Trelis/Llama-2-7b-chat-hf-function-calling
Trelis/Llama-2-7b-chat-hf-function-calling is a 7 billion parameter Llama 2 chat model developed by Trelis, fine-tuned to extend the base Llama 2 architecture with robust function calling capabilities. This model is specifically optimized to respond with structured JSON arguments for function names and parameters, making it ideal for integrating external tools and APIs. It supports a 4096-token context length and is available in various quantized formats, including a GPTQ-trained version for enhanced accuracy and speed.
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Trelis/Llama-2-7b-chat-hf-function-calling Overview
This model, developed by Trelis, is a 7 billion parameter Llama 2 chat variant specifically enhanced with function calling capabilities. It is designed to extend the base Llama 2 architecture by enabling the model to respond with structured JSON objects, specifying function names and their arguments. This allows for seamless integration with external tools and APIs, facilitating advanced conversational AI applications.
Key Capabilities
- Function Calling: Generates structured JSON responses for function calls, including function name and arguments.
- Llama 2 Base: Built upon the Llama 2 7B chat model, inheriting its conversational strengths.
- Quantized Versions: Available in multiple quantization formats, including
bitsandbytes NF4,GGML, andGPTQ, with a dedicated GPTQ-trained version for improved accuracy and inference speed. - Structured Prompting: Utilizes a specific prompt format to define available functions and guide the model's function call responses.
Good For
- Tool-use and Agentic AI: Ideal for applications requiring the LLM to interact with external systems, databases, or APIs.
- Automated Workflows: Automating tasks by translating natural language requests into executable function calls.
- Developers: Integrating function-calling into Llama 2-based applications without extensive custom fine-tuning.
- Resource-constrained Environments: The availability of GGML and GPTQ versions makes it suitable for deployment on consumer hardware or devices like Macs.