ermiaazarkhalili/Llama-3.2-1B-Instruct_Function_Calling_xLAM
ermiaazarkhalili/Llama-3.2-1B-Instruct_Function_Calling_xLAM is a 1 billion parameter language model developed by ermiaazarkhalili, fine-tuned from meta-llama/Llama-3.2-1B-Instruct. Optimized using Supervised Fine-Tuning (SFT) with LoRA adapters on the Salesforce/xlam-function-calling-60k dataset, this model specializes in function calling tasks. It is designed for efficient inference and research into fine-tuning language models for specific instruction-following capabilities.
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Model Overview
This model, Llama-3.2-1B-Function-Calling-xLAM, is a 1 billion parameter language model developed by ermiaazarkhalili. It is a fine-tuned version of meta-llama/Llama-3.2-1B-Instruct, specifically optimized for function calling. The model was trained using Supervised Fine-Tuning (SFT) with LoRA (Low-Rank Adaptation) adapters and 4-bit quantization on the Salesforce/xlam-function-calling-60k dataset.
Key Capabilities
- Function Calling: Specialized in understanding and generating function calls based on instructions.
- Efficient Fine-Tuning: Utilizes LoRA with 4-bit quantization for effective training.
- Optimized for Inference: Available in multiple formats, including GGUF quantizations for CPU/mixed CPU-GPU inference.
- Instruction Following: Benefits from SFT to follow instructions effectively.
Good For
- Research: Ideal for exploring language model fine-tuning techniques.
- Prototyping: Suitable for developing conversational AI prototypes.
- Educational Purposes: A good resource for learning about SFT and LoRA.
- Personal Projects: Can be integrated into personal applications requiring function calling capabilities.
Limitations
- Primarily trained on English data.
- Context length limited to 2,048 tokens during fine-tuning.
- May exhibit hallucinations and is not extensively safety-tuned, requiring external guardrails for production use.