CodeActAgent-Mistral-7b-v0.1
| Property | Value |
|---|---|
| Base Model | Mistral-7B v0.1 |
| Context Window | 32k tokens |
| Author | xingyaoww |
| Paper | arXiv:2402.01030 |
What is CodeActAgent-Mistral-7b-v0.1?
CodeActAgent-Mistral-7b-v0.1 is an innovative language model that implements the CodeAct framework, which uses executable Python code as a unified action space for LLM agents. Built on the Mistral-7B architecture, this model represents a significant advancement in how AI agents interact with tools and execute tasks, showing up to 20% higher success rates compared to traditional text and JSON-based approaches.
Implementation Details
The model is trained on CodeActInstruct, a dataset of 7,000 multi-turn interactions, and is designed to handle both tool-based tasks and general conversations. It features a 32k context window and integrates directly with a Python interpreter for executing code actions.
- Unified action space through executable Python code
- Dynamic action revision based on execution results
- Multi-turn interaction capability
- Built on Mistral-7B base model
Core Capabilities
- Superior performance in out-of-domain agent tasks
- Maintained general conversation abilities
- Real-time code execution and result interpretation
- Extended context handling (32k tokens)
- Tool use and API interaction
Frequently Asked Questions
Q: What makes this model unique?
The model's unique feature is its use of executable Python code as the primary action mechanism, allowing for more precise and verifiable agent actions compared to traditional text or JSON-based approaches. This results in significantly improved performance in tool-use scenarios while maintaining strong general-purpose capabilities.
Q: What are the recommended use cases?
The model excels in scenarios requiring tool interaction, API usage, and complex multi-step tasks. It's particularly well-suited for automation tasks, data processing, and situations where precise programmatic actions are needed, while still maintaining strong performance in general conversation and knowledge-based interactions.
