openchat_3.5-awq

Maintainer: nateraw

Total Score

91

Last updated 6/21/2024
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Model Overview

openchat_3.5-awq is an innovative open-source language model developed by Replicate's nateraw. It is part of the OpenChat library, which includes a series of high-performing models fine-tuned using a strategy called C-RLFT (Contextual Reinforcement Learning from Feedback). This approach allows the models to learn from mixed-quality data without explicit preference labels, delivering exceptional performance on par with ChatGPT despite being a relatively compact 7B model.

The OpenChat models outperform other open-source alternatives like OpenHermes 2.5, OpenOrca Mistral, and Zephyr-Ξ² on various benchmarks, including reasoning, coding, and mathematical tasks. The latest version, openchat_3.5-0106, even surpasses the capabilities of ChatGPT (March) and Grok-1 on several key metrics.

Model Inputs and Outputs

Inputs

  • prompt: The input text prompt for the model to generate a response.
  • max_new_tokens: The maximum number of tokens the model should generate as output.
  • temperature: The value used to modulate the next token probabilities.
  • top_p: A probability threshold for generating the output. If < 1.0, only keep the top tokens with cumulative probability >= top_p (nucleus filtering).
  • top_k: The number of highest probability tokens to consider for generating the output. If > 0, only keep the top k tokens with highest probability (top-k filtering).
  • prompt_template: The template used to format the prompt. The input prompt is inserted into the template using the {prompt} placeholder.
  • presence_penalty: The penalty applied to tokens based on their presence in the generated text.
  • frequency_penalty: The penalty applied to tokens based on their frequency in the generated text.

Outputs

  • The model generates a sequence of tokens as output, which can be concatenated to form the model's response.

Capabilities

openchat_3.5-awq demonstrates strong performance in a variety of tasks, including:

  • Reasoning and Coding: The model outperforms ChatGPT (March) and other open-source alternatives on coding and reasoning benchmarks like HumanEval, BBH MC, and AGIEval.
  • Mathematical Reasoning: The model achieves state-of-the-art results on mathematical reasoning tasks like GSM8K, showcasing its ability to tackle complex numerical problems.
  • General Language Understanding: The model performs well on MMLU, a broad benchmark for general language understanding, indicating its versatility in handling diverse language tasks.

What Can I Use It For?

The openchat_3.5-awq model can be leveraged for a wide range of applications, such as:

  • Conversational AI: The model can be deployed as a conversational agent, engaging users in natural language interactions and providing helpful responses.
  • Content Generation: The model can be used to generate high-quality text, such as articles, stories, or creative writing, by fine-tuning on specific domains or datasets.
  • Task-oriented Dialogue: The model can be fine-tuned for task-oriented dialogues, such as customer service, technical support, or virtual assistance.
  • Code Generation: The model's strong performance on coding tasks makes it a valuable tool for automating code generation, programming assistance, or code synthesis.

Things to Try

Here are some ideas for what you can try with openchat_3.5-awq:

  • Explore the model's capabilities: Test the model on a variety of tasks, such as open-ended conversations, coding challenges, or mathematical problems, to understand its strengths and limitations.
  • Fine-tune the model: Leverage the model's strong foundation by fine-tuning it on your specific dataset or domain to create a customized language model for your applications.
  • Combine with other technologies: Integrate the model with other AI or automation tools, such as voice interfaces or robotic systems, to create more comprehensive and intelligent solutions.
  • Contribute to the open-source ecosystem: As an open-source model, you can explore ways to improve or extend the OpenChat library, such as by contributing to the codebase, providing feedback, or collaborating on 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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