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