Model Overview

This model, developed by ermiaazarkhalili, is a 1.5 billion parameter language model derived from Qwen/Qwen2.5-1.5B-Instruct. It has been specifically fine-tuned using Supervised Fine-Tuning (SFT) with LoRA adapters on the Salesforce/xlam-function-calling-60k dataset, making it specialized for function calling tasks.

Key Capabilities

• Function Calling Optimization: Fine-tuned on a dedicated function-calling dataset to improve instruction following for tool use.
• Efficient Training: Utilizes LoRA (Low-Rank Adaptation) with 4-bit quantization for efficient fine-tuning.
• Base Model: Built upon the Qwen2.5-1.5B-Instruct architecture, providing a strong foundation for instruction adherence.
• Inference Flexibility: Available in various formats, including GGUF quantizations for CPU or mixed CPU/GPU inference.

Intended Use Cases

• Research: Ideal for exploring language model fine-tuning techniques and function calling capabilities.
• Educational Purposes: Suitable for learning and experimentation with LLMs and SFT.
• Prototyping: Useful for developing and testing conversational AI agents that require function calling.
• Personal Projects: Applicable for various personal applications where a compact, function-calling capable model is beneficial.

Limitations

• Primarily trained on English data.
• Fine-tuned with a 2,048 token context length.
• May exhibit hallucinations and is not extensively safety-tuned.