forked from super_cognition/zhuoshi_llm_factory
df192611e3 | ||
---|---|---|
.. | ||
accelerate | ||
deepspeed | ||
extras | ||
full_multi_gpu | ||
inference | ||
lora_multi_gpu | ||
lora_single_gpu | ||
merge_lora | ||
qlora_single_gpu | ||
README.md | ||
README_zh.md |
README.md
We provide diverse examples about fine-tuning LLMs.
examples/
├── lora_single_gpu/
│ ├── pretrain.sh: Do continuous pre-training using LoRA
│ ├── sft.sh: Do supervised fine-tuning using LoRA
│ ├── reward.sh: Do reward modeling using LoRA
│ ├── ppo.sh: Do PPO training using LoRA
│ ├── dpo.sh: Do DPO training using LoRA
│ ├── orpo.sh: Do ORPO training using LoRA
│ ├── sft_mllm.sh: Do supervised fine-tuning on multimodal data using LoRA
│ ├── prepare.sh: Save tokenized dataset
│ └── predict.sh: Do batch predict and compute BLEU and ROUGE scores after LoRA tuning
├── qlora_single_gpu/
│ ├── bitsandbytes.sh: Fine-tune 4/8-bit BNB models using QLoRA
│ ├── gptq.sh: Fine-tune 4/8-bit GPTQ models using QLoRA
│ ├── awq.sh: Fine-tune 4-bit AWQ models using QLoRA
│ └── aqlm.sh: Fine-tune 2-bit AQLM models using QLoRA
├── lora_multi_gpu/
│ ├── single_node.sh: Fine-tune model with Accelerate on single node using LoRA
│ ├── multi_node.sh: Fine-tune model with Accelerate on multiple nodes using LoRA
│ └── ds_zero3.sh: Fine-tune model with DeepSpeed ZeRO-3 using LoRA (weight sharding)
├── full_multi_gpu/
│ ├── single_node.sh: Full fine-tune model with DeepSpeed on single node
│ ├── multi_node.sh: Full fine-tune model with DeepSpeed on multiple nodes
│ └── predict.sh: Do parallel batch predict and compute BLEU and ROUGE scores after full tuning
├── merge_lora/
│ ├── merge.sh: Merge LoRA weights into the pre-trained models
│ └── quantize.sh: Quantize the fine-tuned model with AutoGPTQ
├── inference/
│ ├── cli_demo.sh: Chat with fine-tuned model in the CLI with LoRA adapters
│ ├── api_demo.sh: Chat with fine-tuned model in an OpenAI-style API with LoRA adapters
│ ├── web_demo.sh: Chat with fine-tuned model in the Web browser with LoRA adapters
│ └── evaluate.sh: Evaluate model on the MMLU/CMMLU/C-Eval benchmarks with LoRA adapters
└── extras/
├── galore/
│ └── sft.sh: Fine-tune model with GaLore
├── badam/
│ └── sft.sh: Fine-tune model with BAdam
├── loraplus/
│ └── sft.sh: Fine-tune model using LoRA+
├── mod/
│ └── sft.sh: Fine-tune model using Mixture-of-Depths
├── llama_pro/
│ ├── expand.sh: Expand layers in the model
│ └── sft.sh: Fine-tune the expanded model
└── fsdp_qlora/
└── sft.sh: Fine-tune quantized model with FSDP+QLoRA