NexusRaven-V2-13B
| Property | Value |
|---|---|
| Base Model | CodeLlama-13b-Instruct-hf |
| License | Nexusflow Community License |
| Paper | Research Paper |
| Primary Use | Function Calling |
What is NexusRaven-V2-13B?
NexusRaven-V2-13B is a state-of-the-art language model specifically designed for function calling capabilities. Built on the CodeLlama architecture, it represents a significant advancement in AI-powered function calling, outperforming even GPT-4 by 7% in complex scenarios. The model is particularly notable for its commercial viability and open-source nature, trained without reliance on proprietary LLM data.
Implementation Details
The model is implemented using PyTorch and operates within the Transformers framework. It specializes in processing Python functions with well-defined signatures and docstrings, capable of handling both simple and complex nested function calls.
- Built on CodeLlama-13b-Instruct-hf architecture
- Optimized for zero-shot function calling
- Supports both CPU and GPU deployment with automatic device mapping
- Implements temperature control for precise output generation
Core Capabilities
- Generates single, nested, and parallel function calls
- Provides detailed explanations for generated function calls
- Achieves superior performance in human-generated use cases
- Handles unseen functions effectively through zero-shot learning
- Supports integration with OpenAI function calling schematics
Frequently Asked Questions
Q: What makes this model unique?
NexusRaven-V2-13B stands out for its superior function calling capabilities, commercial-friendly licensing, and ability to surpass GPT-4 in specific function calling tasks. It's particularly notable for being trained without proprietary LLM data while maintaining high performance.
Q: What are the recommended use cases?
The model excels in scenarios requiring complex function calling, API integration, and automated task execution. It's particularly well-suited for commercial applications requiring detailed function manipulation and explanation generation. The model works best when connected with a retriever for handling multiple functions.
