openchat

Maintainer: openchat

Total Score

289

Last updated 5/28/2024

🗣️

PropertyValue
Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided

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Model overview

The openchat model is a series of open-source language models fine-tuned on a diverse and high-quality dataset of multi-round conversations. According to the maintainer, the OpenChat models are designed to achieve high performance with limited data, with only ~6K GPT-4 conversations filtered from the ~90K ShareGPT conversations used for fine-tuning.

The OpenChat-3.5-0106 model in particular is described as the "Overall Best Performing Open Source 7B Model" for coding, generalization, and mathematical reasoning tasks. It outperforms both ChatGPT (March) and the proprietary Grok-1 model on various benchmarks.

Model inputs and outputs

The openchat model accepts conversational inputs in a specific format, with an <|end_of_turn|> token marking the end of each turn. The model can operate in different modes, including a "Default Mode (GPT4 Correct)" for general tasks and a "Mathematical Reasoning Mode" tailored for solving math problems.

Inputs

  • Conversational inputs: The model expects a sequence of conversational turns, with each turn separated by the <|end_of_turn|> token.
  • Mode selection: The model can be instructed to operate in different modes, such as "Default Mode (GPT4 Correct)" or "Mathematical Reasoning Mode", by including a mode identifier in the input.

Outputs

  • Conversational responses: The model generates a response to the provided conversational input, which can be used to continue the conversation.
  • Task-specific outputs: Depending on the mode, the model can produce outputs tailored for tasks like mathematical problem-solving or general language understanding.

Capabilities

The openchat-3.5-0106 model excels at a variety of tasks, including summarization, question answering, extraction, and classification. It has demonstrated strong performance on benchmarks like MT-Bench, HumanEval, and GSM8K, often outperforming larger proprietary models.

What can I use it for?

The openchat models are suitable for a wide range of applications, from building open-source chatbots and virtual assistants to integrating language understanding capabilities into educational or creative tools. The maintainers encourage using the models for research purposes, such as probing the limitations and biases of dialogue models or exploring safe deployment strategies.

Things to try

One interesting aspect of the openchat models is their ability to operate in different modes, allowing users to tailor the model's behavior to specific types of tasks. For example, you could experiment with the "Mathematical Reasoning Mode" to see how the model performs on math-focused prompts, or try the "Default Mode (GPT4 Correct)" for more general language understanding and generation tasks.

Another area to explore is the model's few-shot capabilities, as the maintainers note that the model often performs even better with few-shot prompts. This could be a valuable avenue for further research and development.



This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!

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