openchat-3.5-1210

Maintainer: openchat

Total Score

277

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-3.5-1210 model is a 7B parameter AI model developed by the openchat team. It is the "Overall Best Performing Open Source 7B Model" according to the maintainers, outperforming ChatGPT (March) and Grok-1 on several benchmarks. The model is capable of both coding and general language tasks, with a 15-point improvement in Coding over the previous OpenChat-3.5 model.

The openchat-3.5-0106 and openchat_3.5 are similar high-performing open-source models from the same team, with the openchat_3.5-awq and openchat-3.5-1210-gguf variants also available. All these models leverage the team's C-RLFT (Constrained Reinforcement Learning from Trajectories) fine-tuning approach to achieve exceptional results from limited training data.

Model inputs and outputs

Inputs

  • Text prompts: The model can take in text prompts from users, which can include instructions, questions, or open-ended requests.
  • Conversation history: The model is designed to maintain context across multiple turns of a conversation, allowing users to build upon previous exchanges.
  • Conditional inputs: The model supports setting a "condition" (e.g. "Code", "Math Correct") to adjust its behavior for specialized tasks.

Outputs

  • Generated text: The primary output of the model is coherent, contextually relevant text generated in response to the input prompts.
  • Code generation: The model can generate code snippets when provided with appropriate programming prompts.
  • Numeric outputs: The model can perform basic mathematical reasoning and provide numeric outputs for problems.

Capabilities

The openchat-3.5-1210 model has demonstrated strong performance across a variety of benchmarks, including MT-Bench, HumanEval, and GSM8K. It outperforms both ChatGPT (March) and the proprietary Grok-1 model on several tasks, showcasing its capabilities in areas like coding, mathematical reasoning, and general language understanding.

The model also supports specialized "Coding" and "Mathematical Reasoning" modes, which can be accessed by providing the appropriate conditional input. These modes allow the model to focus on more technical tasks and further enhance its capabilities in those domains.

What can I use it for?

The openchat-3.5-1210 model can be a valuable tool for a wide range of applications, from chatbots and virtual assistants to content generation and code development. Its strong performance on benchmarks suggests it could be useful for tasks like:

  • Chatbots and virtual assistants: The model's ability to maintain conversation context and generate coherent responses makes it suitable for building interactive chatbots and virtual assistants.
  • Content generation: The model can be used to generate creative writing, articles, and other types of text content.
  • Code development: The model's coding capabilities can be leveraged to assist with tasks like code generation, explanation, and debugging.
  • Educational applications: The model's mathematical reasoning abilities could be employed in educational tools and tutoring systems.

Things to try

One interesting aspect of the openchat-3.5-1210 model is its ability to adjust its behavior based on the provided "condition" input. For example, you could try prompting the model with a simple math problem and observe how it responds in the "Mathematical Reasoning" mode, compared to its more general language understanding capabilities.

Additionally, the model's strong performance on coding tasks suggests it could be a valuable tool for developers. You could try providing the model with various coding challenges or prompts and see how it handles them, exploring its capabilities in areas like algorithm design, syntax generation, and code explanation.



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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