llama-factory

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Fine-tuning a large language model can be easy as...

https://github.com/hiyouga/LLaMA-Factory/assets/16256802/9840a653-7e9c-41c8-ae89-7ace5698baf6

Choose your path:

• 🤗 Spaces: https://huggingface.co/spaces/hiyouga/LLaMA-Board
• ModelScope: https://modelscope.cn/studios/hiyouga/LLaMA-Board
• Colab: https://colab.research.google.com/drive/1eRTPn37ltBbYsISy9Aw2NuI2Aq5CQrD9?usp=sharing
• Local machine: Please refer to usage

Table of Contents

• Features
• Benchmark
• Changelog
• Supported Models
• Supported Training Approaches
• Provided Datasets
• Requirement
• Getting Started
• Projects using LLaMA Factory
• License
• Citation
• Acknowledgement

Features

• Various models: LLaMA, Mistral, Mixtral-MoE, Qwen, Yi, Gemma, Baichuan, ChatGLM, Phi, etc.
• Integrated methods: (Continuous) pre-training, supervised fine-tuning, reward modeling, PPO and DPO.
• Scalable resources: 32-bit full-tuning, 16-bit freeze-tuning, 16-bit LoRA, 2/4/8-bit QLoRA via AQLM/AWQ/GPTQ/LLM.int8.
• Advanced algorithms: DoRA, LongLoRA, LLaMA Pro, LoftQ, agent tuning.
• Practical tricks: FlashAttention-2, Unsloth, RoPE scaling, NEFTune, rsLoRA.
• Experiment monitors: LlamaBoard, TensorBoard, Wandb, MLflow, etc.

Benchmark

Compared to ChatGLM's P-Tuning, LLaMA-Factory's LoRA tuning offers up to 3.7 times faster training speed with a better Rouge score on the advertising text generation task. By leveraging 4-bit quantization technique, LLaMA-Factory's QLoRA further improves the efficiency regarding the GPU memory.

Changelog

[24/02/28] We supported weight-decomposed LoRA (DoRA). Try --use_dora to activate DoRA training.

[24/02/15] We supported block expansion proposed by LLaMA Pro. See tests/llama_pro.py for usage.

[24/02/05] Qwen1.5 (Qwen2 beta version) series models are supported in LLaMA-Factory. Check this blog post for details.

Supported Models

[!NOTE] Default module is used for the --lora_target argument, you can use --lora_target all to specify all the available modules.

For the "base" models, the --template argument can be chosen from default, alpaca, vicuna etc. But make sure to use the corresponding template for the "chat" models.

Please refer to constants.py for a full list of models we supported.

Supported Training Approaches

[!NOTE] Use --quantization_bit 4 argument to enable QLoRA.

Provided Datasets

Please refer to data/README.md for details.

Some datasets require confirmation before using them, so we recommend logging in with your Hugging Face account using these commands.

pip install --upgrade huggingface_hub
huggingface-cli login

Requirement

Hardware Requirement

* estimated

Getting Started

Data Preparation (optional)

Please refer to data/README.md for checking the details about the format of dataset files. You can either use a single .json file or a dataset loading script with multiple files to create a custom dataset.

[!NOTE] Please update data/dataset_info.json to use your custom dataset. About the format of this file, please refer to data/README.md.

Dependence Installation (optional)

git clone https://github.com/hiyouga/LLaMA-Factory.git
conda create -n llama_factory python=3.10
conda activate llama_factory
cd LLaMA-Factory
pip install -r requirements.txt

If you want to enable the quantized LoRA (QLoRA) on the Windows platform, you will be required to install a pre-built version of bitsandbytes library, which supports CUDA 11.1 to 12.2.

pip install https://github.com/jllllll/bitsandbytes-windows-webui/releases/download/wheels/bitsandbytes-0.40.0-py3-none-win_amd64.whl

To enable FlashAttention-2 on the Windows platform, you need to install the precompiled flash-attn library, which supports CUDA 12.1 to 12.2. Please download the corresponding version from flash-attention based on your requirements.

Use ModelScope Hub (optional)

If you have trouble with downloading models and datasets from Hugging Face, you can use LLaMA-Factory together with ModelScope in the following manner.

export USE_MODELSCOPE_HUB=1 # `set USE_MODELSCOPE_HUB=1` for Windows

Then you can train the corresponding model by specifying a model ID of the ModelScope Hub. (find a full list of model IDs at ModelScope Hub)

CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
    --model_name_or_path modelscope/Llama-2-7b-ms \
    ... # arguments (same as below)

LLaMA Board also supports using the models and datasets on the ModelScope Hub.

CUDA_VISIBLE_DEVICES=0 USE_MODELSCOPE_HUB=1 python src/train_web.py

Train on a single GPU

[!IMPORTANT] If you want to train models on multiple GPUs, please refer to Distributed Training.

LLaMA Board GUI

CUDA_VISIBLE_DEVICES=0 python src/train_web.py

Pre-Training

CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
    --stage pt \
    --do_train \
    --model_name_or_path path_to_llama_model \
    --dataset wiki_demo \
    --finetuning_type lora \
    --lora_target q_proj,v_proj \
    --output_dir path_to_pt_checkpoint \
    --overwrite_cache \
    --per_device_train_batch_size 4 \
    --gradient_accumulation_steps 4 \
    --lr_scheduler_type cosine \
    --logging_steps 10 \
    --save_steps 1000 \
    --learning_rate 5e-5 \
    --num_train_epochs 3.0 \
    --plot_loss \
    --fp16

Supervised Fine-Tuning

CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
    --stage sft \
    --do_train \
    --model_name_or_path path_to_llama_model \
    --dataset alpaca_gpt4_en \
    --template default \
    --finetuning_type lora \
    --lora_target q_proj,v_proj \
    --output_dir path_to_sft_checkpoint \
    --overwrite_cache \
    --per_device_train_batch_size 4 \
    --gradient_accumulation_steps 4 \
    --lr_scheduler_type cosine \
    --logging_steps 10 \
    --save_steps 1000 \
    --learning_rate 5e-5 \
    --num_train_epochs 3.0 \
    --plot_loss \
    --fp16

Reward Modeling

CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
    --stage rm \
    --do_train \
    --model_name_or_path path_to_llama_model \
    --adapter_name_or_path path_to_sft_checkpoint \
    --create_new_adapter \
    --dataset comparison_gpt4_en \
    --template default \
    --finetuning_type lora \
    --lora_target q_proj,v_proj \
    --output_dir path_to_rm_checkpoint \
    --per_device_train_batch_size 2 \
    --gradient_accumulation_steps 4 \
    --lr_scheduler_type cosine \
    --logging_steps 10 \
    --save_steps 1000 \
    --learning_rate 1e-6 \
    --num_train_epochs 1.0 \
    --plot_loss \
    --fp16

PPO Training

CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
    --stage ppo \
    --do_train \
    --model_name_or_path path_to_llama_model \
    --adapter_name_or_path path_to_sft_checkpoint \
    --create_new_adapter \
    --dataset alpaca_gpt4_en \
    --template default \
    --finetuning_type lora \
    --lora_target q_proj,v_proj \
    --reward_model path_to_rm_checkpoint \
    --output_dir path_to_ppo_checkpoint \
    --per_device_train_batch_size 2 \
    --gradient_accumulation_steps 4 \
    --lr_scheduler_type cosine \
    --top_k 0 \
    --top_p 0.9 \
    --logging_steps 10 \
    --save_steps 1000 \
    --learning_rate 1e-5 \
    --num_train_epochs 1.0 \
    --plot_loss \
    --fp16

[!TIP] Use --adapter_name_or_path path_to_sft_checkpoint,path_to_ppo_checkpoint to infer the fine-tuned model.

[!WARNING] Use --per_device_train_batch_size=1 for LLaMA-2 models in fp16 PPO training.

DPO Training

CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
    --stage dpo \
    --do_train \
    --model_name_or_path path_to_llama_model \
    --adapter_name_or_path path_to_sft_checkpoint \
    --create_new_adapter \
    --dataset comparison_gpt4_en \
    --template default \
    --finetuning_type lora \
    --lora_target q_proj,v_proj \
    --output_dir path_to_dpo_checkpoint \
    --per_device_train_batch_size 2 \
    --gradient_accumulation_steps 4 \
    --lr_scheduler_type cosine \
    --logging_steps 10 \
    --save_steps 1000 \
    --learning_rate 1e-5 \
    --num_train_epochs 1.0 \
    --plot_loss \
    --fp16

[!TIP] Use --adapter_name_or_path path_to_sft_checkpoint,path_to_dpo_checkpoint to infer the fine-tuned model.

Distributed Training

Use Huggingface Accelerate

accelerate config # configure the environment
accelerate launch src/train_bash.py # arguments (same as above)

compute_environment: LOCAL_MACHINE
debug: false
distributed_type: MULTI_GPU
downcast_bf16: 'no'
gpu_ids: all
machine_rank: 0
main_training_function: main
mixed_precision: fp16
num_machines: 1
num_processes: 4
rdzv_backend: static
same_network: true
tpu_env: []
tpu_use_cluster: false
tpu_use_sudo: false
use_cpu: false

Use DeepSpeed

deepspeed --num_gpus 8 --master_port=9901 src/train_bash.py \
    --deepspeed ds_config.json \
    ... # arguments (same as above)

{
  "train_batch_size": "auto",
  "train_micro_batch_size_per_gpu": "auto",
  "gradient_accumulation_steps": "auto",
  "gradient_clipping": "auto",
  "zero_allow_untested_optimizer": true,
  "fp16": {
    "enabled": "auto",
    "loss_scale": 0,
    "initial_scale_power": 16,
    "loss_scale_window": 1000,
    "hysteresis": 2,
    "min_loss_scale": 1
  },
  "zero_optimization": {
    "stage": 2,
    "allgather_partitions": true,
    "allgather_bucket_size": 5e8,
    "reduce_scatter": true,
    "reduce_bucket_size": 5e8,
    "overlap_comm": false,
    "contiguous_gradients": true
  }
}

Merge LoRA weights and export model

python src/export_model.py \
    --model_name_or_path path_to_llama_model \
    --adapter_name_or_path path_to_checkpoint \
    --template default \
    --finetuning_type lora \
    --export_dir path_to_export \
    --export_size 2 \
    --export_legacy_format False

[!WARNING] Merging LoRA weights into a quantized model is not supported.

[!TIP] Use --export_quantization_bit 4 and --export_quantization_dataset data/c4_demo.json to quantize the model after merging the LoRA weights.

Inference with OpenAI-style API

python src/api_demo.py \
    --model_name_or_path path_to_llama_model \
    --adapter_name_or_path path_to_checkpoint \
    --template default \
    --finetuning_type lora

[!TIP] Visit http://localhost:8000/docs for API documentation.

Inference with command line

python src/cli_demo.py \
    --model_name_or_path path_to_llama_model \
    --adapter_name_or_path path_to_checkpoint \
    --template default \
    --finetuning_type lora

Inference with web browser

python src/web_demo.py \
    --model_name_or_path path_to_llama_model \
    --adapter_name_or_path path_to_checkpoint \
    --template default \
    --finetuning_type lora

Evaluation

CUDA_VISIBLE_DEVICES=0 python src/evaluate.py \
    --model_name_or_path path_to_llama_model \
    --adapter_name_or_path path_to_checkpoint \
    --template vanilla \
    --finetuning_type lora \
    --task mmlu \
    --split test \
    --lang en \
    --n_shot 5 \
    --batch_size 4

Predict

CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
    --stage sft \
    --do_predict \
    --model_name_or_path path_to_llama_model \
    --adapter_name_or_path path_to_checkpoint \
    --dataset alpaca_gpt4_en \
    --template default \
    --finetuning_type lora \
    --output_dir path_to_predict_result \
    --per_device_eval_batch_size 1 \
    --max_samples 100 \
    --predict_with_generate \
    --fp16

[!WARNING] Use --per_device_train_batch_size=1 for LLaMA-2 models in fp16 predict.

[!TIP] We recommend using --per_device_eval_batch_size=1 and --max_target_length 128 at 4/8-bit predict.

Projects using LLaMA Factory

1. Wang et al. ESRL: Efficient Sampling-based Reinforcement Learning for Sequence Generation. 2023. [arxiv]
2. Yu et al. Open, Closed, or Small Language Models for Text Classification? 2023. [arxiv]
3. Luceri et al. Leveraging Large Language Models to Detect Influence Campaigns in Social Media. 2023. [arxiv]
4. Zhang et al. Alleviating Hallucinations of Large Language Models through Induced Hallucinations. 2023. [arxiv]
5. Wang et al. Know Your Needs Better: Towards Structured Understanding of Marketer Demands with Analogical Reasoning Augmented LLMs. 2024. [arxiv]
6. Wang et al. CANDLE: Iterative Conceptualization and Instantiation Distillation from Large Language Models for Commonsense Reasoning. 2024. [arxiv]
7. Choi et al. FACT-GPT: Fact-Checking Augmentation via Claim Matching with LLMs. 2024. [arxiv]
8. Zhang et al. AutoMathText: Autonomous Data Selection with Language Models for Mathematical Texts. 2024. [arxiv]
9. Lyu et al. KnowTuning: Knowledge-aware Fine-tuning for Large Language Models. 2024. [arxiv]
10. Yang et al. LaCo: Large Language Model Pruning via Layer Collaps. 2024. [arxiv]
11. Bhardwaj et al. Language Models are Homer Simpson! Safety Re-Alignment of Fine-tuned Language Models through Task Arithmetic. 2024. [arxiv]
12. Yang et al. Enhancing Empathetic Response Generation by Augmenting LLMs with Small-scale Empathetic Models. 2024. [arxiv]
13. Yi et al. Generation Meets Verification: Accelerating Large Language Model Inference with Smart Parallel Auto-Correct Decoding. 2024. [arxiv]
14. Cao et al. Head-wise Shareable Attention for Large Language Models. 2024. [arxiv]
15. Zhang et al. Enhancing Multilingual Capabilities of Large Language Models through Self-Distillation from Resource-Rich Languages. 2024. [arxiv]
16. Kim et al. Efficient and Effective Vocabulary Expansion Towards Multilingual Large Language Models. 2024. [arxiv]
17. StarWhisper: A large language model for Astronomy, based on ChatGLM2-6B and Qwen-14B.
18. DISC-LawLLM: A large language model specialized in Chinese legal domain, based on Baichuan-13B, is capable of retrieving and reasoning on legal knowledge.
19. Sunsimiao: A large language model specialized in Chinese medical domain, based on Baichuan-7B and ChatGLM-6B.
20. CareGPT: A series of large language models for Chinese medical domain, based on LLaMA2-7B and Baichuan-13B.
21. MachineMindset: A series of MBTI Personality large language models, capable of giving any LLM 16 different personality types based on different datasets and training methods.

[!TIP] If you have a project that should be incorporated, please contact via email or create a pull request.

License

This repository is licensed under the Apache-2.0 License.

Please follow the model licenses to use the corresponding model weights: Baichuan2 / BLOOM / ChatGLM3 / DeepSeek / Falcon / Gemma / InternLM2 / LLaMA / LLaMA-2 / Mistral / Phi-1.5/2 / Qwen / StarCoder2 / XVERSE / Yi / Yuan

Citation

If this work is helpful, please kindly cite as:

@Misc{llama-factory,
  title = {LLaMA Factory},
  author = {hiyouga},
  howpublished = {\url{https://github.com/hiyouga/LLaMA-Factory}},
  year = {2023}
}

Acknowledgement

This repo benefits from PEFT, QLoRA and FastChat. Thanks for their wonderful works.

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