[ColossalChat](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat) is the project to implement LLM with RLHF, powered by the [Colossal-AI](https://github.com/hpcaitech/ColossalAI) project.
Coati stands for `ColossalAI Talking Intelligence`. It is the name for the module implemented in this project and is also the name of the large language model developed by the ColossalChat project.
The Coati package provides a unified large language model framework that has implemented the following functions
- Supports comprehensive large-model training acceleration capabilities for ColossalAI, without requiring knowledge of complex distributed training algorithms
- Supervised datasets collection
- Supervised insturcts fine-tuning
- Training reward model
- Reinforcement learning with human feedback
- Quantization inference
- Fast model deploying
- Perfectly integration with the Hugging Face ecosystem, high degree of model customization
* [2023/03] [ColossalChat: An Open-Source Solution for Cloning ChatGPT With a Complete RLHF Pipeline](https://medium.com/@yangyou_berkeley/colossalchat-an-open-source-solution-for-cloning-chatgpt-with-a-complete-rlhf-pipeline-5edf08fb538b)
* [2023/02] [Open Source Solution Replicates ChatGPT Training Process! Ready to go with only 1.6GB GPU Memory](https://www.hpc-ai.tech/blog/colossal-ai-chatgpt)
Due to resource constraints, we will only provide this service from 29th Mar 2023 to 5 April 2023. However, we have provided the inference code in the [inference](./inference/) folder. The WebUI will be open-sourced soon as well.
> Warning: Due to model and dataset size limitations, Coati is just a baby model, Coati7B may output incorrect information and lack the ability for multi-turn dialogue. There is still significant room for improvement.
Stage2 trains a reward model, which obtains corresponding scores by manually ranking different outputs for the same prompt and supervises the training of the reward model
you can run the `examples/train_rm.sh` to start a reward model training
8-bit quantization is originally supported by the latest [transformers](https://github.com/huggingface/transformers). Please install it from source.
Please ensure you have downloaded HF-format model weights of LLaMA models.
Usage:
```python
from transformers import LlamaForCausalLM
USE_8BIT = True # use 8-bit quantization; otherwise, use fp16
model = LlamaForCausalLM.from_pretrained(
"pretrained/path",
load_in_8bit=USE_8BIT,
torch_dtype=torch.float16,
device_map="auto",
)
if not USE_8BIT:
model.half() # use fp16
model.eval()
```
**Troubleshooting**: if you get error indicating your CUDA-related libraries not found when loading 8-bit model, you can check whether your `LD_LIBRARY_PATH` is correct.
E.g. you can set `export LD_LIBRARY_PATH=$CUDA_HOME/lib64:$LD_LIBRARY_PATH`.
#### 4-bit setup
Please ensure you have downloaded HF-format model weights of LLaMA models first.
Then you can follow [GPTQ-for-LLaMa](https://github.com/qwopqwop200/GPTQ-for-LLaMa). This lib provides efficient CUDA kernels and weight convertion script.
After installing this lib, we may convert the original HF-format LLaMA model weights to 4-bit version.
Run this command in your cloned `GPTQ-for-LLaMa` directory, then you will get a 4-bit weight file `llama7b-4bit-128g.pt`.
**Troubleshooting**: if you get error about `position_ids`, you can checkout to commit `50287c3b9ae4a3b66f6b5127c643ec39b769b155`(`GPTQ-for-LLaMa` repo).
For more details, see [`inference/`](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat/inference).
We have integrated the Transformers save and load pipeline, allowing users to freely call Hugging Face's language models and save them in the HF format.
Referring to the successful attempts of [BLOOM](https://bigscience.huggingface.co/) and [Stable Diffusion](https://en.wikipedia.org/wiki/Stable_Diffusion), any and all developers and partners with computing powers, datasets, models are welcome to join and build the Colossal-AI community, making efforts towards the era of big AI models from the starting point of replicating ChatGPT!
You may contact us or participate in the following ways:
1. [Leaving a Star ⭐](https://github.com/hpcaitech/ColossalAI/stargazers) to show your like and support. Thanks!
2. Posting an [issue](https://github.com/hpcaitech/ColossalAI/issues/new/choose), or submitting a PR on GitHub follow the guideline in [Contributing](https://github.com/hpcaitech/ColossalAI/blob/main/CONTRIBUTING.md).
title = {LoRA: Low-Rank Adaptation of Large Language Models},
author = {Edward J. Hu and Yelong Shen and Phillip Wallis and Zeyuan Allen-Zhu and Yuanzhi Li and Shean Wang and Weizhu Chen},
journal = {ArXiv},
year = {2021},
volume = {abs/2106.09685}
}
@article{ouyang2022training,
title={Training language models to follow instructions with human feedback},
author={Ouyang, Long and Wu, Jeff and Jiang, Xu and Almeida, Diogo and Wainwright, Carroll L and Mishkin, Pamela and Zhang, Chong and Agarwal, Sandhini and Slama, Katarina and Ray, Alex and others},
journal={arXiv preprint arXiv:2203.02155},
year={2022}
}
@article{touvron2023llama,
title={LLaMA: Open and Efficient Foundation Language Models},
author={Touvron, Hugo and Lavril, Thibaut and Izacard, Gautier and Martinet, Xavier and Lachaux, Marie-Anne and Lacroix, Timoth{\'e}e and Rozi{\`e}re, Baptiste and Goyal, Naman and Hambro, Eric and Azhar, Faisal and Rodriguez, Aurelien and Joulin, Armand and Grave, Edouard and Lample, Guillaume},
journal={arXiv preprint arXiv:2302.13971},
year={2023}
}
@misc{alpaca,
author = {Rohan Taori and Ishaan Gulrajani and Tianyi Zhang and Yann Dubois and Xuechen Li and Carlos Guestrin and Percy Liang and Tatsunori B. Hashimoto },
title = {Stanford Alpaca: An Instruction-following LLaMA model},
