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https://github.com/jianzhnie/llamatuner
Easy and Efficient Finetuning LLMs. (Supported LLama, LLama2, LLama3, Qwen, Baichuan, GLM , Falcon) 大模型高效量化训练+部署.
https://github.com/jianzhnie/llamatuner
chatgpt dpo llama llama3 mixtral ppo qlora qwen rlhf
Last synced: 3 days ago
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Easy and Efficient Finetuning LLMs. (Supported LLama, LLama2, LLama3, Qwen, Baichuan, GLM , Falcon) 大模型高效量化训练+部署.
- Host: GitHub
- URL: https://github.com/jianzhnie/llamatuner
- Owner: jianzhnie
- License: apache-2.0
- Created: 2023-05-25T07:52:41.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-07-10T03:28:19.000Z (6 months ago)
- Last Synced: 2024-12-28T11:04:26.430Z (3 days ago)
- Topics: chatgpt, dpo, llama, llama3, mixtral, ppo, qlora, qwen, rlhf
- Language: Python
- Homepage: https://jianzhnie.github.io/llmtech/
- Size: 874 KB
- Stars: 583
- Watchers: 9
- Forks: 63
- Open Issues: 20
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-ChatGPT-repositories - LLamaTuner - Easy and Efficient Finetuning LLMs. (Supported LLama, LLama2, LLama3, Qwen, Baichuan, GLM , Falcon) 大模型高效量化训练+部署. (Langchain)
README
[](https://www.python.org/downloads/release/python-390/)
[](https://github.com/psf/black)
👋🤗🤗👋 Join our [WeChat](assets/wechat.jpg).
# Easy and Efficient Fine-tuning LLMs --- 简单高效的大语言模型训练/部署
[中文](README_zh.md) | English
## Introduction
LLamaTuner is an efficient, flexible and full-featured toolkit for fine-tuning LLM (Llama3, Phi3, Qwen, Mistral, ...)
**Efficient**
- Support LLM, VLM pre-training / fine-tuning on almost all GPUs. LLamaTuner is capable of fine-tuning 7B LLM on a single 8GB GPU, as well as multi-node fine-tuning of models exceeding 70B.
- Automatically dispatch high-performance operators such as FlashAttention and Triton kernels to increase training throughput.
- Compatible with [DeepSpeed](https://github.com/microsoft/DeepSpeed) 🚀, easily utilizing a variety of ZeRO optimization techniques.
**Flexible**
- Support various LLMs ([Llama 3](https://huggingface.co/meta-llama), [Mixtral](https://huggingface.co/mistralai), [Llama 2](https://huggingface.co/meta-llama), [ChatGLM](https://huggingface.co/THUDM), [Qwen](https://huggingface.co/Qwen), [Baichuan](https://huggingface.co/baichuan-inc), ...).
- Support VLM ([LLaVA](https://github.com/haotian-liu/LLaVA)).
- Well-designed data pipeline, accommodating datasets in any format, including but not limited to open-source and custom formats.
- Support various training algorithms ([QLoRA](http://arxiv.org/abs/2305.14314), [LoRA](http://arxiv.org/abs/2106.09685), full-parameter fune-tune), allowing users to choose the most suitable solution for their requirements.
**Full-featured**
- Support continuous pre-training, instruction fine-tuning, and agent fine-tuning.
- Support chatting with large models with pre-defined templates.
## Table of Contents
- [Easy and Efficient Fine-tuning LLMs --- 简单高效的大语言模型训练/部署](#easy-and-efficient-fine-tuning-llms------简单高效的大语言模型训练部署)
- [Introduction](#introduction)
- [Table of Contents](#table-of-contents)
- [Supported Models](#supported-models)
- [Supported Training Approaches](#supported-training-approaches)
- [Supported Datasets](#supported-datasets)
- [Data Preprocessing](#data-preprocessing)
- [Model Zoo](#model-zoo)
- [Requirement](#requirement)
- [Hardware Requirement](#hardware-requirement)
- [Getting Started](#getting-started)
- [Clone the code](#clone-the-code)
- [Getting Started](#getting-started-1)
- [QLora int4 Finetune](#qlora-int4-finetune)
- [Known Issues and Limitations](#known-issues-and-limitations)
- [License](#license)
- [Acknowledgements](#acknowledgements)
- [Some lmm fine-tuning repos](#some-lmm-fine-tuning-repos)
- [Citation](#citation)
## Supported Models
| Model | Model size | Default module | Template |
| ---------------------------------------------------- | -------------------------------- | --------------- | --------- |
| [Baichuan](https://huggingface.co/baichuan-inc) | 7B/13B | W_pack | baichuan |
| [Baichuan2](https://huggingface.co/baichuan-inc) | 7B/13B | W_pack | baichuan2 |
| [BLOOM](https://huggingface.co/bigscience) | 560M/1.1B/1.7B/3B/7.1B/176B | query_key_value | - |
| [BLOOMZ](https://huggingface.co/bigscience) | 560M/1.1B/1.7B/3B/7.1B/176B | query_key_value | - |
| [ChatGLM3](https://huggingface.co/THUDM) | 6B | query_key_value | chatglm3 |
| [Command-R](https://huggingface.co/CohereForAI) | 35B/104B | q_proj,v_proj | cohere |
| [DeepSeek (MoE)](https://huggingface.co/deepseek-ai) | 7B/16B/67B/236B | q_proj,v_proj | deepseek |
| [Falcon](https://huggingface.co/tiiuae) | 7B/11B/40B/180B | query_key_value | falcon |
| [Gemma/CodeGemma](https://huggingface.co/google) | 2B/7B | q_proj,v_proj | gemma |
| [InternLM2](https://huggingface.co/internlm) | 7B/20B | wqkv | intern2 |
| [LLaMA](https://github.com/facebookresearch/llama) | 7B/13B/33B/65B | q_proj,v_proj | - |
| [LLaMA-2](https://huggingface.co/meta-llama) | 7B/13B/70B | q_proj,v_proj | llama2 |
| [LLaMA-3](https://huggingface.co/meta-llama) | 8B/70B | q_proj,v_proj | llama3 |
| [LLaVA-1.5](https://huggingface.co/llava-hf) | 7B/13B | q_proj,v_proj | vicuna |
| [Mistral/Mixtral](https://huggingface.co/mistralai) | 7B/8x7B/8x22B | q_proj,v_proj | mistral |
| [OLMo](https://huggingface.co/allenai) | 1B/7B | q_proj,v_proj | - |
| [PaliGemma](https://huggingface.co/google) | 3B | q_proj,v_proj | gemma |
| [Phi-1.5/2](https://huggingface.co/microsoft) | 1.3B/2.7B | q_proj,v_proj | - |
| [Phi-3](https://huggingface.co/microsoft) | 3.8B | qkv_proj | phi |
| [Qwen](https://huggingface.co/Qwen) | 1.8B/7B/14B/72B | c_attn | qwen |
| [Qwen1.5 (Code/MoE)](https://huggingface.co/Qwen) | 0.5B/1.8B/4B/7B/14B/32B/72B/110B | q_proj,v_proj | qwen |
| [StarCoder2](https://huggingface.co/bigcode) | 3B/7B/15B | q_proj,v_proj | - |
| [XVERSE](https://huggingface.co/xverse) | 7B/13B/65B | q_proj,v_proj | xverse |
| [Yi (1/1.5)](https://huggingface.co/01-ai) | 6B/9B/34B | q_proj,v_proj | yi |
| [Yi-VL](https://huggingface.co/01-ai) | 6B/34B | q_proj,v_proj | yi_vl |
| [Yuan](https://huggingface.co/IEITYuan) | 2B/51B/102B | q_proj,v_proj | yuan |
## Supported Training Approaches
| Approach | Full-tuning | Freeze-tuning | LoRA | QLoRA |
| ---------------------- | ------------------ | ------------------ | ------------------ | ------------------ |
| Pre-Training | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
| Supervised Fine-Tuning | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
| Reward Modeling | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
| PPO Training | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
| DPO Training | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
| KTO Training | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
| ORPO Training | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
## Supported Datasets
As of now, we support the following datasets, most of which are all available in the [Hugging Face datasets library](https://huggingface.co/datasets/).
Supervised fine-tuning dataset
- [Stanford Alpaca](https://github.com/tatsu-lab/stanford_alpaca)
- [Stanford Alpaca (Chinese)](https://github.com/ymcui/Chinese-LLaMA-Alpaca)
- [Hello-SimpleAI/HC3](https://huggingface.co/datasets/Hello-SimpleAI/HC3)
- [BELLE 2M (zh)](https://huggingface.co/datasets/BelleGroup/train_2M_CN)
- [BELLE 1M (zh)](https://huggingface.co/datasets/BelleGroup/train_1M_CN)
- [BELLE 0.5M (zh)](https://huggingface.co/datasets/BelleGroup/train_0.5M_CN)
- [BELLE Dialogue 0.4M (zh)](https://huggingface.co/datasets/BelleGroup/generated_chat_0.4M)
- [BELLE School Math 0.25M (zh)](https://huggingface.co/datasets/BelleGroup/school_math_0.25M)
- [BELLE Multiturn Chat 0.8M (zh)](https://huggingface.co/datasets/BelleGroup/multiturn_chat_0.8M)
- [databricks-dolly-15k](https://huggingface.co/datasets/databricks/databricks-dolly-15k)
- [mosaicml/dolly_hhrlhf](https://huggingface.co/datasets/mosaicml/dolly_hhrlhf)
- [GPT-4 Generated Data](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM)
- [Alpaca CoT](https://huggingface.co/datasets/QingyiSi/Alpaca-CoT)
- [UltraChat](https://github.com/thunlp/UltraChat)
- [OpenAssistant/oasst1](https://huggingface.co/datasets/OpenAssistant/oasst1)
- [ShareGPT_Vicuna_unfiltered](https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered)
- [BIAI/OL-CC](https://data.baai.ac.cn/details/OL-CC)
- [timdettmers/openassistant-guanaco](https://huggingface.co/datasets/timdettmers/openassistant-guanaco)
- [Evol-Instruct](https://huggingface.co/datasets/victor123/evol_instruct_70k)
- [OpenOrca](https://huggingface.co/datasets/Open-Orca/OpenOrca)
- [Platypus](https://huggingface.co/datasets/garage-bAInd/Open-Platypus)
- [OpenHermes](https://huggingface.co/datasets/teknium/openhermes)
Preference datasets
- [DPO mixed (en&zh)](https://huggingface.co/datasets/hiyouga/DPO-En-Zh-20k)
- [Orca DPO Pairs (en)](https://huggingface.co/datasets/Intel/orca_dpo_pairs)
- [HH-RLHF (en)](https://huggingface.co/datasets/Anthropic/hh-rlhf)
- [Open Assistant(en&zh)](https://huggingface.co/datasets/OpenAssistant/oasst1)
- [Nectar (en)](https://huggingface.co/datasets/berkeley-nest/Nectar)
- [Orca DPO (de)](https://huggingface.co/datasets/mayflowergmbh/intel_orca_dpo_pairs_de)
- [KTO mixed (en)](https://huggingface.co/datasets/argilla/kto-mix-15k)
Please refer to [data/README.md](data/README.md) to learn how to use these datasets. If you want to explore more datasets, please refer to the [awesome-instruction-datasets](https://github.com/jianzhnie/awesome-instruction-datasets). Some datasets require confirmation before using them, so we recommend logging in with your Hugging Face account using these commands.
```bash
pip install --upgrade huggingface_hub
huggingface-cli login
```
### Data Preprocessing
We provide a number of data preprocessing tools in the [data](./llamatuner/data) folder. These tools are intended to be a starting point for further research and development.
- [data_utils.py](./llamatuner/data/data_utils.py) : Data preprocessing and formatting
- [sft_dataset.py](./llamatuner/data/sft_dataset.py) : Supervised fine-tuning dataset class and collator
- [conv_dataset.py](./llamatuner/data/conv_dataset.py) : Conversation dataset class and collator
## Model Zoo
We provide a number of models in the [Hugging Face model hub](https://huggingface.co/decapoda-research). These models are trained with QLoRA and can be used for inference and finetuning. We provide the following models:
| Base Model | Adapter | Instruct Datasets | Train Script | Log | Model on Huggingface |
| ---------------------------------------------------------------- | ------------ | ------------------------------------------------------------------------------------------ | --------------------------------------------------------- | ----------------------------------------------------------------- | ----------------------------------------------------------------------------------- |
| [llama-7b](https://huggingface.co/decapoda-research/llama-7b-hf) | FullFinetune | - | - | - | |
| [llama-7b](https://huggingface.co/decapoda-research/llama-7b-hf) | QLoRA | [openassistant-guanaco](https://huggingface.co/datasets/timdettmers/openassistant-guanaco) | [finetune_lamma7b](./scripts/finetune_llama_guanaco7b.sh) | [wandb log](https://wandb.ai/jianzhnie/huggingface/runs/1e2km7b1) | [GaussianTech/llama-7b-sft](https://huggingface.co/GaussianTech/llama-7b-sft) |
| [llama-7b](https://huggingface.co/decapoda-research/llama-7b-hf) | QLoRA | [OL-CC](https://data.baai.ac.cn/details/OL-CC) | [finetune_lamma7b](./scripts/finetune_llama_guanaco7b.sh) | | |
| [baichuan7b](https://huggingface.co/baichuan-inc/baichuan-7B) | QLoRA | [openassistant-guanaco](https://huggingface.co/datasets/timdettmers/openassistant-guanaco) | [finetune_baichuan7b](./scripts/finetune_baichuan_7b.sh) | [wandb log](https://wandb.ai/jianzhnie/huggingface/runs/41lq9joa) | [GaussianTech/baichuan-7b-sft](https://huggingface.co/GaussianTech/baichuan-7b-sft) |
| [baichuan7b](https://huggingface.co/baichuan-inc/baichuan-7B) | QLoRA | [OL-CC](https://data.baai.ac.cn/details/OL-CC) | [finetune_baichuan7b](./scripts/finetune_baichuan_7b.sh) | [wandb log](https://wandb.ai/jianzhnie/huggingface/runs/1lw2bmvn) | - |
## Requirement
| Mandatory | Minimum | Recommend |
| ------------ | ------- | --------- |
| python | 3.8 | 3.10 |
| torch | 1.13.1 | 2.2.0 |
| transformers | 4.37.2 | 4.41.0 |
| datasets | 2.14.3 | 2.19.1 |
| accelerate | 0.27.2 | 0.30.1 |
| peft | 0.9.0 | 0.11.1 |
| trl | 0.8.2 | 0.8.6 |
| Optional | Minimum | Recommend |
| ------------ | ------- | --------- |
| CUDA | 11.6 | 12.2 |
| deepspeed | 0.10.0 | 0.14.0 |
| bitsandbytes | 0.39.0 | 0.43.1 |
| vllm | 0.4.0 | 0.4.2 |
| flash-attn | 2.3.0 | 2.5.8 |
### Hardware Requirement
\* *estimated*
| Method | Bits | 7B | 13B | 30B | 70B | 110B | 8x7B | 8x22B |
| ----------------- | ---- | ----- | ----- | ----- | ------ | ------ | ----- | ------ |
| Full | AMP | 120GB | 240GB | 600GB | 1200GB | 2000GB | 900GB | 2400GB |
| Full | 16 | 60GB | 120GB | 300GB | 600GB | 900GB | 400GB | 1200GB |
| Freeze | 16 | 20GB | 40GB | 80GB | 200GB | 360GB | 160GB | 400GB |
| LoRA/GaLore/BAdam | 16 | 16GB | 32GB | 64GB | 160GB | 240GB | 120GB | 320GB |
| QLoRA | 8 | 10GB | 20GB | 40GB | 80GB | 140GB | 60GB | 160GB |
| QLoRA | 4 | 6GB | 12GB | 24GB | 48GB | 72GB | 30GB | 96GB |
| QLoRA | 2 | 4GB | 8GB | 16GB | 24GB | 48GB | 18GB | 48GB |
## Getting Started
### Clone the code
Clone this repository and navigate to the Efficient-Tuning-LLMs folder
```bash
git clone https://github.com/jianzhnie/LLamaTuner.git
cd LLamaTuner
```
## Getting Started
| main function | Useage | Scripts |
| -------------------------------- | ------------------------------------------------------------------------------------ | ------------------------------------------ |
| [train.py](tools/train.py) | Full finetune LLMs on SFT datasets | [full_finetune](./scripts/full_finetune) |
| [train_lora.py](tools/train_lora.py) | Finetune LLMs by using Lora (Low-Rank Adaptation of Large Language Models finetune) | [lora_finetune](./scripts/lora_finetune) |
| [train_qlora.py](tools/rain_qlora.py) | Finetune LLMs by using QLora (QLoRA: Efficient Finetuning of Quantized LLMs) | [qlora_finetune](./scripts/qlora_finetune) |
### QLora int4 Finetune
The `train_qlora.py` code is a starting point for finetuning and inference on various datasets.
Basic command for finetuning a baseline model on the Alpaca dataset:
```bash
python train_qlora.py --model_name_or_path
```
For models larger than 13B, we recommend adjusting the learning rate:
```bash
python train_qlora.py –learning_rate 0.0001 --model_name_or_path
```
To find more scripts for finetuning and inference, please refer to the `scripts` folder.
## Known Issues and Limitations
Here a list of known issues and bugs. If your issue is not reported here, please open a new issue and describe the problem.
1. 4-bit inference is slow. Currently, our 4-bit inference implementation is not yet integrated with the 4-bit matrix multiplication
2. Resuming a LoRA training run with the Trainer currently runs on an error
3. Currently, using `bnb_4bit_compute_type='fp16'` can lead to instabilities. For 7B LLaMA, only 80% of finetuning runs complete without error. We have solutions, but they are not integrated yet into bitsandbytes.
4. Make sure that `tokenizer.bos_token_id = 1` to avoid generation issues.
## License
`LLamaTuner` is released under the Apache 2.0 license.
## Acknowledgements
We thank the Huggingface team, in particular Younes Belkada, for their support integrating QLoRA with PEFT and transformers libraries.
We appreciate the work by many open-source contributors, especially:
- [LLaMa](https://github.com/facebookresearch/llama/)
- [Vicuna](https://github.com/lm-sys/FastChat/)
- [xTuring](https://github.com/stochasticai/xTuring)
- [Alpaca-LoRA](https://github.com/tloen/alpaca-lora/)
- [Stanford Alpaca](https://github.com/tatsu-lab/stanford_alpaca/)
- [LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory)
- [Hugging Face](https://huggingface.co/)
- [Peft](https://github.com/huggingface/peft.git)
- [axolotl](https://github.com/OpenAccess-AI-Collective/axolotl)
- [deepspeed](https://www.deepspeed.ai/)
- [Unsloth](https://github.com/unslothai/unsloth)
- [qlora](https://github.com/artidoro/qlora)
- [bitsandbytes](https://github.com/TimDettmers/bitsandbytes)
## Some lmm fine-tuning repos
- https://github.com/QwenLM
- https://github.com/InternLM
- https://github.com/ymcui/Chinese-LLaMA-Alpaca-3
- https://github.com/ymcui/Chinese-Mixtral
- https://github.com/SmartFlowAI/EmoLLM
- https://github.com/yangjianxin1/Firefly
- https://github.com/LiuHC0428/LAW-GPT
## Citation
Please cite the repo if you use the data or code in this repo.
```bibtex
@misc{Chinese-Guanaco,
author = {jianzhnie},
title = {LLamaTuner: Easy and Efficient Fine-tuning LLMs},
year = {2023},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/jianzhnie/LLamaTuner}},
}
```