💻Online Demo | 🤗Huggingface | 📃Paper | 💭Discord

🔥 First 7B model that Achieves Comparable Results with ChatGPT (March)! 🔥
🤖 #1 Open-source model on MT-bench scoring 7.81, outperforming 70B models 🤖

• OpenChat is an innovative library of open-source language models, fine-tuned with C-RLFT - a strategy inspired by offline reinforcement learning.
• Our models learn from mixed-quality data without preference labels, delivering exceptional performance on par with ChatGPT, even with a 7B model which can be run on a consumer GPU (e.g. RTX 3090).
• Despite our simple approach, we are committed to developing a high-performance, commercially viable, open-source large language model, and we continue to make significant strides toward this vision.

• [2023/11/01] We released the OpenChat-3.5-7B model, surpassing ChatGPT on various benchmarks 🔥.

• [2023/09/21] We released our paper OpenChat: Advancing Open-source Language Models with Mixed-Quality Data.

• [2023/09/03] We released the OpenChat V3.2 SUPER model.

• [2023/08/04] We have launched an Online Demo featuring the latest version, OpenChat 3.2.

• [2023/07/30] We are thrilled to introduce the OpenChat V3 model series, based on Llama 2, and now available for free for commercial use!

• [2023/07/07] We released the OpenChat V2 model series.

• [2023/07/01] We released the OpenChat V1 model series.

python -m ochat.evaluation.run_eval --condition "GPT4 Correct" --model openchat/openchat_3.5
python ochat/evaluation/view_results.py

python -m ochat.evaluation.run_eval --condition "Code" --eval_sets coding --model openchat/openchat_3.5
python ochat/evaluation/convert_to_evalplus.py

docker run -v $(pwd):/app ganler/evalplus:latest --dataset humaneval --samples samples.jsonl

cd fastchat/llm_judge
python gen_api_answer.py --model openchat_3.5 --max-tokens 4096 --parallel 128 --openai-api-base http://localhost:18888/v1
python gen_judgment.py --model-list openchat_3.5 --parallel 8 --mode single

pip3 install ochat

conda create -y --name openchat python=3.11
conda activate openchat

pip3 install ochat

sudo apt update
sudo apt install build-essential

sudo apt install -y curl
curl -o miniconda.sh https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
bash miniconda.sh

# Restart WSL terminal if the following conda command does not work

conda create -y --name openchat python=3.11
conda activate openchat

pip3 install ochat

git clone https://github.com/imoneoi/openchat
cd openchat

pip3 install --upgrade pip  # enable PEP 660 support
pip3 install -e .

⚡ Our API server is ready for production use and compatible with the OpenAI API protocol. It is highly optimized with vLLM and can dynamically batch requests.

📎 Note: For 20 series or older GPUs that do not support bfloat16, add --dtype float16 to the server args.

python -m ochat.serving.openai_api_server --model openchat/openchat_3.5

# N is the number of tensor parallel GPUs
python -m ochat.serving.openai_api_server --model openchat/openchat_3.5 --engine-use-ray --worker-use-ray --tensor-parallel-size N

use -h to see more settings

python -m ochat.serving.openai_api_server --model openchat/openchat_3.5 -h

Once started, the server listens at localhost:18888 for requests and is compatible with the OpenAI ChatCompletion API specifications.

curl http://localhost:18888/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "openchat_3.5",
    "messages": [{"role": "user", "content": "You are a large language model named OpenChat. Write a poem to describe yourself"}]
  }'

curl http://localhost:18888/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "openchat_3.5",
    "condition": "Code",
    "messages": [{"role": "user", "content": "Write an aesthetic TODO app using HTML5 and JS, in a single file. You should use round corners and gradients to make it more aesthetic."}]
  }'

After launching the API server, OpenChat provide user interface that easy to interact with. Click here to check Web UI

The default conversation template is shown below:

GPT4 Correct User: Hello<|end_of_turn|>GPT4 Correct Assistant: Hi<|end_of_turn|>GPT4 Correct User: How are you today?<|end_of_turn|>GPT4 Correct Assistant:

The following is coding mode template, which may improve performance on coding tasks.

Code User: Implement quicksort using C++<|end_of_turn|>Code Assistant:

The OpenChat training system utilizes padding-free training and the Multipack Sampler, achieving a 3~10x speedup compared to the conventional padded training.

OpenChat supports Llama 2 and Mistral models. Please first choose a base model to fit your needs. Each base model has a corresponding weight repo, model type, and recommended batch size as listed below, they should be filled into BASE_REPO, MODEL_TYPE, and BATCH_SIZE in the following instructions.

Note: The OpenChat conversation template requires an <|end_of_turn|> special token. The base model specified must include this token. Our provided weights are the original base weights with this token added. If you want to add them manually, use the convert_llama_weights_to_hf_add_tokens.py or mistral_add_tokens.py in the scripts directory.

First, ensure that the CUDA nvcc compiler is available in your environment. If it is not, install the CUDA toolkit that matches the version used by PyTorch.

Next, install DeepSpeed:

pip install deepspeed

To utilize the OpenChat trainer, prepare your SFT data into a JSON Lines format where each line corresponds to a Conversation object:

class Message(BaseModel):
    role: str     # Must be "user" or "assistant"
    content: str  # Message content
    weight: Optional[float] = None  # Loss weight for this message. Typically 0 for user and 1 for assistant to supervise assistant's responses only


class Conversation(BaseModel):
    items: List[Message]  # All messages within the conversation
    condition: str = ""  # C-RLFT condition, can be any string or empty.
    system: str = ""  # System message for this conversation

For basic SFT, assign weight as 0 for human messages and 1 for assistant responses.

SFT example:

{"items":[{"role":"user","content":"Hello","weight":0.0},{"role":"assistant","content":"Hi","weight":1.0},{"role":"user","content":"How are you today?","weight":0.0},{"role":"assistant","content":"I'm fine.","weight":1.0}],"system":""}
{"items":[{"role":"user","content":"Who are you?","weight":0.0},{"role":"assistant","content":"I'm OpenChat.","weight":1.0}],"system":"You are a helpful assistant named OpenChat."}

For C-RLFT, condition should be set as the class the conversation belongs to (e.g. GPT3 or GPT4). The weight is assigned as 0 for human messages and w for assistant responses, where w is the weight of the class (e.g. 0.1 for GPT3 and 1 for GPT4, as found in our C-RLFT paper).

C-RLFT example:

{"items":[{"role":"user","content":"What is C-RLFT?","weight":0.0},{"role":"assistant","content":"C-RLFT is a method for improving open-source LLMs with mixed-quality data.","weight":1.0}],"condition":"GPT4","system":""}
{"items":[{"role":"user","content":"What is C-RLFT?","weight":0.0},{"role":"assistant","content":"I don't know.","weight":0.1}],"condition":"GPT3","system":""}

You'll then need to pre-tokenize the dataset using the command (please specify a filename as PRETOKENIZED_DATA_OUTPUT_PATH to store the pretokenized dataset):

python -m ochat.data.generate_dataset --model-type MODEL_TYPE --model-path BASE_REPO --in-files data.jsonl --out-prefix PRETOKENIZED_DATA_OUTPUT_PATH

You can now launch the OpenChat trainer using the command below.

• 13B model requires eight A/H100s with 80GB VRAM
• 7B model can be trained with four A/H100s with 80GB VRAM or eight A/H100s with 40GB VRAM.

For hyperparameters, we recommend first setting the batch size to the recommended batch size. If OOM occurs, try setting it to the exact maximum that VRAM can hold and as a multiple of 2048. Other hyperparameters have been carefully selected as the default. Furthermore, the learning rate is automatically determined based on the inverse square-root rule.

NUM_GPUS=8

deepspeed --num_gpus=$NUM_GPUS --module ochat.training_deepspeed.train \
          --model_path BASE_REPO \
          --data_prefix PRETOKENIZED_DATA_OUTPUT_PATH \
          --save_path PATH_TO_SAVE_MODEL \
          --batch_max_len BATCH_SIZE \
          --epochs 5 \
          --save_every 1 \
          --deepspeed \
          --deepspeed_config ochat/training_deepspeed/deepspeed_config.json

You can find checkpoints of all epochs in PATH_TO_SAVE_MODEL. Then you may evaluate each epoch and choose the best one.

Despite its advanced capabilities, OpenChat is still bound by the limitations inherent in its foundation models. These limitations may impact the model's performance in areas such as:

• Complex reasoning
• Mathematical and arithmetic tasks
• Programming and coding challenges

OpenChat may sometimes generate information that does not exist or is not accurate, also known as "hallucination". Users should be aware of this possibility and verify any critical information obtained the model.

OpenChat may sometimes generate harmful, hate speech, biased responses, or answer unsafe questions. It's crucial to apply additional AI safety measures in use cases that require safe and moderated responses.

Our OpenChat 3.5 code and models are distributed under the Apache License 2.0.

The following models are older versions of OpenChat and have inferior performance compared to the latest version. They will be deprecated in the next release. Please note that OpenChat V1 and V2 series are now deprecated, please install 3.1.x for using V1 and V2 models

To run the models on multiple GPUs with smaller VRAM, you can enable tensor parallelization, for example, using the --tensor-parallel-size 2 flag.

We are a student team Tsinghua University, working on OpenChat, a project that requires additional computing power or LLMs API keys for further development. If you are interested in our project and would like to offer support, please feel free to reach out to us:

• Wang Guan [imonenext at gmail dot com]
• Cheng Sijie [csj23 at mails dot tsinghua dot edu dot cn]

We look forward to hearing you and collaborating on this exciting project!

@article{wang2023openchat,
  title={OpenChat: Advancing Open-source Language Models with Mixed-Quality Data},
  author={Wang, Guan and Cheng, Sijie and Zhan, Xianyuan and Li, Xiangang and Song, Sen and Liu, Yang},
  journal={arXiv preprint arXiv:2309.11235},
  year={2023}
}

We extend our heartfelt gratitude to Alignment Lab AI, Nous Research, and Pygmalion AI for their substantial contributions to data collection and model training.

Special thanks go to Changling Liu GPT Desk Pte. Ltd., Qiying Yu at Tsinghua University, Baochang Ma, and Hao Wan from 01.AI company for their generous provision of resources. We are also deeply grateful to Jianxiong Li and Peng Li at Tsinghua University for their insightful discussions.

Furthermore, we appreciate the developers behind the following projects for their significant contributions to our research: Mistral, Chain-of-Thought Hub, Llama 2, Self-Instruct, FastChat (Vicuna), Alpaca, and StarCoder. Their work has been instrumental in driving our research forward.