Text Generation
Transformers
PyTorch
mistral
openchat
C-RLFT
conversational
Inference Endpoints
text-generation-inference
Edit model card

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

GitHub Repo β€’ Online Demo β€’ Discord β€’ Twitter β€’ Huggingface β€’ Paper

πŸ”₯ The first 7B model Achieves Comparable Results with ChatGPT (March)! πŸ”₯

πŸ€– #1 Open-source model on MT-bench scoring 7.81, outperforming 70B models πŸ€–

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.

DOI

Usage

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)
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"}]
  }'

Coding Mode

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."}]
  }'
Model Size Context Weights Serving
OpenChat 3.5 7B 8192 Huggingface python -m ochat.serving.openai_api_server --model openchat/openchat_3.5 --engine-use-ray --worker-use-ray

For inference with Huggingface Transformers (slow and not recommended), follow the conversation template provided below.

Conversation templates (click to expand)
import transformers
tokenizer = transformers.AutoTokenizer.from_pretrained("openchat/openchat_3.5")

# Single-turn
tokens = tokenizer("GPT4 Correct User: Hello<|end_of_turn|>GPT4 Correct Assistant:").input_ids
assert tokens == [1, 420, 6316, 28781, 3198, 3123, 1247, 28747, 22557, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747]

# Multi-turn
tokens = tokenizer("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:").input_ids
assert 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]

# Coding Mode
tokens = tokenizer("Code User: Implement quicksort using C++<|end_of_turn|>Code Assistant:").input_ids
assert tokens == [1, 7596, 1247, 28747, 26256, 2936, 7653, 1413, 334, 1680, 32000, 7596, 21631, 28747]

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

messages = [
    {"role": "user", "content": "Hello"},
    {"role": "assistant", "content": "Hi"},
    {"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)
assert 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]

Comparison with X.AI Grok models

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! πŸš€πŸ’‘

(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-Ξ²^ 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-Ξ² 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:

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}
}

πŸ’Œ Contact

Project Lead:

  • Guan Wang [imonenext at gmail dot com]
  • Alpay Ariyak [aariyak at wpi dot edu]
Downloads last month
18,698

Datasets used to train openchat/openchat_3.5

Spaces using openchat/openchat_3.5 100

Collection including openchat/openchat_3.5