rizerphe/CodeLlama-function-calling-6320-7b-Instruct-hf

Model Overview

rizerphe/CodeLlama-function-calling-6320-7b-Instruct-hf is a 7 billion parameter instruction-tuned model built upon the CodeLlama architecture. It has been fine-tuned using LoRA on a curated dataset comprising a fraction of the glaive-function-calling-v2 dataset for function calling examples and a cleaned version of sharegpt-hyperfiltered-3k for general chat. This specialized training enables the model to effectively interpret user requests and generate appropriate function calls, while also maintaining conversational abilities.

Key Capabilities

• Function Calling: The model is specifically optimized to understand user intent and generate structured function calls, including parameters, based on available function definitions. It can correctly identify when a function call is needed and format it appropriately.
• Instruction Following: It demonstrates strong instruction-following capabilities, responding to direct questions and engaging in multi-turn conversations.
• Contextual Understanding: The model can maintain context across turns in a conversation, even when functions are involved, allowing for more natural and effective interactions.
• Hybrid Use Cases: It seamlessly transitions between generating function calls and providing natural language responses, depending on the prompt and available tools.

Good For

• Tool-augmented LLM applications: Ideal for developers building agents or applications that require an LLM to interact with external tools or APIs through function calls.
• Automated task execution: Suitable for scenarios where user requests need to be translated into executable actions via predefined functions.
• Conversational AI with structured outputs: Useful for chatbots or virtual assistants that need to both converse naturally and trigger specific actions or retrieve structured information.