini [USE_ADAM_MINI] Whether or not to use the Adam-mini optimizer. (default: False) --freeze_vision_tower [FREEZE_VISION_TOWER] Whether ot not to freeze vision tower in MLLM training. (default: True) --no_freeze_vision_tower Whether ot not to freeze vision tower in MLLM training. (default: False) --train_mm_proj_only [TRAIN_MM_PROJ_ONLY] Whether or not to train the multimodal projector for MLLM only. (default: False) --compute_accuracy [COMPUTE_ACCURACY] Whether or not to compute the token-level accuracy at evaluation. (default: False) --plot_loss [PLOT_LOSS] Whether or not to save the training loss curves. (default: False) --do_sample [DO_SAMPLE] Whether or not to use sampling, use greedy decoding otherwise. (default: True) --no_do_sample Whether or not to use sampling, use greedy decoding otherwise. (default: False) --temperature TEMPERATURE The value used to modulate the next token probabilities. (default: 0.95) --top_p TOP_P The smallest set of most probable tokens with probabilities that add up to top_p or higher are kept. (default: 0.7) --top_k TOP_K The number of highest probability vocabulary tokens to keep for top-k filtering. (default: 50) --num_beams NUM_BEAMS Number of beams for beam search. 1 means no beam search. (default: 1) --max_length MAX_LENGTH The maximum length the generated tokens can have. It can be overridden by max_new_tokens. (default: 1024) --max_new_tokens MAX_NEW_TOKENS The maximum numbers of tokens to generate, ignoring the number of tokens in the prompt. (default: 1024) --repetition_penalty REPETITION_PENALTY The parameter for repetition penalty. 1.0 means no penalty. (default: 1.0) --length_penalty LENGTH_PENALTY Exponential penalty to the length that is used with beam-based generation. (default: 1.0) --default_system DEFAULT_SYSTEM Default system message to use in chat completion. (default: None) usage: launcher.py [-h] --model_name_or_path MODEL_NAME_OR_PATH [--adapter_name_or_path ADAPTER_NAME_OR_PATH] [--adapter_folder ADAPTER_FOLDER] [--cache_dir CACHE_DIR] [--use_fast_tokenizer [USE_FAST_TOKENIZER]] [--no_use_fast_tokenizer] [--resize_vocab [RESIZE_VOCAB]] [--split_special_tokens [SPLIT_SPECIAL_TOKENS]] [--new_special_tokens NEW_SPECIAL_TOKENS] [--model_revision MODEL_REVISION] [--low_cpu_mem_usage [LOW_CPU_MEM_USAGE]] [--no_low_cpu_mem_usage] [--quantization_method {bitsandbytes,hqq,eetq}] [--quantization_bit QUANTIZATION_BIT] [--quantization_type {fp4,nf4}] [--double_quantization [DOUBLE_QUANTIZATION]] [--no_double_quantization] [--quantization_device_map {auto}] [--rope_scaling {linear,dynamic}] [--flash_attn {auto,disabled,sdpa,fa2}] [--shift_attn [SHIFT_ATTN]] [--mixture_of_depths {convert,load}] [--use_unsloth [USE_UNSLOTH]] [--visual_inputs [VISUAL_INPUTS]] [--moe_aux_loss_coef MOE_AUX_LOSS_COEF] [--disable_gradient_checkpointing [DISABLE_GRADIENT_CHECKPOINTING]] [--upcast_layernorm [UPCAST_LAYERNORM]] [--upcast_lmhead_output [UPCAST_LMHEAD_OUTPUT]] [--train_from_scratch [TRAIN_FROM_SCRATCH]] [--infer_backend {huggingface,vllm}] [--vllm_maxlen VLLM_MAXLEN] [--vllm_gpu_util VLLM_GPU_UTIL] [--vllm_enforce_eager [VLLM_ENFORCE_EAGER]] [--vllm_max_lora_rank VLLM_MAX_LORA_RANK] [--offload_folder OFFLOAD_FOLDER] [--use_cache [USE_CACHE]] [--no_use_cache] [--infer_dtype {auto,float16,bfloat16,float32}] [--hf_hub_token HF_HUB_TOKEN] [--ms_hub_token MS_HUB_TOKEN] [--export_dir EXPORT_DIR] [--export_size EXPORT_SIZE] [--export_device {cpu,auto}] [--export_quantization_bit EXPORT_QUANTIZATION_BIT] [--export_quantization_dataset EXPORT_QUANTIZATION_DATASET] [--export_quantization_nsamples EXPORT_QUANTIZATION_NSAMPLES] [--export_quantization_maxlen EXPORT_QUANTIZATION_MAXLEN] [--export_legacy_format [EXPORT_LEGACY_FORMAT]] [--export_hub_model_id EXPORT_HUB_MODEL_ID] [--print_param_status [PRINT_PARAM_STATUS]] [--template TEMPLATE] [--dataset DATASET] [--eval_dataset EVAL_DATASET] [--dataset_dir DATASET_DIR] [--cutoff_len CUTOFF_LEN] [--train_on_prompt [TRAIN_ON_PROMPT]] [--mask_history [MASK_HISTORY]] [--streaming [STREAMING]] [--buffer_size BUFFER_SIZE] [--mix_strategy {concat,interleave_under,interleave_over}] [--interleave_probs INTERLEAVE_PROBS] [--overwrite_cache [OVERWRITE_CACHE]] [--preprocessing_num_workers PREPROCESSING_NUM_WORKERS] [--max_samples MAX_SAMPLES] [--eval_num_beams EVAL_NUM_BEAMS] [--ignore_pad_token_for_loss [IGNORE_PAD_TOKEN_FOR_LOSS]] [--no_ignore_pad_token_for_loss] [--val_size VAL_SIZE] [--packing PACKING] [--neat_packing [NEAT_PACKING]] [--tool_format TOOL_FORMAT] [--tokenized_path TOKENIZED_PATH] --output_dir OUTPUT_DIR [--overwrite_output_dir [OVERWRITE_OUTPUT_DIR]] [--do_train [DO_TRAIN]] [--do_eval [DO_EVAL]] [--do_predict [DO_PREDICT]] [--eval_strategy {no,steps,epoch}] [--prediction_loss_only [PREDICTION_LOSS_ONLY]] [--per_device_train_batch_size PER_DEVICE_TRAIN_BATCH_SIZE] [--per_device_eval_batch_size PER_DEVICE_EVAL_BATCH_SIZE] [--per_gpu_train_batch_size PER_GPU_TRAIN_BATCH_SIZE] [--per_gpu_eval_batch_size PER_GPU_EVAL_BATCH_SIZE] [--gradient_accumulation_steps GRADIENT_ACCUMULATION_STEPS] [--eval_accumulation_steps EVAL_ACCUMULATION_STEPS] [--eval_delay EVAL_DELAY] [--torch_empty_cache_steps TORCH_EMPTY_CACHE_STEPS] [--learning_rate LEARNING_RATE] [--weight_decay WEIGHT_DECAY] [--adam_beta1 ADAM_BETA1] [--adam_beta2 ADAM_BETA2] [--adam_epsilon ADAM_EPSILON] [--max_grad_norm MAX_GRAD_NORM] [--num_train_epochs NUM_TRAIN_EPOCHS] [--max_steps MAX_STEPS] [--lr_scheduler_type {linear,cosine,cosine_with_restarts,polynomial,constant,constant_with_warmup,inverse_sqrt,reduce_lr_on_plateau,cosine_with_min_lr,warmup_stable_decay}] [--lr_scheduler_kwargs LR_SCHEDULER_KWARGS] [--warmup_ratio WARMUP_RATIO] [--warmup_steps WARMUP_STEPS] [--log_level {detail,debug,info,warning,error,critical,passive}] [--log_level_replica {detail,debug,info,warning,error,critical,passive}] [--log_on_each_node [LOG_ON_EACH_NODE]] [--no_log_on_each_node] [--logging_dir LOGGING_DIR] [--logging_strategy {no,steps,epoch}] [--logging_first_step [LOGGING_FIRST_STEP]] [--logging_steps LOGGING_STEPS] [--logging_nan_inf_filter [LOGGING_NAN_INF_FILTER]] [--no_logging_nan_inf_filter] [--save_strategy {no,steps,epoch}] [--save_steps SAVE_STEPS] [--save_total_limit SAVE_TOTAL_LIMIT] [--save_safetensors [SAVE_SAFETENSORS]] [--no_save_safetensors] [--save_on_each_node [SAVE_ON_EACH_NODE]] [--save_only_model [SAVE_ONLY_MODEL]] [--restore_callback_states_from_checkpoint [RESTORE_CALLBACK_STATES_FROM_CHECKPOINT]] [--no_cuda [NO_CUDA]] [--use_cpu [USE_CPU]] [--use_mps_device [USE_MPS_DEVICE]] [--seed SEED] [--data_seed DATA_SEED] [--jit_mode_eval [JIT_MODE_EVAL]] [--use_ipex [USE_IPEX]] [--bf16 [BF16]] [--fp16 [FP16]] [--fp16_opt_level FP16_OPT_LEVEL] [--half_precision_backend {auto,apex,cpu_amp}] [--bf16_full_eval [BF16_FULL_EVAL]] [--fp16_full_eval [FP16_FULL_EVAL]] [--tf32 TF32] [--local_rank LOCAL_RANK] [--ddp_backend {nccl,gloo,mpi,ccl,hccl,cncl}] [--tpu_num_cores TPU_NUM_CORES] [--tpu_metrics_debug [TPU_METRICS_DEBUG]] [--debug DEBUG [DEBUG ...]] [--dataloader_drop_last [DATALOADER_DROP_LAST]] [--eval_steps EVAL_STEPS] [--dataloader_num_workers DATALOADER_NUM_WORKERS] [--dataloader_prefetch_factor DATALOADER_PREFETCH_FACTOR] [--past_index PAST_INDEX] [--run_name RUN_NAME] [--disable_tqdm DISABLE_TQDM] [--remove_unused_columns [REMOVE_UNUSED_COLUMNS]] [--no_remove_unused_columns] [--label_names LABEL_NAMES [LABEL_NAMES ...]] [--load_best_model_at_end [LOAD_BEST_MODEL_AT_END]] [--metric_for_best_model METRIC_FOR_BEST_MODEL] [--greater_is_better GREATER_IS_BETTER] [--ignore_data_skip [IGNORE_DATA_SKIP]] [--fsdp FSDP] [--fsdp_min_num_params FSDP_MIN_NUM_PARAMS] [--fsdp_config FSDP_CONFIG] [--fsdp_transformer_layer_cls_to_wrap FSDP_TRANSFORMER_LAYER_CLS_TO_WRAP] [--accelerator_config ACCELERATOR_CONFIG] [--deepspeed DEEPSPEED] [--label_smoothing_factor LABEL_SMOOTHING_FACTOR] [--optim {adamw_hf,adamw_torch,adamw_torch_fused,adamw_torch_xla,adamw_torch_npu_fused,adamw_apex_fused,adafactor,adamw_anyprecision,sgd,adagrad,adamw_bnb_8bit,adamw_8bit,lion_8bit,lion_32bit,paged_adamw_32bit,paged_adamw_8bit,paged_lion_32bit,paged_lion_8bit,rmsprop,rmsprop_bnb,rmsprop_bnb_8bit,rmsprop_bnb_32bit,galore_adamw,galore_adamw_8bit,galore_adafactor,galore_adamw_layerwise,galore_adamw_8bit_layerwise,galore_adafactor_layerwise,lomo,adalomo}] [--optim_args OPTIM_ARGS] [--adafactor [ADAFACTOR]] [--group_by_length [GROUP_BY_LENGTH]] [--length_column_name LENGTH_COLUMN_NAME] [--report_to REPORT_TO] [--ddp_find_unused_parameters DDP_FIND_UNUSED_PARAMETERS] [--ddp_bucket_cap_mb DDP_BUCKET_CAP_MB] [--ddp_broadcast_buffers DDP_BROADCAST_BUFFERS] [--dataloader_pin_memory [DATALOADER_PIN_MEMORY]] [--no_dataloader_pin_memory] [--dataloader_persistent_workers [DATALOADER_PERSISTENT_WORKERS]] [--skip_memory_metrics [SKIP_MEMORY_METRICS]] [--no_skip_memory_metrics] [--use_legacy_prediction_loop [USE_LEGACY_PREDICTION_LOOP]] [--push_to_hub [PUSH_TO_HUB]] [--resume_from_checkpoint RESUME_FROM_CHECKPOINT] [--hub_model_id HUB_MODEL_ID] [--hub_strategy {end,every_save,checkpoint,all_checkpoints}] [--hub_token HUB_TOKEN] [--hub_private_repo [HUB_PRIVATE_REPO]] [--hub_always_push [HUB_ALWAYS_PUSH]] [--gradient_checkpointing [GRADIENT_CHECKPOINTING]] [--gradient_checkpointing_kwargs GRADIENT_CHECKPOINTING_KWARGS] [--include_inputs_for_metrics [INCLUDE_INPUTS_FOR_METRICS]] [--eval_do_concat_batches [EVAL_DO_CONCAT_BATCHES]] [--no_eval_do_concat_batches] [--fp16_backend {auto,apex,cpu_amp}] [--evaluation_strategy {no,steps,epoch}] [--push_to_hub_model_id PUSH_TO_HUB_MODEL_ID] [--push_to_hub_organization PUSH_TO_HUB_ORGANIZATION] [--push_to_hub_token PUSH_TO_HUB_TOKEN] [--mp_parameters MP_PARAMETERS] [--auto_find_batch_size [AUTO_FIND_BATCH_SIZE]] [--full_determinism [FULL_DETERMINISM]] [--torchdynamo TORCHDYNAMO] [--ray_scope RAY_SCOPE] [--ddp_timeout DDP_TIMEOUT] [--torch_compile [TORCH_COMPILE]] [--torch_compile_backend TORCH_COMPILE_BACKEND] [--torch_compile_mode TORCH_COMPILE_MODE] [--dispatch_batches DISPATCH_BATCHES] [--split_batches SPLIT_BATCHES] [--include_tokens_per_second [INCLUDE_TOKENS_PER_SECOND]] [--include_num_input_tokens_seen [INCLUDE_NUM_INPUT_TOKENS_SEEN]] [--neftune_noise_alpha NEFTUNE_NOISE_ALPHA] [--optim_target_modules OPTIM_TARGET_MODULES] [--batch_eval_metrics [BATCH_EVAL_METRICS]] [--eval_on_start [EVAL_ON_START]] [--eval_use_gather_object [EVAL_USE_GATHER_OBJECT]] [--sortish_sampler [SORTISH_SAMPLER]] [--predict_with_generate [PREDICT_WITH_GENERATE]] [--generation_max_length GENERATION_MAX_LENGTH] [--generation_num_beams GENERATION_NUM_BEAMS] [--generation_config GENERATION_CONFIG] [--use_badam [USE_BADAM]] [--badam_mode {layer,ratio}] [--badam_start_block BADAM_START_BLOCK] [--badam_switch_mode {ascending,descending,random,fixed}] [--badam_switch_interval BADAM_SWITCH_INTERVAL] [--badam_update_ratio BADAM_UPDATE_RATIO] [--badam_mask_mode {adjacent,scatter}] [--badam_verbose BADAM_VERBOSE] [--use_galore [USE_GALORE]] [--galore_target GALORE_TARGET] [--galore_rank GALORE_RANK] [--galore_update_interval GALORE_UPDATE_INTERVAL] [--galore_scale GALORE_SCALE] [--galore_proj_type {std,reverse_std,right,left,full}] [--galore_layerwise [GALORE_LAYERWISE]] [--pref_beta PREF_BETA] [--pref_ftx PREF_FTX] [--pref_loss {sigmoid,hinge,ipo,kto_pair,orpo,simpo}] [--dpo_label_smoothing DPO_LABEL_SMOOTHING] [--kto_chosen_weight KTO_CHOSEN_WEIGHT] [--kto_rejected_weight KTO_REJECTED_WEIGHT] [--simpo_gamma SIMPO_GAMMA] [--ppo_buffer_size PPO_BUFFER_SIZE] [--ppo_epochs PPO_EPOCHS] [--ppo_score_norm [PPO_SCORE_NORM]] [--ppo_target PPO_TARGET] [--ppo_whiten_rewards [PPO_WHITEN_REWARDS]] [--ref_model REF_MODEL] [--ref_model_adapters REF_MODEL_ADAPTERS] [--ref_model_quantization_bit REF_MODEL_QUANTIZATION_BIT] [--reward_model REWARD_MODEL] [--reward_model_adapters REWARD_MODEL_ADAPTERS] [--reward_model_quantization_bit REWARD_MODEL_QUANTIZATION_BIT] [--reward_model_type {lora,full,api}] [--additional_target ADDITIONAL_TARGET] [--lora_alpha LORA_ALPHA] [--lora_dropout LORA_DROPOUT] [--lora_rank LORA_RANK] [--lora_target LORA_TARGET] [--loraplus_lr_ratio LORAPLUS_LR_RATIO] [--loraplus_lr_embedding LORAPLUS_LR_EMBEDDING] [--use_rslora [USE_RSLORA]] [--use_dora [USE_DORA]] [--pissa_init [PISSA_INIT]] [--pissa_iter PISSA_ITER] [--pissa_convert [PISSA_CONVERT]] [--create_new_adapter [CREATE_NEW_ADAPTER]] [--freeze_trainable_layers FREEZE_TRAINABLE_LAYERS] [--freeze_trainable_modules FREEZE_TRAINABLE_MODULES] [--freeze_extra_modules FREEZE_EXTRA_MODULES] [--pure_bf16 [PURE_BF16]] [--stage {pt,sft,rm,ppo,dpo,kto}] [--finetuning_type {lora,freeze,full}] [--use_llama_pro [USE_LLAMA_PRO]] [--use_adam_mini [USE_ADAM_MINI]] [--freeze_vision_tower [FREEZE_VISION_TOWER]] [--no_freeze_vision_tower] [--train_mm_proj_only [TRAIN_MM_PROJ_ONLY]] [--compute_accuracy [COMPUTE_ACCURACY]] [--plot_loss [PLOT_LOSS]] [--do_sample [DO_SAMPLE]] [--no_do_sample] [--temperature TEMPERATURE] [--top_p TOP_P] [--top_k TOP_K] [--num_beams NUM_BEAMS] [--max_length MAX_LENGTH] [--max_new_tokens MAX_NEW_TOKENS] [--repetition_penalty REPETITION_PENALTY] [--length_penalty LENGTH_PENALTY] [--default_system DEFAULT_SYSTEM] optional arguments: -h, --help show this help message and exit --model_name_or_path MODEL_NAME_OR_PATH Path to the model weight or identifier from huggingface.co/models or modelscope.cn/models. (default: None) --adapter_name_or_path ADAPTER_NAME_OR_PATH Path to the adapter weight or identifier from huggingface.co/models. Use commas to separate multiple adapters. (default: None) --adapter_folder ADAPTER_FOLDER The folder containing the adapter weights to load. (default: None) --cache_dir CACHE_DIR Where to store the pre-trained models downloaded from huggingface.co or modelscope.cn. (default: None) --use_fast_tokenizer [USE_FAST_TOKENIZER] Whether or not to use one of the fast tokenizer (backed by the tokenizers library). (default: True) --no_use_fast_tokenizer Whether or not to use one of the fast tokenizer (backed by the tokenizers library). (default: False) --resize_vocab [RESIZE_VOCAB] Whether or not to resize the tokenizer vocab and the embedding layers. (default: False) --split_special_tokens [SPLIT_SPECIAL_TOKENS] Whether or not the special tokens should be split during the tokenization process. (default: False) --new_special_tokens NEW_SPECIAL_TOKENS Special tokens to be added into the tokenizer. Use commas to separate multiple tokens. (default: None) --model_revision MODEL_REVISION The specific model version to use (can be a branch name, tag name or commit id). (default: main) --low_cpu_mem_usage [LOW_CPU_MEM_USAGE] Whether or not to use memory-efficient model loading. (default: True) --no_low_cpu_mem_usage Whether or not to use memory-efficient model loading. (default: False) --quantization_method {bitsandbytes,hqq,eetq} Quantization method to use for on-the-fly quantization. (default: bitsandbytes) --quantization_bit QUANTIZATION_BIT The number of bits to quantize the model using bitsandbytes. (default: None) --quantization_type {fp4,nf4} Quantization data type to use in int4 training. (default: nf4) --double_quantization [DOUBLE_QUANTIZATION] Whether or not to use double quantization in int4 training. (default: True) --no_double_quantization Whether or not to use double quantization in int4 training. (default: False) --quantization_device_map {auto} Device map used to infer the 4-bit quantized model, needs bitsandbytes>=0.43.0. (default: None) --rope_scaling {linear,dynamic} Which scaling strategy should be adopted for the RoPE embeddings. (default: None) --flash_attn {auto,disabled,sdpa,fa2} Enable FlashAttention for faster training and inference. (default: auto) --shift_attn [SHIFT_ATTN] Enable shift short attention (S^2-Attn) proposed by LongLoRA. (default: False) --mixture_of_depths {convert,load} Convert the model to mixture-of-depths (MoD) or load the MoD model. (default: None) --use_unsloth [USE_UNSLOTH] Whether or not to use unsloth's optimization for the LoRA training. (default: False) --visual_inputs [VISUAL_INPUTS] Whethor or not to use multimodal LLM that accepts visual inputs. (default: False) --moe_aux_loss_coef MOE_AUX_LOSS_COEF Coefficient of the auxiliary router loss in mixture- of-experts model. (default: None) --disable_gradient_checkpointing [DISABLE_GRADIENT_CHECKPOINTING] Whether or not to disable gradient checkpointing. (default: False) --upcast_layernorm [UPCAST_LAYERNORM] Whether or not to upcast the layernorm weights in fp32. (default: False) --upcast_lmhead_output [UPCAST_LMHEAD_OUTPUT] Whether or not to upcast the output of lm_head in fp32. (default: False) --train_from_scratch [TRAIN_FROM_SCRATCH] Whether or not to randomly initialize the model weights. (default: False) --infer_backend {huggingface,vllm} Backend engine used at inference. (default: huggingface) --vllm_maxlen VLLM_MAXLEN Maximum sequence (prompt + response) length of the vLLM engine. (default: 2048) --vllm_gpu_util VLLM_GPU_UTIL The fraction of GPU memory in (0,1) to be used for the vLLM engine. (default: 0.9) --vllm_enforce_eager [VLLM_ENFORCE_EAGER] Whether or not to disable CUDA graph in the vLLM engine. (default: False) --vllm_max_lora_rank VLLM_MAX_LORA_RANK Maximum rank of all LoRAs in the vLLM engine. (default: 32) --offload_folder OFFLOAD_FOLDER Path to offload model weights. (default: offload) --use_cache [USE_CACHE] Whether or not to use KV cache in generation. (default: True) --no_use_cache Whether or not to use KV cache in generation. (default: False) --infer_dtype {auto,float16,bfloat16,float32} Data type for model weights and activations at inference. (default: auto) --hf_hub_token HF_HUB_TOKEN Auth token to log in with Hugging Face Hub. (default: None) --ms_hub_token MS_HUB_TOKEN Auth token to log in with ModelScope Hub. (default: None) --export_dir EXPORT_DIR Path to the directory to save the exported model. (default: None) --export_size EXPORT_SIZE The file shard size (in GB) of the exported model. (default: 1) --export_device {cpu,auto} The device used in model export, use `auto` to accelerate exporting. (default: cpu) --export_quantization_bit EXPORT_QUANTIZATION_BIT The number of bits to quantize the exported model. (default: None) --export_quantization_dataset EXPORT_QUANTIZATION_DATASET Path to the dataset or dataset name to use in quantizing the exported model. (default: None) --export_quantization_nsamples EXPORT_QUANTIZATION_NSAMPLES The number of samples used for quantization. (default: 128) --export_quantization_maxlen EXPORT_QUANTIZATION_MAXLEN The maximum length of the model inputs used for quantization. (default: 1024) --export_legacy_format [EXPORT_LEGACY_FORMAT] Whether or not to save the `.bin` files instead of `.safetensors`. (default: False) --export_hub_model_id EXPORT_HUB_MODEL_ID The name of the repository if push the model to the Hugging Face hub. (default: None) --print_param_status [PRINT_PARAM_STATUS] For debugging purposes, print the status of the parameters in the model. (default: False) --template TEMPLATE Which template to use for constructing prompts in training and inference. (default: None) --dataset DATASET The name of dataset(s) to use for training. Use commas to separate multiple datasets. (default: None) --eval_dataset EVAL_DATASET The name of dataset(s) to use for evaluation. Use commas to separate multiple datasets. (default: None) --dataset_dir DATASET_DIR Path to the folder containing the datasets. (default: data) --cutoff_len CUTOFF_LEN The cutoff length of the tokenized inputs in the dataset. (default: 1024) --train_on_prompt [TRAIN_ON_PROMPT] Whether or not to disable the mask on the prompt. (default: False) --mask_history [MASK_HISTORY] Whether or not to mask the history and train on the last turn only. (default: False) --streaming [STREAMING] Enable dataset streaming. (default: False) --buffer_size BUFFER_SIZE Size of the buffer to randomly sample examples from in dataset streaming. (default: 16384) --mix_strategy {concat,interleave_under,interleave_over} Strategy to use in dataset mixing (concat/interleave) (undersampling/oversampling). (default: concat) --interleave_probs INTERLEAVE_PROBS Probabilities to sample data from datasets. Use commas to separate multiple datasets. (default: None) --overwrite_cache [OVERWRITE_CACHE] Overwrite the cached training and evaluation sets. (default: False) --preprocessing_num_workers PREPROCESSING_NUM_WORKERS The number of processes to use for the pre-processing. (default: None) --max_samples MAX_SAMPLES For debugging purposes, truncate the number of examples for each dataset. (default: None) --eval_num_beams EVAL_NUM_BEAMS Number of beams to use for evaluation. This argument will be passed to `model.generate` (default: None) --ignore_pad_token_for_loss [IGNORE_PAD_TOKEN_FOR_LOSS] Whether or not to ignore the tokens corresponding to the pad label in loss computation. (default: True) --no_ignore_pad_token_for_loss Whether or not to ignore the tokens corresponding to the pad label in loss computation. (default: False) --val_size VAL_SIZE Size of the development set, should be an integer or a float in range `[0,1)`. (default: 0.0) --packing PACKING Enable sequences packing in training. Will automatically enable in pre-training. (default: None) --neat_packing [NEAT_PACKING] Enable sequence packing without cross-attention. (default: False) --tool_format TOOL_FORMAT Tool format to use for constructing function calling examples. (default: None) --tokenized_path TOKENIZED_PATH Path to save or load the tokenized datasets. (default: None) --output_dir OUTPUT_DIR The output directory where the model predictions and checkpoints will be written. (default: None) --overwrite_output_dir [OVERWRITE_OUTPUT_DIR] Overwrite the content of the output directory. Use this to continue training if output_dir points to a checkpoint directory. (default: False) --do_train [DO_TRAIN] Whether to run training. (default: False) --do_eval [DO_EVAL] Whether to run eval on the dev set. (default: False) --do_predict [DO_PREDICT] Whether to run predictions on the test set. (default: False) --eval_strategy {no,steps,epoch} The evaluation strategy to use. (default: no) --prediction_loss_only [PREDICTION_LOSS_ONLY] When performing evaluation and predictions, only returns the loss. (default: False) --per_device_train_batch_size PER_DEVICE_TRAIN_BATCH_SIZE Batch size per GPU/TPU/MPS/NPU core/CPU for training. (default: 8) --per_device_eval_batch_size PER_DEVICE_EVAL_BATCH_SIZE Batch size per GPU/TPU/MPS/NPU core/CPU for evaluation. (default: 8) --per_gpu_train_batch_size PER_GPU_TRAIN_BATCH_SIZE Deprecated, the use of `--per_device_train_batch_size` is preferred. Batch size per GPU/TPU core/CPU for training. (default: None) --per_gpu_eval_batch_size PER_GPU_EVAL_BATCH_SIZE Deprecated, the use of `--per_device_eval_batch_size` is preferred. Batch size per GPU/TPU core/CPU for evaluation. (default: None) --gradient_accumulation_steps GRADIENT_ACCUMULATION_STEPS Number of updates steps to accumulate before performing a backward/update pass. (default: 1) --eval_accumulation_steps EVAL_ACCUMULATION_STEPS Number of predictions steps to accumulate before moving the tensors to the CPU. (default: None) --eval_delay EVAL_DELAY Number of epochs or steps to wait for before the first evaluation can be performed, depending on the eval_strategy. (default: 0) --torch_empty_cache_steps TORCH_EMPTY_CACHE_STEPS Number of steps to wait before calling `torch..empty_cache()`.This can help avoid CUDA out-of-memory errors by lowering peak VRAM usage at a cost of about [10{'option_strings': ['-- torch_empty_cache_steps'], 'dest': 'torch_empty_cache_steps', 'nargs': None, 'const': None, 'default': None, 'type': 'int', 'choices': None, 'required': False, 'help': 'Number of steps to wait before calling `torch..empty_cache()`.This can help avoid CUDA out-of-memory errors by lowering peak VRAM usage at a cost of about [10% slower performance] (https://github.com/huggingface/transformers/issues/31 372).If left unset or set to None, cache will not be emptied.', 'metavar': None, 'container': , 'prog': 'launcher.py'}lower performance](https://githu b.com/huggingface/transformers/issues/31372).If left unset or set to None, cache will not be emptied. (default: None) --learning_rate LEARNING_RATE The initial learning rate for AdamW. (default: 5e-05) --weight_decay WEIGHT_DECAY Weight decay for AdamW if we apply some. (default: 0.0) --adam_beta1 ADAM_BETA1 Beta1 for AdamW optimizer (default: 0.9) --adam_beta2 ADAM_BETA2 Beta2 for AdamW optimizer (default: 0.999) --adam_epsilon ADAM_EPSILON Epsilon for AdamW optimizer. (default: 1e-08) --max_grad_norm MAX_GRAD_NORM Max gradient norm. (default: 1.0) --num_train_epochs NUM_TRAIN_EPOCHS Total number of training epochs to perform. (default: 3.0) --max_steps MAX_STEPS If > 0: set total number of training steps to perform. Override num_train_epochs. (default: -1) --lr_scheduler_type {linear,cosine,cosine_with_restarts,polynomial,constant,constant_with_warmup,inverse_sqrt,reduce_lr_on_plateau,cosine_with_min_lr,warmup_stable_decay} The scheduler type to use. (default: linear) --lr_scheduler_kwargs LR_SCHEDULER_KWARGS Extra parameters for the lr_scheduler such as {'num_cycles': 1} for the cosine with hard restarts. (default: {}) --warmup_ratio WARMUP_RATIO Linear warmup over warmup_ratio fraction of total steps. (default: 0.0) --warmup_steps WARMUP_STEPS Linear warmup over warmup_steps. (default: 0) --log_level {detail,debug,info,warning,error,critical,passive} Logger log level to use on the main node. Possible choices are the log levels as strings: 'debug', 'info', 'warning', 'error' and 'critical', plus a 'passive' level which doesn't set anything and lets the application set the level. Defaults to 'passive'. (default: passive) --log_level_replica {detail,debug,info,warning,error,critical,passive} Logger log level to use on replica nodes. Same choices and defaults as ``log_level`` (default: warning) --log_on_each_node [LOG_ON_EACH_NODE] When doing a multinode distributed training, whether to log once per node or just once on the main node. (default: True) --no_log_on_each_node When doing a multinode distributed training, whether to log once per node or just once on the main node. (default: False) --logging_dir LOGGING_DIR Tensorboard log dir. (default: None) --logging_strategy {no,steps,epoch} The logging strategy to use. (default: steps) --logging_first_step [LOGGING_FIRST_STEP] Log the first global_step (default: False) --logging_steps LOGGING_STEPS Log every X updates steps. Should be an integer or a float in range `[0,1)`. If smaller than 1, will be interpreted as ratio of total training steps. (default: 500) --logging_nan_inf_filter [LOGGING_NAN_INF_FILTER] Filter nan and inf losses for logging. (default: True) --no_logging_nan_inf_filter Filter nan and inf losses for logging. (default: False) --save_strategy {no,steps,epoch} The checkpoint save strategy to use. (default: steps) --save_steps SAVE_STEPS Save checkpoint every X updates steps. Should be an integer or a float in range `[0,1)`. If smaller than 1, will be interpreted as ratio of total training steps. (default: 500) --save_total_limit SAVE_TOTAL_LIMIT If a value is passed, will limit the total amount of checkpoints. Deletes the older checkpoints in `output_dir`. When `load_best_model_at_end` is enabled, the 'best' checkpoint according to `metric_for_best_model` will always be retained in addition to the most recent ones. For example, for `save_total_limit=5` and `load_best_model_at_end=True`, the four last checkpoints will always be retained alongside the best model. When `save_total_limit=1` and `load_best_model_at_end=True`, it is possible that two checkpoints are saved: the last one and the best one (if they are different). Default is unlimited checkpoints (default: None) --save_safetensors [SAVE_SAFETENSORS] Use safetensors saving and loading for state dicts instead of default torch.load and torch.save. (default: True) --no_save_safetensors Use safetensors saving and loading for state dicts instead of default torch.load and torch.save. (default: False) --save_on_each_node [SAVE_ON_EACH_NODE] When doing multi-node distributed training, whether to save models and checkpoints on each node, or only on the main one (default: False) --save_only_model [SAVE_ONLY_MODEL] When checkpointing, whether to only save the model, or also the optimizer, scheduler & rng state.Note that when this is true, you won't be able to resume training from checkpoint.This enables you to save storage by not storing the optimizer, scheduler & rng state.You can only load the model using from_pretrained with this option set to True. (default: False) --restore_callback_states_from_checkpoint [RESTORE_CALLBACK_STATES_FROM_CHECKPOINT] Whether to restore the callback states from the checkpoint. If `True`, will override callbacks passed to the `Trainer` if they exist in the checkpoint. (default: False) --no_cuda [NO_CUDA] This argument is deprecated. It will be removed in version 5.0 of 🤗 Transformers. (default: False) --use_cpu [USE_CPU] Whether or not to use cpu. If set to False, we will use cuda/tpu/mps/npu device if available. (default: False) --use_mps_device [USE_MPS_DEVICE] This argument is deprecated. `mps` device will be used if available similar to `cuda` device. It will be removed in version 5.0 of 🤗 Transformers (default: False) --seed SEED Random seed that will be set at the beginning of training. (default: 42) --data_seed DATA_SEED Random seed to be used with data samplers. (default: None) --jit_mode_eval [JIT_MODE_EVAL] Whether or not to use PyTorch jit trace for inference (default: False) --use_ipex [USE_IPEX] Use Intel extension for PyTorch when it is available, installation: 'https://github.com/intel/intel- extension-for-pytorch' (default: False) --bf16 [BF16] Whether to use bf16 (mixed) precision instead of 32-bit. Requires Ampere or higher NVIDIA architecture or using CPU (use_cpu) or Ascend NPU. This is an experimental API and it may change. (default: False) --fp16 [FP16] Whether to use fp16 (mixed) precision instead of 32-bit (default: False) --fp16_opt_level FP16_OPT_LEVEL For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']. See details at https://nvidia.github.io/apex/amp.html (default: O1) --half_precision_backend {auto,apex,cpu_amp} The backend to be used for half precision. (default: auto) --bf16_full_eval [BF16_FULL_EVAL] Whether to use full bfloat16 evaluation instead of 32-bit. This is an experimental API and it may change. (default: False) --fp16_full_eval [FP16_FULL_EVAL] Whether to use full float16 evaluation instead of 32-bit (default: False) --tf32 TF32 Whether to enable tf32 mode, available in Ampere and newer GPU architectures. This is an experimental API and it may change. (default: None) --local_rank LOCAL_RANK For distributed training: local_rank (default: -1) --ddp_backend {nccl,gloo,mpi,ccl,hccl,cncl} The backend to be used for distributed training (default: None) --tpu_num_cores TPU_NUM_CORES TPU: Number of TPU cores (automatically passed by launcher script) (default: None) --tpu_metrics_debug [TPU_METRICS_DEBUG] Deprecated, the use of `--debug tpu_metrics_debug` is preferred. TPU: Whether to print debug metrics (default: False) --debug DEBUG [DEBUG ...] Whether or not to enable debug mode. Current options: `underflow_overflow` (Detect underflow and overflow in activations and weights), `tpu_metrics_debug` (print debug metrics on TPU). (default: None) --dataloader_drop_last [DATALOADER_DROP_LAST] Drop the last incomplete batch if it is not divisible by the batch size. (default: False) --eval_steps EVAL_STEPS Run an evaluation every X steps. Should be an integer or a float in range `[0,1)`. If smaller than 1, will be interpreted as ratio of total training steps. (default: None) --dataloader_num_workers DATALOADER_NUM_WORKERS Number of subprocesses to use for data loading (PyTorch only). 0 means that the data will be loaded in the main process. (default: 0) --dataloader_prefetch_factor DATALOADER_PREFETCH_FACTOR Number of batches loaded in advance by each worker. 2 means there will be a total of 2 * num_workers batches prefetched across all workers. Default is 2 for PyTorch < 2.0.0 and otherwise None. (default: None) --past_index PAST_INDEX If >=0, uses the corresponding part of the output as the past state for next step. (default: -1) --run_name RUN_NAME An optional descriptor for the run. Notably used for wandb, mlflow and comet logging. (default: None) --disable_tqdm DISABLE_TQDM Whether or not to disable the tqdm progress bars. (default: None) --remove_unused_columns [REMOVE_UNUSED_COLUMNS] Remove columns not required by the model when using an nlp.Dataset. (default: True) --no_remove_unused_columns Remove columns not required by the model when using an nlp.Dataset. (default: False) --label_names LABEL_NAMES [LABEL_NAMES ...] The list of keys in your dictionary of inputs that correspond to the labels. (default: None) --load_best_model_at_end [LOAD_BEST_MODEL_AT_END] Whether or not to load the best model found during training at the end of training. When this option is enabled, the best checkpoint will always be saved. See `save_total_limit` for more. (default: False) --metric_for_best_model METRIC_FOR_BEST_MODEL The metric to use to compare two different models. (default: None) --greater_is_better GREATER_IS_BETTER Whether the `metric_for_best_model` should be maximized or not. (default: None) --ignore_data_skip [IGNORE_DATA_SKIP] When resuming training, whether or not to skip the first epochs and batches to get to the same training data. (default: False) --fsdp FSDP Whether or not to use PyTorch Fully Sharded Data Parallel (FSDP) training (in distributed training only). The base option should be `full_shard`, `shard_grad_op` or `no_shard` and you can add CPU- offload to `full_shard` or `shard_grad_op` like this: full_shard offload` or `shard_grad_op offload`. You can add auto-wrap to `full_shard` or `shard_grad_op` with the same syntax: full_shard auto_wrap` or `shard_grad_op auto_wrap`. (default: ) --fsdp_min_num_params FSDP_MIN_NUM_PARAMS This parameter is deprecated. FSDP's minimum number of parameters for Default Auto Wrapping. (useful only when `fsdp` field is passed). (default: 0) --fsdp_config FSDP_CONFIG Config to be used with FSDP (Pytorch Fully Sharded Data Parallel). The value is either a fsdp json config file (e.g., `fsdp_config.json`) or an already loaded json file as `dict`. (default: None) --fsdp_transformer_layer_cls_to_wrap FSDP_TRANSFORMER_LAYER_CLS_TO_WRAP This parameter is deprecated. Transformer layer class name (case-sensitive) to wrap, e.g, `BertLayer`, `GPTJBlock`, `T5Block` .... (useful only when `fsdp` flag is passed). (default: None) --accelerator_config ACCELERATOR_CONFIG Config to be used with the internal Accelerator object initializtion. The value is either a accelerator json config file (e.g., `accelerator_config.json`) or an already loaded json file as `dict`. (default: None) --deepspeed DEEPSPEED Enable deepspeed and pass the path to deepspeed json config file (e.g. `ds_config.json`) or an already loaded json file as a dict (default: None) --label_smoothing_factor LABEL_SMOOTHING_FACTOR The label smoothing epsilon to apply (zero means no label smoothing). (default: 0.0) --optim {adamw_hf,adamw_torch,adamw_torch_fused,adamw_torch_xla,adamw_torch_npu_fused,adamw_apex_fused,adafactor,adamw_anyprecision,sgd,adagrad,adamw_bnb_8bit,adamw_8bit,lion_8bit,lion_32bit,paged_adamw_32bit,paged_adamw_8bit,paged_lion_32bit,paged_lion_8bit,rmsprop,rmsprop_bnb,rmsprop_bnb_8bit,rmsprop_bnb_32bit,galore_adamw,galore_adamw_8bit,galore_adafactor,galore_adamw_layerwise,galore_adamw_8bit_layerwise,galore_adafactor_layerwise,lomo,adalomo} The optimizer to use. (default: adamw_torch) --optim_args OPTIM_ARGS Optional arguments to supply to optimizer. (default: None) --adafactor [ADAFACTOR] Whether or not to replace AdamW by Adafactor. (default: False) --group_by_length [GROUP_BY_LENGTH] Whether or not to group samples of roughly the same length together when batching. (default: False) --length_column_name LENGTH_COLUMN_NAME Column name with precomputed lengths to use when grouping by length. (default: length) --report_to REPORT_TO The list of integrations to report the results and logs to. (default: None) --ddp_find_unused_parameters DDP_FIND_UNUSED_PARAMETERS When using distributed training, the value of the flag `find_unused_parameters` passed to `DistributedDataParallel`. (default: None) --ddp_bucket_cap_mb DDP_BUCKET_CAP_MB When using distributed training, the value of the flag `bucket_cap_mb` passed to `DistributedDataParallel`. (default: None) --ddp_broadcast_buffers DDP_BROADCAST_BUFFERS When using distributed training, the value of the flag `broadcast_buffers` passed to `DistributedDataParallel`. (default: None) --dataloader_pin_memory [DATALOADER_PIN_MEMORY] Whether or not to pin memory for DataLoader. (default: True) --no_dataloader_pin_memory Whether or not to pin memory for DataLoader. (default: False) --dataloader_persistent_workers [DATALOADER_PERSISTENT_WORKERS] If True, the data loader will not shut down the worker processes after a dataset has been consumed once. This allows to maintain the workers Dataset instances alive. Can potentially speed up training, but will increase RAM usage. (default: False) --skip_memory_metrics [SKIP_MEMORY_METRICS] Whether or not to skip adding of memory profiler reports to metrics. (default: True) --no_skip_memory_metrics Whether or not to skip adding of memory profiler reports to metrics. (default: False) --use_legacy_prediction_loop [USE_LEGACY_PREDICTION_LOOP] Whether or not to use the legacy prediction_loop in the Trainer. (default: False) --push_to_hub [PUSH_TO_HUB] Whether or not to upload the trained model to the model hub after training. (default: False) --resume_from_checkpoint RESUME_FROM_CHECKPOINT The path to a folder with a valid checkpoint for your model. (default: None) --hub_model_id HUB_MODEL_ID The name of the repository to keep in sync with the local `output_dir`. (default: None) --hub_strategy {end,every_save,checkpoint,all_checkpoints} The hub strategy to use when `--push_to_hub` is activated. (default: every_save) --hub_token HUB_TOKEN The token to use to push to the Model Hub. (default: None) --hub_private_repo [HUB_PRIVATE_REPO] Whether the model repository is private or not. (default: False) --hub_always_push [HUB_ALWAYS_PUSH] Unless `True`, the Trainer will skip pushes if the previous one wasn't finished yet. (default: False) --gradient_checkpointing [GRADIENT_CHECKPOINTING] If True, use gradient checkpointing to save memory at the expense of slower backward pass. (default: False) --gradient_checkpointing_kwargs GRADIENT_CHECKPOINTING_KWARGS Gradient checkpointing key word arguments such as `use_reentrant`. Will be passed to `torch.utils.checkpoint.checkpoint` through `model.gradient_checkpointing_enable`. (default: None) --include_inputs_for_metrics [INCLUDE_INPUTS_FOR_METRICS] Whether or not the inputs will be passed to the `compute_metrics` function. (default: False) --eval_do_concat_batches [EVAL_DO_CONCAT_BATCHES] Whether to recursively concat inputs/losses/labels/predictions across batches. If `False`, will instead store them as lists, with each batch kept separate. (default: True) --no_eval_do_concat_batches Whether to recursively concat inputs/losses/labels/predictions across batches. If `False`, will instead store them as lists, with each batch kept separate. (default: False) --fp16_backend {auto,apex,cpu_amp} Deprecated. Use half_precision_backend instead (default: auto) --evaluation_strategy {no,steps,epoch} Deprecated. Use `eval_strategy` instead (default: None) --push_to_hub_model_id PUSH_TO_HUB_MODEL_ID The name of the repository to which push the `Trainer`. (default: None) --push_to_hub_organization PUSH_TO_HUB_ORGANIZATION The name of the organization in with to which push the `Trainer`. (default: None) --push_to_hub_token PUSH_TO_HUB_TOKEN The token to use to push to the Model Hub. (default: None) --mp_parameters MP_PARAMETERS Used by the SageMaker launcher to send mp-specific args. Ignored in Trainer (default: ) --auto_find_batch_size [AUTO_FIND_BATCH_SIZE] Whether to automatically decrease the batch size in half and rerun the training loop again each time a CUDA Out-of-Memory was reached (default: False) --full_determinism [FULL_DETERMINISM] Whether to call enable_full_determinism instead of set_seed for reproducibility in distributed training. Important: this will negatively impact the performance, so only use it for debugging. (default: False) --torchdynamo TORCHDYNAMO This argument is deprecated, use `--torch_compile_backend` instead. (default: None) --ray_scope RAY_SCOPE The scope to use when doing hyperparameter search with Ray. By default, `"last"` will be used. Ray will then use the last checkpoint of all trials, compare those, and select the best one. However, other options are also available. See the Ray documentation (https://doc s.ray.io/en/latest/tune/api_docs/analysis.html#ray.tun e.ExperimentAnalysis.get_best_trial) for more options. (default: last) --ddp_timeout DDP_TIMEOUT Overrides the default timeout for distributed training (value should be given in seconds). (default: 1800) --torch_compile [TORCH_COMPILE] If set to `True`, the model will be wrapped in `torch.compile`. (default: False) --torch_compile_backend TORCH_COMPILE_BACKEND Which backend to use with `torch.compile`, passing one will trigger a model compilation. (default: None) --torch_compile_mode TORCH_COMPILE_MODE Which mode to use with `torch.compile`, passing one will trigger a model compilation. (default: None) --dispatch_batches DISPATCH_BATCHES Deprecated. Pass {'dispatch_batches':VALUE} to `accelerator_config`. (default: None) --split_batches SPLIT_BATCHES Deprecated. Pass {'split_batches':True} to `accelerator_config`. (default: None) --include_tokens_per_second [INCLUDE_TOKENS_PER_SECOND] If set to `True`, the speed metrics will include `tgs` (tokens per second per device). (default: False) --include_num_input_tokens_seen [INCLUDE_NUM_INPUT_TOKENS_SEEN] If set to `True`, will track the number of input tokens seen throughout training. (May be slower in distributed training) (default: False) --neftune_noise_alpha NEFTUNE_NOISE_ALPHA Activates neftune noise embeddings into the model. NEFTune has been proven to drastically improve model performances for instrcution fine-tuning. Check out the original paper here: https://arxiv.org/abs/2310.05914 and the original code here: https://github.com/neelsjain/NEFTune. Only supported for `PreTrainedModel` and `PeftModel` classes. (default: None) --optim_target_modules OPTIM_TARGET_MODULES Target modules for the optimizer defined in the `optim` argument. Only used for the GaLore optimizer at the moment. (default: None) --batch_eval_metrics [BATCH_EVAL_METRICS] Break eval metrics calculation into batches to save memory. (default: False) --eval_on_start [EVAL_ON_START] Whether to run through the entire `evaluation` step at the very beginning of training as a sanity check. (default: False) --eval_use_gather_object [EVAL_USE_GATHER_OBJECT] Whether to run recursively gather object in a nested list/tuple/dictionary of objects from all devices. (default: False) --sortish_sampler [SORTISH_SAMPLER] Whether to use SortishSampler or not. (default: False) --predict_with_generate [PREDICT_WITH_GENERATE] Whether to use generate to calculate generative metrics (ROUGE, BLEU). (default: False) --generation_max_length GENERATION_MAX_LENGTH The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default to the `max_length` value of the model configuration. (default: None) --generation_num_beams GENERATION_NUM_BEAMS The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default to the `num_beams` value of the model configuration. (default: None) --generation_config GENERATION_CONFIG Model id, file path or url pointing to a GenerationConfig json file, to use during prediction. (default: None) --use_badam [USE_BADAM] Whether or not to use the BAdam optimizer. (default: False) --badam_mode {layer,ratio} Whether to use layer-wise or ratio-wise BAdam optimizer. (default: layer) --badam_start_block BADAM_START_BLOCK The starting block index for layer-wise BAdam. (default: None) --badam_switch_mode {ascending,descending,random,fixed} the strategy of picking block to update for layer-wise BAdam. (default: ascending) --badam_switch_interval BADAM_SWITCH_INTERVAL Number of steps to update the block for layer-wise BAdam. Use -1 to disable the block update. (default: 50) --badam_update_ratio BADAM_UPDATE_RATIO The ratio of the update for ratio-wise BAdam. (default: 0.05) --badam_mask_mode {adjacent,scatter} The mode of the mask for BAdam optimizer. `adjacent` means that the trainable parameters are adjacent to each other, `scatter` means that trainable parameters are randomly choosed from the weight. (default: adjacent) --badam_verbose BADAM_VERBOSE The verbosity level of BAdam optimizer. 0 for no print, 1 for print the block prefix, 2 for print trainable parameters. (default: 0) --use_galore [USE_GALORE] Whether or not to use the gradient low-Rank projection (GaLore). (default: False) --galore_target GALORE_TARGET Name(s) of modules to apply GaLore. Use commas to separate multiple modules. Use `all` to specify all the linear modules. (default: all) --galore_rank GALORE_RANK The rank of GaLore gradients. (default: 16) --galore_update_interval GALORE_UPDATE_INTERVAL Number of steps to update the GaLore projection. (default: 200) --galore_scale GALORE_SCALE GaLore scaling coefficient. (default: 0.25) --galore_proj_type {std,reverse_std,right,left,full} Type of GaLore projection. (default: std) --galore_layerwise [GALORE_LAYERWISE] Whether or not to enable layer-wise update to further save memory. (default: False) --pref_beta PREF_BETA The beta parameter in the preference loss. (default: 0.1) --pref_ftx PREF_FTX The supervised fine-tuning loss coefficient in DPO training. (default: 0.0) --pref_loss {sigmoid,hinge,ipo,kto_pair,orpo,simpo} The type of DPO loss to use. (default: sigmoid) --dpo_label_smoothing DPO_LABEL_SMOOTHING The robust DPO label smoothing parameter in cDPO that should be between 0 and 0.5. (default: 0.0) --kto_chosen_weight KTO_CHOSEN_WEIGHT The weight factor of the desirable losses in KTO training. (default: 1.0) --kto_rejected_weight KTO_REJECTED_WEIGHT The weight factor of the undesirable losses in KTO training. (default: 1.0) --simpo_gamma SIMPO_GAMMA The target reward margin term in SimPO loss. (default: 0.5) --ppo_buffer_size PPO_BUFFER_SIZE The number of mini-batches to make experience buffer in a PPO optimization step. (default: 1) --ppo_epochs PPO_EPOCHS The number of epochs to perform in a PPO optimization step. (default: 4) --ppo_score_norm [PPO_SCORE_NORM] Use score normalization in PPO training. (default: False) --ppo_target PPO_TARGET Target KL value for adaptive KL control in PPO training. (default: 6.0) --ppo_whiten_rewards [PPO_WHITEN_REWARDS] Whiten the rewards before compute advantages in PPO training. (default: False) --ref_model REF_MODEL Path to the reference model used for the PPO or DPO training. (default: None) --ref_model_adapters REF_MODEL_ADAPTERS Path to the adapters of the reference model. (default: None) --ref_model_quantization_bit REF_MODEL_QUANTIZATION_BIT The number of bits to quantize the reference model. (default: None) --reward_model REWARD_MODEL Path to the reward model used for the PPO training. (default: None) --reward_model_adapters REWARD_MODEL_ADAPTERS Path to the adapters of the reward model. (default: None) --reward_model_quantization_bit REWARD_MODEL_QUANTIZATION_BIT The number of bits to quantize the reward model. (default: None) --reward_model_type {lora,full,api} The type of the reward model in PPO training. Lora model only supports lora training. (default: lora) --additional_target ADDITIONAL_TARGET Name(s) of modules apart from LoRA layers to be set as trainable and saved in the final checkpoint. Use commas to separate multiple modules. (default: None) --lora_alpha LORA_ALPHA The scale factor for LoRA fine-tuning (default: lora_rank * 2). (default: None) --lora_dropout LORA_DROPOUT Dropout rate for the LoRA fine-tuning. (default: 0.0) --lora_rank LORA_RANK The intrinsic dimension for LoRA fine-tuning. (default: 8) --lora_target LORA_TARGET Name(s) of target modules to apply LoRA. Use commas to separate multiple modules. Use `all` to specify all the linear modules. (default: all) --loraplus_lr_ratio LORAPLUS_LR_RATIO LoRA plus learning rate ratio (lr_B / lr_A). (default: None) --loraplus_lr_embedding LORAPLUS_LR_EMBEDDING LoRA plus learning rate for lora embedding layers. (default: 1e-06) --use_rslora [USE_RSLORA] Whether or not to use the rank stabilization scaling factor for LoRA layer. (default: False) --use_dora [USE_DORA] Whether or not to use the weight-decomposed lora method (DoRA). (default: False) --pissa_init [PISSA_INIT] Whether or not to initialize a PiSSA adapter. (default: False) --pissa_iter PISSA_ITER The number of iteration steps performed by FSVD in PiSSA. Use -1 to disable it. (default: 16) --pissa_convert [PISSA_CONVERT] Whether or not to convert the PiSSA adapter to a normal LoRA adapter. (default: False) --create_new_adapter [CREATE_NEW_ADAPTER] Whether or not to create a new adapter with randomly initialized weight. (default: False) --freeze_trainable_layers FREEZE_TRAINABLE_LAYERS The number of trainable layers for freeze (partial- parameter) fine-tuning. Positive numbers mean the last n layers are set as trainable, negative numbers mean the first n layers are set as trainable. (default: 2) --freeze_trainable_modules FREEZE_TRAINABLE_MODULES Name(s) of trainable modules for freeze (partial- parameter) fine-tuning. Use commas to separate multiple modules. Use `all` to specify all the available modules. (default: all) --freeze_extra_modules FREEZE_EXTRA_MODULES Name(s) of modules apart from hidden layers to be set as trainable for freeze (partial-parameter) fine- tuning. Use commas to separate multiple modules. (default: None) --pure_bf16 [PURE_BF16] Whether or not to train model in purely bf16 precision (without AMP). (default: False) --stage {pt,sft,rm,ppo,dpo,kto} Which stage will be performed in training. (default: sft) --finetuning_type {lora,freeze,full} Which fine-tuning method to use. (default: lora) --use_llama_pro [USE_LLAMA_PRO] Whether or not to make only the parameters in the expanded blocks trainable. (default: False) --use_adam_mini [USE_ADAM_MINI] Whether or not to use the Adam-mini optimizer. (default: False) --freeze_vision_tower [FREEZE_VISION_TOWER] Whether ot not to freeze vision tower in MLLM training. (default: True) --no_freeze_vision_tower Whether ot not to freeze vision tower in MLLM training. (default: False) --train_mm_proj_only [TRAIN_MM_PROJ_ONLY] Whether or not to train the multimodal projector for MLLM only. (default: False) --compute_accuracy [COMPUTE_ACCURACY] Whether or not to compute the token-level accuracy at evaluation. (default: False) --plot_loss [PLOT_LOSS] Whether or not to save the training loss curves. (default: False) --do_sample [DO_SAMPLE] Whether or not to use sampling, use greedy decoding otherwise. (default: True) --no_do_sample Whether or not to use sampling, use greedy decoding otherwise. (default: False) --temperature TEMPERATURE The value used to modulate the next token probabilities. (default: 0.95) --top_p TOP_P The smallest set of most probable tokens with probabilities that add up to top_p or higher are kept. (default: 0.7) --top_k TOP_K The number of highest probability vocabulary tokens to keep for top-k filtering. (default: 50) --num_beams NUM_BEAMS Number of beams for beam search. 1 means no beam search. (default: 1) --max_length MAX_LENGTH The maximum length the generated tokens can have. It can be overridden by max_new_tokens. (default: 1024) --max_new_tokens MAX_NEW_TOKENS The maximum numbers of tokens to generate, ignoring the number of tokens in the prompt. (default: 1024) --repetition_penalty REPETITION_PENALTY The parameter for repetition penalty. 1.0 means no penalty. (default: 1.0) --length_penalty LENGTH_PENALTY Exponential penalty to the length that is used with beam-based generation. (default: 1.0) --default_system DEFAULT_SYSTEM Default system message to use in chat completion. (default: None)