LLaMA2-Accessory / README.md
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---
license: other
---
# LLaMA2-Accessory: An Open-source Toolkit for LLM Development ๐Ÿš€
๐Ÿš€**LLaMA2-Accessory** is an open-source toolkit for pre-training, fine-tuning and deployment of **Large Language Models (LLMs)** and **mutlimodal LLMs**. This repo is mainly inherited from [LLaMA-Adapter](https://github.com/OpenGVLab/LLaMA-Adapter) with more advanced features.๐Ÿง 
## News
- **[2023.07.23]** Initial release ๐Ÿ“Œ
## Features
* **๐Ÿ’กSupport More Datasets and Tasks**
- ๐ŸŽฏ Pre-training with [RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) and [StarCoder](https://github.com/bigcode-project/starcoder).
- ๐Ÿ“š Single-modal fine-tuning with [Alpaca](https://github.com/tatsu-lab/stanford_alpaca), [ShareGPT](https://github.com/domeccleston/sharegpt), [LIMA](https://arxiv.org/pdf/2305.11206.pdf), [UltraChat](https://github.com/thunlp/UltraChat) and [MOSS](https://github.com/OpenLMLab/MOSS).
- ๐ŸŒˆ Multi-modal fine-tuning with image-text pairs ([LAION](https://laion.ai/blog/laion-5b/), [COYO](https://github.com/kakaobrain/coyo-dataset) and more), interleaved image-text data ([MMC4](https://github.com/allenai/mmc4) and [OBELISC](https://github.com/huggingface/OBELISC)) and visual instruction data ([LLaVA](https://github.com/haotian-liu/LLaVA), [Shrika](https://github.com/shikras/shikra), [Bard](https://bard.google.com/))
- ๐Ÿ”ง LLM for API Control ([GPT4Tools](https://github.com/StevenGrove/GPT4Tools) and [Gorilla](https://github.com/ShishirPatil/gorilla)).
* **โšกEfficient Optimization and Deployment**
- ๐Ÿš Parameter-efficient fine-tuning with [Zero-init Attenion](https://github.com/OpenGVLab/LLaMA-Adapter) and [Bias-norm Tuning](https://github.com/OpenGVLab/LLaMA-Adapter).
- ๐Ÿ’ป Fully Sharded Data Parallel ([FSDP](https://engineering.fb.com/2021/07/15/open-source/fsdp/)), [Flash Attention 2](https://github.com/Dao-AILab/flash-attention) and [QLoRA](https://github.com/artidoro/qlora).
* **๐Ÿ‹๏ธโ€โ™€๏ธSupport More Visual Encoders and LLMs**
- ๐Ÿ‘โ€๐Ÿ—จ Visual Encoders: [CLIP](https://github.com/openai/CLIP), [Q-Former](https://github.com/salesforce/LAVIS) and [ImageBind](https://github.com/facebookresearch/ImageBind).
- ๐Ÿงฉ LLMs: LLaMA and LLaMA2.
## Installation
See [docs/install.md](./docs/install.md).
## Training & Inference
See [docs/pretrain.md](./docs/pretrain.md) and [docs/finetune.md](./docs/finetune.md).
## Demos
* Instruction-tuned LLaMA2: [alpaca](https://alpha-vllm.github.io/demo_presentation/examples/finetune/sg/alpaca.html) & [gorilla](https://alpha-vllm.github.io/demo_presentation/examples/finetune/sg/gorilla.html).
* Chatbot LLaMA2: [dialog_sharegpt](https://alpha-vllm.github.io/demo_presentation/examples/finetune/sg/dialog_sharegpt.html) & [dialog_lima](https://alpha-vllm.github.io/demo_presentation/examples/finetune/sg/dialog_lima.html) & [llama2-chat](https://alpha-vllm.github.io/demo_presentation/examples/finetune/sg/llama2-chat.html).
* Multimodal LLaMA2: [in-context](https://alpha-vllm.github.io/demo_presentation/examples/finetune/mm/in-context.html)
## Core Contributors
[Chris Liu](https://github.com/ChrisLiu6), [Ziyi Lin](https://github.com/linziyi96), [Guian Fang](https://github.com/Enderfga), [Jiaming Han](https://github.com/csuhan), [Renrui Zhang](https://github.com/ZrrSkywalker), [Wenqi Shao](https://github.com/wqshao126), [Peng Gao](https://github.com/gaopengpjlab)
## Hiring Announcement
๐Ÿ”ฅ **We are hiring** interns, postdocs, and full-time researchers at the **General Vision Group, Shanghai AI Lab**, with a focus on multi-modality and vision foundation models. If you are interested, please contact [gaopengcuhk@gmail.com](mailto:gaopengcuhk@gmail.com).
## Citation
If you find our code and paper useful, please kindly cite:
```bash
@article{zhang2023llamaadapter,
title = {LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention},
author={Zhang, Renrui and Han, Jiaming and Liu, Chris and Gao, Peng and Zhou, Aojun and Hu, Xiangfei and Yan, Shilin and Lu, Pan and Li, Hongsheng and Qiao, Yu},
journal={arXiv preprint arXiv:2303.16199},
year={2023}
}
```
```bash
@article{gao2023llamaadapterv2,
title = {LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Model},
author={Gao, Peng and Han, Jiaming and Zhang, Renrui and Lin, Ziyi and Geng, Shijie and Zhou, Aojun and Zhang, Wei and Lu, Pan and He, Conghui and Yue, Xiangyu and Li, Hongsheng and Qiao, Yu},
journal={arXiv preprint arXiv:2304.15010},
year={2023}
}
```
## Acknowledgement
+ [@facebookresearch](https://github.com/facebookresearch) for [llama](https://github.com/facebookresearch/llama)
+ [@OpenGVLab](https://github.com/OpenGVLab) for [LLaMA-Adapter](https://github.com/OpenGVLab/LLaMA-Adapter)
+ [@facebookresearch](https://github.com/facebookresearch) for [ImageBind](https://github.com/facebookresearch/ImageBind) & [LIMA](https://huggingface.co/datasets/64bits/lima_vicuna_format)
+ [@Instruction-Tuning-with-GPT-4](https://github.com/Instruction-Tuning-with-GPT-4) for [GPT-4-LLM](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM)
+ [@tatsu-lab](https://github.com/tatsu-lab) for [stanford_alpaca](https://github.com/tatsu-lab/stanford_alpaca)
+ [@tloen](https://github.com/tloen) for [alpaca-lora](https://github.com/tloen/alpaca-lora)
+ [@lm-sys](https://github.com/lm-sys) for [FastChat](https://github.com/lm-sys/FastChat)
+ [@domeccleston](https://github.com/domeccleston) for [sharegpt](https://github.com/domeccleston/sharegpt)
+ [@karpathy](https://github.com/karpathy) for [nanoGPT](https://github.com/karpathy/nanoGPT)
+ [@Dao-AILab](https://github.com/Dao-AILab) for [flash-attention](https://github.com/Dao-AILab/flash-attention)
+ [@NVIDIA](https://github.com/NVIDIA) for [apex](https://github.com/NVIDIA/apex) & [Megatron-LM](https://github.com/NVIDIA/Megatron-LM)
+ [@Vision-CAIR](https://github.com/Vision-CAIR) for [MiniGPT-4](https://github.com/Vision-CAIR/MiniGPT-4)
+ [@haotian-liu](https://github.com/haotian-liu) for [LLaVA](https://github.com/haotian-liu/LLaVA)
+ [@huggingface](https://github.com/huggingface) for [peft](https://github.com/huggingface/peft) & [OBELISC](https://github.com/huggingface/OBELISC)
+ [@Lightning-AI](https://github.com/Lightning-AI) for [lit-gpt](https://github.com/Lightning-AI/lit-gpt) & [lit-llama](https://github.com/Lightning-AI/lit-llama)
+ [@allenai](https://github.com/allenai) for [mmc4](https://github.com/allenai/mmc4)
+ [@StevenGrove](https://github.com/StevenGrove) for [GPT4Tools](https://github.com/StevenGrove/GPT4Tools)
+ [@ShishirPatil](https://github.com/ShishirPatil) for [gorilla](https://github.com/ShishirPatil/gorilla)
+ [@OpenLMLab](https://github.com/OpenLMLab) for [MOSS](https://github.com/OpenLMLab/MOSS)
+ [@thunlp](https://github.com/thunlp) for [UltraChat](https://github.com/thunlp/UltraChat)
+ [@LAION-AI](https://github.com/LAION-AI) for [LAION-5B](https://laion.ai/blog/laion-5b/)
+ [@shikras](https://github.com/shikras) for [shikra](https://github.com/shikras/shikra)
+ [@kakaobrain](https://github.com/kakaobrain) for [coyo-dataset](https://github.com/kakaobrain/coyo-dataset)
+ [@salesforce](https://github.com/salesforce) for [LAVIS](https://github.com/salesforce/LAVIS)
+ [@openai](https://github.com/openai) for [CLIP](https://github.com/openai/CLIP)
+ [@bigcode-project](https://github.com/bigcode-project) for [starcoder](https://github.com/bigcode-project/starcoder)
+ [@tiiuae](https://huggingface.co/tiiuae) for [falcon-refinedweb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb)
+ [@microsoft](https://github.com/microsoft) for [DeepSpeed](https://github.com/microsoft/DeepSpeed)
+ [@declare-lab](https://github.com/declare-lab) for [flacuna](https://github.com/declare-lab/flacuna)
+ [@Google](https://github.com/google) for [Bard](https://bard.google.com/)
## License
Llama 2 is licensed under the [LLAMA 2 Community License](LICENSE_llama2), Copyright (c) Meta Platforms, Inc. All Rights Reserved.