Llama-2-7b-chat-hf-function-calling-v2
| Property | Value |
|---|---|
| Base Model | Llama 2 7B |
| Context Length | 4K tokens |
| License | Meta Community License |
| Training Data | Trelis Function Calling Extended Dataset |
What is Llama-2-7b-chat-hf-function-calling-v2?
This model is a specialized version of Meta's Llama 2 7B, enhanced with function calling capabilities. It enables structured API interactions through a JSON-based interface, allowing developers to define functions with specific parameters and receive structured responses. The v2 version introduces improved syntax and better function handling compared to its predecessor.
Implementation Details
The model implements a streamlined approach to function calling, requiring only function descriptions for inference without additional instructions. It supports various argument types including strings, numbers, and arrays, with the ability to handle multiple function calls with up to three arguments.
- Simplified syntax with function descriptions outside system prompts
- JSON-structured function definitions and responses
- Support for multiple argument types and arrays
- Compatible with various prompt templates (Llama, DeepSeek, Yi)
Core Capabilities
- Structured function calling with JSON responses
- Support for complex argument types and multiple parameters
- Integration with text generation interfaces and chat UIs
- Flexible deployment options (local, cloud, or API-based)
Frequently Asked Questions
Q: What makes this model unique?
This model's primary advantage is its ability to handle structured function calls while maintaining the base Llama 2 capabilities. The v2 version specifically improves upon the original by simplifying the syntax and moving function descriptions outside the system prompt, leading to more reliable function calling behavior.
Q: What are the recommended use cases?
The model is ideal for applications requiring structured API interactions, chatbots with specific function capabilities, and systems requiring formatted data responses. It's particularly useful for scenarios where you need to bridge natural language understanding with programmatic function execution.
