Overview

This model, ermiaazarkhalili/Qwen2.5-0.5B-Instruct_Function_Calling_xLAM, is a 0.5 billion parameter language model derived from Qwen/Qwen2.5-0.5B-Instruct. It has been fine-tuned using Supervised Fine-Tuning (SFT) with LoRA (Low-Rank Adaptation) on the Salesforce/xlam-function-calling-60k dataset. The primary goal of this fine-tuning is to enhance its capabilities in function calling scenarios.

Key Features

• Function Calling Optimization: Specifically trained on a dedicated function calling dataset.
• Efficient Training: Utilizes LoRA with 4-bit quantization for reduced computational overhead.
• Compact Size: At 0.5 billion parameters, it offers a balance between performance and resource efficiency.
• Flexible Deployment: Available in multiple formats, including GGUF quantizations for CPU or mixed CPU/GPU inference.
• Context Length: Fine-tuned with a maximum sequence length of 2,048 tokens.

Intended Use Cases

• Research: Ideal for exploring language model fine-tuning techniques, especially for function calling.
• Education: Suitable for learning and teaching purposes in AI and NLP.
• Prototyping: Useful for developing and testing conversational AI agents that require function calling capabilities.
• Personal Projects: Can be integrated into personal applications where a small, specialized model is beneficial.