Overview

This model, ermiaazarkhalili/Qwen2.5-14B-Instruct_Function_Calling_xLAM, is a 14.8 billion parameter language model derived from the Qwen2.5-14B-Instruct base model. It has been specifically fine-tuned for function calling capabilities using Supervised Fine-Tuning (SFT) with LoRA adapters on the Salesforce/xlam-function-calling-60k dataset.

Key Capabilities

• Function Calling Optimization: Specialized training for understanding and generating function calls.
• Efficient Fine-Tuning: Utilizes LoRA (Low-Rank Adaptation) with 4-bit quantization for efficient training.
• Base Model: Built upon the robust Qwen2.5-14B-Instruct architecture.
• Inference Flexibility: Available in multiple formats, including GGUF quantizations for CPU/mixed CPU/GPU inference.

Good For

• Research: Exploring language model fine-tuning techniques, especially for function calling.
• Prototyping: Developing conversational AI agents that require tool use or function invocation.
• Educational Purposes: Learning about SFT, LoRA, and function-calling model development.
• Personal Projects: Implementing AI assistants with specific action capabilities.