We provide diverse examples about fine-tuning LLMs. Make sure to execute these commands in the `LLaMA-Factory` directory. ## Table of Contents - [LoRA Fine-Tuning](#lora-fine-tuning) - [QLoRA Fine-Tuning](#qlora-fine-tuning) - [Full-Parameter Fine-Tuning](#full-parameter-fine-tuning) - [Merging LoRA Adapters and Quantization](#merging-lora-adapters-and-quantization) - [Inferring LoRA Fine-Tuned Models](#inferring-lora-fine-tuned-models) - [Extras](#extras) Use `CUDA_VISIBLE_DEVICES` (GPU) or `ASCEND_RT_VISIBLE_DEVICES` (NPU) to choose computing devices. ## Examples ### LoRA Fine-Tuning #### (Continuous) Pre-Training ```bash llamafactory-cli train examples/train_lora/llama3_lora_pretrain.yaml ``` #### Supervised Fine-Tuning ```bash llamafactory-cli train examples/train_lora/llama3_lora_sft.yaml ``` #### Multimodal Supervised Fine-Tuning ```bash llamafactory-cli train examples/train_lora/llava1_5_lora_sft.yaml ``` #### Reward Modeling ```bash llamafactory-cli train examples/train_lora/llama3_lora_reward.yaml ``` #### PPO Training ```bash llamafactory-cli train examples/train_lora/llama3_lora_ppo.yaml ``` #### DPO/ORPO/SimPO Training ```bash llamafactory-cli train examples/train_lora/llama3_lora_dpo.yaml ``` #### KTO Training ```bash llamafactory-cli train examples/train_lora/llama3_lora_kto.yaml ``` #### Preprocess Dataset It is useful for large dataset, use `tokenized_path` in config to load the preprocessed dataset. ```bash llamafactory-cli train examples/train_lora/llama3_preprocess.yaml ``` #### Evaluating on MMLU/CMMLU/C-Eval Benchmarks ```bash llamafactory-cli eval examples/train_lora/llama3_lora_eval.yaml ``` #### Batch Predicting and Computing BLEU and ROUGE Scores ```bash llamafactory-cli train examples/train_lora/llama3_lora_predict.yaml ``` #### Supervised Fine-Tuning on Multiple Nodes ```bash FORCE_TORCHRUN=1 NNODES=2 RANK=0 MASTER_ADDR=192.168.0.1 MASTER_PORT=29500 llamafactory-cli train examples/train_lora/llama3_lora_sft.yaml FORCE_TORCHRUN=1 NNODES=2 RANK=1 MASTER_ADDR=192.168.0.1 MASTER_PORT=29500 llamafactory-cli train examples/train_lora/llama3_lora_sft.yaml ``` #### Supervised Fine-Tuning with DeepSpeed ZeRO-3 (Weight Sharding) ```bash FORCE_TORCHRUN=1 llamafactory-cli train examples/train_lora/llama3_lora_sft_ds3.yaml ``` ### QLoRA Fine-Tuning #### Supervised Fine-Tuning with 4/8-bit Bitsandbytes/HQQ/EETQ Quantization (Recommended) ```bash llamafactory-cli train examples/train_qlora/llama3_lora_sft_otfq.yaml ``` #### Supervised Fine-Tuning with 4/8-bit GPTQ Quantization ```bash llamafactory-cli train examples/train_qlora/llama3_lora_sft_gptq.yaml ``` #### Supervised Fine-Tuning with 4-bit AWQ Quantization ```bash llamafactory-cli train examples/train_qlora/llama3_lora_sft_awq.yaml ``` #### Supervised Fine-Tuning with 2-bit AQLM Quantization ```bash llamafactory-cli train examples/train_qlora/llama3_lora_sft_aqlm.yaml ``` ### Full-Parameter Fine-Tuning #### Supervised Fine-Tuning on Single Node ```bash FORCE_TORCHRUN=1 llamafactory-cli train examples/train_full/llama3_full_sft_ds3.yaml ``` #### Supervised Fine-Tuning on Multiple Nodes ```bash FORCE_TORCHRUN=1 NNODES=2 RANK=0 MASTER_ADDR=192.168.0.1 MASTER_PORT=29500 llamafactory-cli train examples/train_full/llama3_full_sft_ds3.yaml FORCE_TORCHRUN=1 NNODES=2 RANK=1 MASTER_ADDR=192.168.0.1 MASTER_PORT=29500 llamafactory-cli train examples/train_full/llama3_full_sft_ds3.yaml ``` #### Batch Predicting and Computing BLEU and ROUGE Scores ```bash llamafactory-cli train examples/train_full/llama3_full_predict.yaml ``` ### Merging LoRA Adapters and Quantization #### Merge LoRA Adapters Note: DO NOT use quantized model or `quantization_bit` when merging LoRA adapters. ```bash llamafactory-cli export examples/merge_lora/llama3_lora_sft.yaml ``` #### Quantizing Model using AutoGPTQ ```bash llamafactory-cli export examples/merge_lora/llama3_gptq.yaml ``` ### Inferring LoRA Fine-Tuned Models #### Use CLI ```bash llamafactory-cli chat examples/inference/llama3_lora_sft.yaml ``` #### Use Web UI ```bash llamafactory-cli webchat examples/inference/llama3_lora_sft.yaml ``` #### Launch OpenAI-style API ```bash llamafactory-cli api examples/inference/llama3_lora_sft.yaml ``` ### Extras #### Full-Parameter Fine-Tuning using GaLore ```bash llamafactory-cli train examples/extras/galore/llama3_full_sft.yaml ``` #### Full-Parameter Fine-Tuning using BAdam ```bash llamafactory-cli train examples/extras/badam/llama3_full_sft.yaml ``` #### Full-Parameter Fine-Tuning using Adam-mini ```bash llamafactory-cli train examples/extras/adam_mini/qwen2_full_sft.yaml ``` #### LoRA+ Fine-Tuning ```bash llamafactory-cli train examples/extras/loraplus/llama3_lora_sft.yaml ``` #### PiSSA Fine-Tuning ```bash llamafactory-cli train examples/extras/pissa/llama3_lora_sft.yaml ``` #### Mixture-of-Depths Fine-Tuning ```bash llamafactory-cli train examples/extras/mod/llama3_full_sft.yaml ``` #### LLaMA-Pro Fine-Tuning ```bash bash examples/extras/llama_pro/expand.sh llamafactory-cli train examples/extras/llama_pro/llama3_freeze_sft.yaml ``` #### FSDP+QLoRA Fine-Tuning ```bash bash examples/extras/fsdp_qlora/train.sh ```