ermiaazarkhalili/Llama-3-8B-Instruct_Function_Calling_xLAM

Overview

Llama-3-8B-Function-Calling-xLAM is an 8 billion parameter language model developed by ermiaazarkhalili, fine-tuned from Meta's Llama-3-8B-Instruct. Its primary focus is on function calling, achieved through Supervised Fine-Tuning (SFT) using LoRA (Low-Rank Adaptation) with 4-bit quantization. The model was trained on the Salesforce/xlam-function-calling-60k dataset, specifically designed for function calling tasks.

Key Capabilities

• Function Calling Optimization: Specifically fine-tuned to understand and generate function calls based on user prompts.
• Efficient Training: Utilizes LoRA with 4-bit NF4 quantization for efficient adaptation of the base model.
• Inference Flexibility: Available in multiple formats, including GGUF quantizations, for deployment on various hardware, including CPU.
• Base Model Strength: Benefits from the robust architecture and pre-training of the Meta-Llama-3-8B-Instruct model.

Good For

• Research: Ideal for studying language model fine-tuning techniques, particularly for function calling.
• Prototyping Conversational AI: Suitable for developing and testing conversational agents that require tool use or function invocation.
• Educational Purposes: Can be used for learning about SFT, LoRA, and function calling implementations in LLMs.
• Personal Projects: Applicable for various personal AI projects where function calling is a core requirement.

Limitations

• Context Length: Fine-tuned with a 2,048 token context limit.
• Language: Primarily trained on English data.
• Safety: Not extensively safety-tuned; requires additional guardrails for production use.
• Knowledge Cutoff: Inherits the knowledge cutoff of its base model.