ermiaazarkhalili/Llama-3.1-8B-Instruct_Function_Calling_xLAM
The ermiaazarkhalili/Llama-3.1-8B-Instruct_Function_Calling_xLAM is an 8 billion parameter language model developed by ermiaazarkhalili, fine-tuned from Meta's Llama-3.1-8B-Instruct. This model is specifically optimized for function calling tasks, having been trained using Supervised Fine-Tuning (SFT) with LoRA adapters on the Salesforce/xlam-function-calling-60k dataset. It features a 2,048 token context length and is primarily intended for research, educational purposes, and prototyping conversational AI with function calling capabilities.
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Overview
This model, Llama-3.1-8B-Function-Calling-xLAM, is an 8 billion parameter language model developed by ermiaazarkhalili. It is a fine-tuned version of Meta's Llama-3.1-8B-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 Features
- Function Calling Optimization: Fine-tuned on a dedicated dataset for function calling tasks.
- Efficient Training: Utilizes LoRA with 4-bit NF4 quantization for efficient adaptation.
- Base Model: Built upon the robust Llama-3.1-8B-Instruct architecture.
- Context Length: Fine-tuned with a maximum sequence length of 2,048 tokens.
- Accessibility: Available in multiple formats, including GGUF quantizations for CPU/mixed inference.
Intended Uses
This model is recommended for:
- Research into language model fine-tuning.
- Educational applications and personal projects.
- Prototyping conversational AI systems that require function calling.
Limitations
Users should be aware of the following limitations:
- Primarily trained on English data.
- Knowledge cutoff is limited to the base model's training data.
- May generate incorrect information (hallucinations).
- Not extensively safety-tuned; requires additional guardrails for production use.