File size: 6,785 Bytes

e5c09aa

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.

By default, LLaMA-Factory uses all visible computing devices.

Basic usage:

```bash

llamafactory-cli train examples/train_lora/llama3_lora_sft.yaml

```

Advanced usage:

```bash

CUDA_VISIBLE_DEVICES=0,1 llamafactory-cli train examples/train_lora/llama3_lora_sft.yaml \

    learning_rate=1e-5 \

    logging_steps=1

```

```bash

bash examples/train_lora/llama3_lora_sft.sh

```

## 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/qwen2_5vl_lora_sft.yaml

```

#### DPO/ORPO/SimPO Training

```bash

llamafactory-cli train examples/train_lora/llama3_lora_dpo.yaml

```

#### Multimodal DPO/ORPO/SimPO Training

```bash

llamafactory-cli train examples/train_lora/qwen2_5vl_lora_dpo.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

```

#### 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

```

#### Supervised Fine-Tuning on Multiple Nodes

```bash

FORCE_TORCHRUN=1 NNODES=2 NODE_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 NODE_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

```

#### Supervised Fine-Tuning with Ray on 4 GPUs

```bash

USE_RAY=1 llamafactory-cli train examples/train_lora/llama3_lora_sft_ray.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-bit Bitsandbytes Quantization on Ascend NPU

```bash

llamafactory-cli train examples/train_qlora/llama3_lora_sft_bnb_npu.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.yaml

```

#### Supervised Fine-Tuning on Multiple Nodes

```bash

FORCE_TORCHRUN=1 NNODES=2 NODE_RANK=0 MASTER_ADDR=192.168.0.1 MASTER_PORT=29500 llamafactory-cli train examples/train_full/llama3_full_sft.yaml

FORCE_TORCHRUN=1 NNODES=2 NODE_RANK=1 MASTER_ADDR=192.168.0.1 MASTER_PORT=29500 llamafactory-cli train examples/train_full/llama3_full_sft.yaml

```

#### Multimodal Supervised Fine-Tuning

```bash

FORCE_TORCHRUN=1 llamafactory-cli train examples/train_full/qwen2_5vl_full_sft.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

```

### Save Ollama modelfile

```bash

llamafactory-cli export examples/merge_lora/llama3_full_sft.yaml

```

### Inferring LoRA Fine-Tuned Models

#### Evaluation using vLLM's Multi-GPU Inference

```

python scripts/vllm_infer.py --model_name_or_path meta-llama/Meta-Llama-3-8B-Instruct --template llama3 --dataset alpaca_en_demo

python scripts/eval_bleu_rouge.py generated_predictions.jsonl

```

#### Use CLI ChatBox

```bash

llamafactory-cli chat examples/inference/llama3_lora_sft.yaml

```

#### Use Web UI ChatBox

```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 APOLLO

```bash

llamafactory-cli train examples/extras/apollo/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

```

#### Full-Parameter Fine-Tuning using Muon

```bash

llamafactory-cli train examples/extras/muon/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

```