OpenChat: Advancing Open-source Language Models with Mixed-Quality Data

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

• 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.

✨ News

• [2024/05/22] We released the Llama-3 based version OpenChat 3.6 20240522, outperforming official Llama 3 8B Instruct and open-source finetunes/merges.

• [2024/01/06] We released the second update, OpenChat 3.5 0106, further improved coding and overall performance 🏆.

• [2023/12/10] We released the first update, OpenChat 3.5 1210, improved coding by 15 points 🚀.

• [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.

Read more

• [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.

🏷️ Benchmarks - OpenChat 3.6

Reproducing benchmarks

Note: Please run the following commands at the base directory of this repository.

python -m ochat.evaluation.run_eval --condition "GPT4 Correct" --model openchat/openchat-3.6-8b-20240522 --eval_sets fs_cothub/mmlu fs_cothub/gsm8k fs_cothub/math
python -m ochat.evaluation.run_eval --condition "GPT4" --model openchat/openchat-3.6-8b-20240522 --eval_sets zs/gpqa

HumanEval is run using the official EvalPlus repository.

🏷️ Benchmarks - OpenChat 3.5

Model	# Params	Average	MT-Bench	HumanEval	BBH MC	AGIEval	TruthfulQA	MMLU	GSM8K	BBH CoT
OpenChat-3.5-0106	7B	64.5	7.8	71.3	51.5	49.1	61.0	65.8	77.4	62.2
ChatGPT (March)*	???B	61.5	7.94	48.1	47.6	47.1	57.7	67.3	74.9	70.1
OpenHermes 2.5	7B	59.3	7.54	48.2	49.4	46.5	57.5	63.8	73.5	59.9
OpenOrca Mistral	7B	52.7	6.86	38.4	49.4	42.9	45.9	59.3	59.1	58.1
Zephyr-β^	7B	34.6	7.34	22.0	40.6	39.0	40.8	39.8	5.1	16.0
Mistral	7B	-	6.84	30.5	39.0	38.0	-	60.1	52.2	-
Open-source SOTA**	13B-70B	61.4	7.71	73.2	49.7	41.7	62.3	63.7	82.3	41.4
			WizardLM 70B	WizardCoder 34B	Orca 13B	Orca 13B	Platypus2 70B	WizardLM 70B	MetaMath 70B	Flan-T5 11B

🔥 OpenChat-3.5-0106 (7B) now outperforms Grok-0 (33B) on all 4 benchmarks and Grok-1 (314B) on average and 3/4 benchmarks.

	License	# Param	Average	MMLU	HumanEval	MATH	GSM8k
OpenChat-3.5-0106	Apache-2.0	7B	61.0	65.8	71.3	29.3	77.4
Grok-0	Proprietary	33B	44.5	65.7	39.7	15.7	56.8
Grok-1	Proprietary	314B	55.8	73	63.2	23.9	62.9

Evaluation details

*: ChatGPT (March) results are from GPT-4 Technical Report, Chain-of-Thought Hub, and our evaluation.

^: Zephyr-β often fails to follow few-shot CoT instructions, likely because it was aligned with only chat data but not trained on few-shot data.

**: Mistral and Open-source SOTA results are taken from reported results in instruction-tuned model papers and official repositories.

All models are evaluated in chat mode (e.g. with the respective conversation template applied). All zero-shot benchmarks follow the same setting as in the AGIEval paper and Orca paper. CoT tasks use the same configuration as Chain-of-Thought Hub, HumanEval is evaluated with EvalPlus, and MT-bench is run using FastChat. To reproduce our results, follow the instructions below.

Reproducing benchmarks

Reasoning and Coding:

Note: Please run the following commands at the base directory of this repository.

python -m ochat.evaluation.run_eval --condition "GPT4 Correct" --model openchat/openchat-3.5-0106 --eval_sets coding fs_cothub/bbh fs_cothub/mmlu zs/agieval zs/bbh_mc_orca zs/truthfulqa_orca
python ochat/evaluation/view_results.py
python ochat/evaluation/convert_to_evalplus.py

Then all humaneval code samples are placed in ochat/evaluation/evalplus_codegen. Use the following command to evaluate an individual code sample named samples.jsonl using Docker as a sandbox.

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

Mathematical Reasoning:

Note: Please run the following commands at the base directory of this repository.

python -m ochat.evaluation.run_eval --condition "Math Correct" --model openchat/openchat-3.5-0106 --eval_sets fs_cothub/gsm8k zs/math
python ochat/evaluation/view_results.py

MT-Bench:

Please first launch a local API server, then download FastChat and run the following commands.

Note: Due to non-zero temperature and GPT-4 API changes over time, there might be variations in the results.

cd fastchat/llm_judge
python gen_api_answer.py --model openchat-3.5-0106 --max-tokens 4096 --parallel 128 --openai-api-base http://localhost:18888/v1
python gen_judgment.py --model-list openchat-3.5-0106 --parallel 8 --mode single

⬇️ Installation

[!NOTE] Need pytorch and CUDA to run OpenChat

pip

pip3 install ochat

[!IMPORTANT] If you are facing package compatibility issues with pip, try the conda method below or check this issue

conda

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

pip3 install ochat

Windows (WSL 1.x, Ubuntu-22.04)

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

From source

Clone this repo and install openchat from source in editable mode

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

pip3 install --upgrade pip  # enable PEP 660 support
pip3 install -e .  # Editable mode, you can make changes in this cloned repo

🚀 Deploying API server

⚡ 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.

List of currently supported models

MODEL_TYPE	MODEL_REPO	License
openchat_3.6	openchat/openchat-3.6-8b-20240522	Llama 3
openchat_3.5	openchat/openchat-3.5-0106	Apache 2.0

For a single GPU (e.g. RTX 3090, 4090)

python -m ochat.serving.openai_api_server --model MODEL_REPO

For multiple GPUs (tensor parallel)

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

use -h to see more settings

python -m ochat.serving.openai_api_server --model MODEL_REPO -h

Deploy as online service

If you want to deploy the server as an online service, you can use --api-keys sk-KEY1 sk-KEY2 ... to specify allowed API keys and --disable-log-requests --disable-log-stats --log-file openchat.log for logging only to a file. For security purposes, we recommend using an HTTPS gateway in front of the server.

Request example

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

💡 Default Mode (GPT4 Correct): Best for coding, chat and general tasks

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

🧮 Mathematical Reasoning Mode: Tailored for solving math problems

curl http://localhost:18888/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "MODEL_TYPE",
    "condition": "Math Correct",
    "messages": [{"role": "user", "content": "10.3 − 7988.8133 = "}]
  }'

🌐 Web UI - OpenChat-UI

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

🤗 Inference with Transformers

[!WARNING] It's recommended to use our optimized API server for deployment. Inferencing with Transformers will be slower.

💡 Default Mode (GPT4 Correct): Best for coding, chat and general tasks

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:

🧮 Mathematical Reasoning Mode: Tailored for solving math problems

Math Correct User: 10.3 − 7988.8133=<|end_of_turn|>Math Correct Assistant:

⚠️ Notice: Remember to set <|end_of_turn|> as end of generation token.

The default (GPT4 Correct) template is also available as the integrated tokenizer.chat_template, which can be used instead of manually specifying the template.

🛠️ Training

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

Choose a base model

OpenChat supports Llama 3 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.

Base Model	Size	Weights (with EOT token)	Model Type	Recommended Batch Size per GPU (8xA100 80GB)
Llama 3	8B	imone/Llama-3-8B-fixed-special-embedding	openchat_3.6	40960
Mistral	7B	`imone/Mistral_7B