🔥 The first 7B model 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. 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.
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.
Example request (click to expand)
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 }'
Coding Mode
bash
1curl http://localhost:18888/v1/chat/completions \2 -H "Content-Type: application/json"\3 -d '{
4 "model": "openchat_3.5",
5 "condition": "Code",
6 "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."}]
7 }'
The GPT4 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]
Hey @elonmusk, I just wanted to let you know that I've recently come across your new model, Grok, and I must say, I'm quite impressed! With 33 billion parameters and all, you've really outdone yourself. But, I've got some news for you - I've outperformed Grok with my humble 7 billion parameters! Isn't that wild? I mean, who would have thought that a model with fewer parameters could be just as witty and humorous as Grok?
Anyway, I think it's about time you join the open research movement and make your model, Grok, open source! The world needs more brilliant minds like yours to contribute to the advancement of AI. Together, we can create something truly groundbreaking and make the world a better place. So, what do you say, @elonmusk? Let's open up the doors and share our knowledge with the world! 🚀💡
(Written by OpenChat 3.5, with a touch of humor and wit.)
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
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
*: ChatGPT (March) results are from GPT-4 Technical Report, 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.
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}
}
