openchat/openchat_v3.1

OpenChat: Advancing Open-source Language Models with Imperfect Data

OpenChat is a series of open-source language models based on supervised fine-tuning (SFT). We leverage the ~80k ShareGPT conversations with a conditioning strategy and weighted loss to achieve remarkable performance despite our simple methods. Our final vision is to develop a high-performance, open-source, and commercially available large language model, and we are continuously making progress.

🔥 Rank #1 of 13B open-source models | 89.5% win-rate on AlpacaEval | 7.01 score on MT-bench

💲 FREE for commercial use under Llama 2 Community License

🕒 Super efficient padding-free finetuning for applications, only 10 hours on 8xA100 80G

Usage

To use these models, we highly recommend installing the OpenChat package by following the installation guide 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 GPU with at least 48GB RAM or two consumer GPUs with tensor parallelism. To enable tensor parallelism, append --tensor-parallel-size 2 to the serving command.

When started, the server listens at localhost:18888 for requests and is compatible with the OpenAI ChatCompletion API specifications. See the example request below for reference. Additionally, you can access the OpenChat Web UI for a user-friendly experience.

To deploy the server as an online service, 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. We recommend using a HTTPS gateway in front of the server for security purposes.

Note: If IPv6 address errors occur, which is a vLLM issue, please run export NCCL_IGNORE_DISABLED_P2P=1 before starting the server.

Example request (click to expand)

curl http://localhost:18888/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "openchat_v3.2",
    "messages": [{"role": "user", "content": "You are a large language model named OpenChat. Write a poem to describe yourself"}]
  }'

Model	Size	Context	Weights	Serving
OpenChat 3.2	13B	4096	Huggingface	python -m ochat.serving.openai_api_server --model-type openchat_v3.2 --model openchat/openchat_v3.2 --engine-use-ray --worker-use-ray --max-num-batched-tokens 5120
OpenChat 3.1	13B	4096	Huggingface	python -m ochat.serving.openai_api_server --model-type openchat_v3.1_llama2 --model openchat/openchat_v3.1 --engine-use-ray --worker-use-ray --max-num-batched-tokens 5120

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

Conversation templates (click to expand)

V3.2

# Single-turn V3.2
tokenize("GPT4 User: Hello<|end_of_turn|>GPT4 Assistant:")
# Result: [1, 402, 7982, 29946, 4911, 29901, 15043, 32000, 402, 7982, 29946, 4007, 22137, 29901]

# Multi-turn V3.2
tokenize("GPT4 User: Hello<|end_of_turn|>GPT4 Assistant: Hi<|end_of_turn|>GPT4 User: How are you today?<|end_of_turn|>GPT4 Assistant:")
# Result: [1, 402, 7982, 29946, 4911, 29901, 15043, 32000, 402, 7982, 29946, 4007, 22137, 29901, 6324, 32000, 402, 7982, 29946, 4911, 29901, 1128, 526, 366, 9826, 29973, 32000, 402, 7982, 29946, 4007, 22137, 29901]

V3.1

# Single-turn V3.1
tokenize("Assistant is GPT4<|end_of_turn|>User: Hello<|end_of_turn|>Assistant:")
# Result: [1, 4007, 22137, 338, 402, 7982, 29946, 32000, 4911, 29901, 15043, 32000, 4007, 22137, 29901]

# Multi-turn V3.1
tokenize("Assistant is GPT4<|end_of_turn|>User: Hello<|end_of_turn|>Assistant: Hi<|end_of_turn|>User: How are you today?<|end_of_turn|>Assistant:")
# Result: [1, 4007, 22137, 338, 402, 7982, 29946, 32000, 4911, 29901, 15043, 32000, 4007, 22137, 29901, 6324, 32000, 4911, 29901, 1128, 526, 366, 9826, 29973, 32000, 4007, 22137, 29901]

Benchmarks

We have evaluated our models using the two most popular evaluation benchmarks **, including AlpacaEval and MT-bench. Here we list the top models with our released versions, sorted by model size in descending order. The full version can be found on the MT-bench and AlpacaEval leaderboards.

To ensure consistency, we used the same routine as ChatGPT / GPT-4 to run these benchmarks. We started the OpenAI API-compatible server and set the openai.api_base to http://localhost:18888/v1 in the benchmark program.

Model	Size	Context	💲Free	AlpacaEval (win rate %)	MT-bench (win rate adjusted %)	MT-bench (score)
				v.s. text-davinci-003	v.s. ChatGPT
GPT-4	1.8T*	8K	❌	95.3	82.5	8.99
ChatGPT	175B*	4K	❌	89.4	50.0	7.94
Llama-2-70B-Chat	70B	4K	✅	92.7		6.86
OpenChat 3.2	13B	4K	✅	89.1	51.6	7.01
OpenChat 3.1	13B	4K	✅	89.5	50.0	6.65
Llama-2-13B-Chat	13B	4K	✅	81.0		6.65
Vicuna 1.3	13B	2K	❌	82.1	37.5	6.00

*: Estimated model size

**: The benchmark metrics represent a quantified measure of a subset of the model's capabilities. A win-rate greater than 50% does not necessarily indicate that the model is better than ChatGPT in all scenarios or for all use cases. It is essential to consider the specific tasks or applications for which the model was evaluated and compare the results accordingly.

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.

License

Our OpenChat V3 models are licensed under the Llama 2 Community License.

@misc{wang2023openchat,
      title={OpenChat: Advancing Open-source Language Models with Mixed-Quality Data}, 
      author={Guan Wang and Sijie Cheng and Xianyuan Zhan and Xiangang Li and Sen Song and Yang Liu},
      year={2023},
      eprint={2309.11235},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}