ermiaazarkhalili/Qwen3-8B-Function-Calling-xLAM-Unsloth
The ermiaazarkhalili/Qwen3-8B-Function-Calling-xLAM-Unsloth model is a fine-tuned 8 billion parameter Qwen3 model, developed by ermiaazarkhalili, specifically optimized for function calling tasks. It leverages Unsloth for efficient training, resulting in 2x faster fine-tuning and 60% less VRAM usage. Trained on the Salesforce/xlam-function-calling-60k dataset, this model excels at interpreting natural language queries and generating structured function calls within a 2,048 token context window.
Loading preview...
Overview
This model, developed by ermiaazarkhalili, is a fine-tuned version of the 8 billion parameter Qwen3 model, specifically optimized for function calling. It utilizes the Unsloth framework for efficient training, enabling 2x faster training and 60% less VRAM consumption compared to standard methods.
Key Capabilities
- Function Calling: Specialized in converting natural language requests into structured function calls.
- Efficient Training: Fine-tuned using Unsloth with QLoRA (4-bit) on an NVIDIA H100 GPU, achieving a final training loss of 0.2186 in under 4 hours.
- Dataset: Trained on the Salesforce/xlam-function-calling-60k dataset, comprising 60,000 examples with XML-tagged queries, tool definitions, and structured answers.
- Context Length: Supports a context window of 2,048 tokens.
- Quantized Versions: Available in GGUF formats (Q4_K_M, Q5_K_M, Q8_0) for CPU and edge inference, compatible with Ollama and llama.cpp.
Good For
- Applications requiring robust and efficient function calling capabilities.
- Developers looking for a Qwen3-based model optimized for tool use and API interaction.
- Scenarios where efficient resource utilization during fine-tuning is critical.