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

💬Online Demo of OpenChat 3.5 | 💻GitHub | 📃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.

✨ News

[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.

🏷️ Benchmarks

Model	# Params	Average	MT-Bench	AGIEval	BBH MC	TruthfulQA	MMLU	HumanEval	BBH CoT	GSM8K
OpenChat-3.5	7B	61.6	7.81	47.4	47.6	59.1	64.3	55.5	63.5	77.3
ChatGPT (March)*	?	61.5	7.94	47.1	47.6	57.7	67.3	48.1	70.1	74.9

OpenHermes 2.5	7B	59.3	7.54	46.5	49.4	57.5	63.8	48.2	59.9	73.5
OpenOrca Mistral	7B	52.7	6.86	42.9	49.4	45.9	59.3	38.4	58.1	59.1
Zephyr-β^	7B	34.6	7.34	39.0	40.6	40.8	39.8	22.0	16.0	5.1
Mistral**	7B	-	6.84	38.0	39.0	-	60.1	30.5	-	52.2
Open-source SOTA**	13B-70B	61.4	7.71	41.7	49.7	62.3	63.7	73.2	41.4	82.3
			WizardLM 70B	Orca 13B	Orca 13B	Platypus2 70B	WizardLM 70B	WizardCoder 34B	Flan-T5 11B	MetaMath 70B

🎇 Comparison with X.AI Grok

	License	# Param	Average	MMLU	HumanEval	MATH	GSM8k
OpenChat 3.5	Apache-2.0	7B	56.4	64.3	55.5	28.6	77.3
Grok-0	Proprietary	33B	44.5	65.7	39.7	15.7	56.8
Grok-1	Proprietary	?	55.8	73	63.2	23.9	62.9

💌Contact

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!

Citation

@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}
}

Acknowledgements

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..