ermiaazarkhalili/Qwen2.5-1.5B-Instruct_Function_Calling_xLAM
ermiaazarkhalili/Qwen2.5-1.5B-Instruct_Function_Calling_xLAM is a 1.5 billion parameter language model developed by ermiaazarkhalili, fine-tuned from Qwen/Qwen2.5-1.5B-Instruct. This model is specifically optimized for function calling tasks, leveraging Supervised Fine-Tuning (SFT) with LoRA adapters on the Salesforce/xlam-function-calling-60k dataset. It is designed for efficient inference and research into fine-tuning methods for instruction-following models.
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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.