To use this model, we highly recommend installing the OpenChat package by following the installation guide in our repository and using the OpenChat OpenAI-compatible API server by running the serving command from the table below. The server is optimized for high-throughput deployment using vLLM and can run on a consumer GPU with 24GB RAM. To enable tensor parallelism, append --tensor-parallel-size N to the serving command.
Once started, the server listens at localhost:18888 for requests and is compatible with the OpenAI ChatCompletion API specifications. Please refer to the example request below for reference. Additionally, you can use the OpenChat Web UI for a user-friendly experience.
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.
💡 Default Mode (GPT4 Correct): Best for coding, chat and general tasks
bash
1curl http://localhost:18888/v1/chat/completions \2 -H "Content-Type: application/json"\3 -d '{
4 "model": "openchat_3.5",
5 "messages": [{"role": "user", "content": "You are a large language model named OpenChat. Write a poem to describe yourself"}]
6 }'
🧮 Mathematical Reasoning Mode: Tailored for solving math problems
💡 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:
python
1messages =[2{"role":"user","content":"Hello"},3{"role":"assistant","content":"Hi"},4{"role":"user","content":"How are you today?"}5]6tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)7assert tokens ==[1,420,6316,28781,3198,3123,1247,28747,22557,32000,420,6316,28781,3198,3123,21631,28747,15359,32000,420,6316,28781,3198,3123,1247,28747,1602,460,368,3154,28804,32000,420,6316,28781,3198,3123,21631,28747]
(Experimental) Evaluator / Feedback Capabilities
We've included evaluator capabilities in this release to advance open-source models as evaluators. You can use `Default Mode (GPT4 Correct)` with the following prompt (same as [Prometheus](https://huggingface.co/datasets/kaist-ai/Feedback-Collection)) to evaluate a response.
###Task Description:
An instruction (might include an Input inside it), a response to evaluate, a reference answer that gets a score of 5, and a score rubric representing a evaluation criteria are given.
1. Write a detailed feedback that assess the quality of the response strictly based on the given score rubric, not evaluating in general.
2. After writing a feedback, write a score that is an integer between 1 and 5. You should refer to the score rubric.
3. The output format should look as follows: "Feedback: (write a feedback for criteria) [RESULT] (an integer number between 1 and 5)"
4. Please do not generate any other opening, closing, and explanations.
###The instruction to evaluate:
{orig_instruction}
###Response to evaluate:
{orig_response}
###Reference Answer (Score 5):
{orig_reference_answer}
###Score Rubrics:
[{orig_criteria}]
Score 1: {orig_score1_description}
Score 2: {orig_score2_description}
Score 3: {orig_score3_description}
Score 4: {orig_score4_description}
Score 5: {orig_score5_description}
###Feedback:
Benchmarks
Model
# Params
Average
MT-Bench
HumanEval
BBH MC
AGIEval
TruthfulQA
MMLU
GSM8K
BBH CoT
OpenChat-3.5-1210
7B
63.8
7.76
68.9
49.5
48.0
61.8
65.3
77.3
61.8
OpenChat-3.5
7B
61.6
7.81
55.5
47.6
47.4
59.1
64.3
77.3
63.5
ChatGPT (March)*
?
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
-
Evaluation Details(click to expand)
*: ChatGPT (March) results are from [GPT-4 Technical Report](https://arxiv.org/abs/2303.08774), [Chain-of-Thought Hub](https://github.com/FranxYao/chain-of-thought-hub), and our evaluation. Please note that ChatGPT is not a fixed baseline and evolves rapidly over time.
^: 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 in our repository.
⚠️ Note that this model was not explicitly trained in Chinese (only < 0.1% of the data is in Chinese). 请注意本模型没有针对性训练中文（中文数据占比小于0.1%）。
Multi-Level Multi-Discipline Chinese Evaluation Suite (CEVAL)
Model
Avg
STEM
Social Science
Humanities
Others
ChatGPT
54.4
52.9
61.8
50.9
53.6
OpenChat
47.29
45.22
52.49
48.52
45.08
Massive Multitask Language Understanding in Chinese (CMMLU, 5-shot)
Models
STEM
Humanities
SocialSciences
Other
ChinaSpecific
Avg
ChatGPT
47.81
55.68
56.5
62.66
50.69
55.51
OpenChat
38.7
45.99
48.32
50.23
43.27
45.85
Limitations
Foundation Model Limitations
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
Hallucination of Non-existent Information
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 from the model.
Safety
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.
License
Our OpenChat 3.5 code and models are distributed under the Apache License 2.0.
Dataset Details
OpenChat 3.5 was trained with C-RLFT on a collection of publicly available high-quality instruction data, with a custom processing pipeline. We detail some notable subsets included here:
@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}
}
💌 Contact
We are a student team 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:
We look forward to hearing you and collaborating on this exciting project!
Acknowledgments
We extend our heartfelt gratitude to AutoMeta and caesus from Alignment Lab AI, LDJ and Teknium from Nous Research, alpin and TearGosling from Pygmalion AI for their substantial contributions to data collection and model training.
Special thanks go to Changling Liu from 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.
