45 lines
2.2 KiB
Markdown
45 lines
2.2 KiB
Markdown
We provide diverse examples about fine-tuning LLMs.
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```
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examples/
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├── lora_single_gpu/
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│ ├── pretrain.sh: Do pre-training using LoRA
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│ ├── sft.sh: Do supervised fine-tuning using LoRA
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│ ├── reward.sh: Do reward modeling using LoRA
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│ ├── ppo.sh: Do PPO training using LoRA
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│ ├── dpo.sh: Do DPO training using LoRA
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│ ├── orpo.sh: Do ORPO training using LoRA
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│ ├── prepare.sh: Save tokenized dataset
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│ └── predict.sh: Do batch predict and compute BLEU and ROUGE scores after LoRA tuning
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├── qlora_single_gpu/
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│ ├── bitsandbytes.sh: Fine-tune 4/8-bit BNB models using QLoRA
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│ ├── gptq.sh: Fine-tune 4/8-bit GPTQ models using QLoRA
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│ ├── awq.sh: Fine-tune 4-bit AWQ models using QLoRA
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│ └── aqlm.sh: Fine-tune 2-bit AQLM models using QLoRA
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├── lora_multi_gpu/
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│ ├── single_node.sh: Fine-tune model with Accelerate on single node using LoRA
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│ └── multi_node.sh: Fine-tune model with Accelerate on multiple nodes using LoRA
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├── full_multi_gpu/
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│ ├── single_node.sh: Full fine-tune model with DeepSpeed on single node
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│ ├── multi_node.sh: Full fine-tune model with DeepSpeed on multiple nodes
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│ └── predict.sh: Do batch predict and compute BLEU and ROUGE scores after full tuning
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├── merge_lora/
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│ ├── merge.sh: Merge LoRA weights into the pre-trained models
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│ └── quantize.sh: Quantize the fine-tuned model with AutoGPTQ
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├── inference/
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│ ├── cli_demo.sh: Launch a command line interface with LoRA adapters
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│ ├── api_demo.sh: Launch an OpenAI-style API with LoRA adapters
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│ ├── web_demo.sh: Launch a web interface with LoRA adapters
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│ └── evaluate.sh: Evaluate model on the MMLU/CMMLU/C-Eval benchmarks with LoRA adapters
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└── extras/
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├── galore/
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│ └── sft.sh: Fine-tune model with GaLore
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├── loraplus/
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│ └── sft.sh: Fine-tune model using LoRA+
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├── llama_pro/
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│ ├── expand.sh: Expand layers in the model
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│ └── sft.sh: Fine-tune the expanded model
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└── fsdp_qlora/
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└── sft.sh: Fine-tune quantized model with FSDP+QLoRA
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```
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