ermiaazarkhalili/FastContext-4B-RL_base-Function-Calling-xLAM-Unsloth
The ermiaazarkhalili/FastContext-4B-RL_base-Function-Calling-xLAM-Unsloth is a 4 billion parameter causal language model, fine-tuned by ermiaazarkhalili from Microsoft's FastContext-1.0-4B-RL. Optimized for function calling, it was trained using Unsloth for efficient fine-tuning on the Salesforce/xlam-function-calling-60k dataset. This model excels at interpreting natural language queries and generating structured function calls, making it suitable for tool-use applications.
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Model Overview
This model, developed by ermiaazarkhalili, is a 4 billion parameter variant of Microsoft's FastContext-1.0-4B-RL, specifically fine-tuned for function calling. It leverages Unsloth for efficient training, achieving 2x faster training times and 60% less VRAM consumption compared to standard methods. The model was trained on the comprehensive Salesforce/xlam-function-calling-60k dataset, which includes 60,000 examples of queries, tool definitions, and structured answers.
Key Capabilities
- Function Calling: Highly optimized for translating natural language requests into structured function calls.
- Efficient Fine-tuning: Benefits from Unsloth's optimizations, making it a resource-friendly option for deployment and further adaptation.
- 4-bit Quantization: Supports 4-bit QLoRA for reduced memory footprint during training and inference.
- GGUF Availability: Provides quantized GGUF versions (Q4_K_M, Q5_K_M, Q8_0) for CPU and edge device inference, compatible with tools like Ollama and llama.cpp.
Good for
- Tool Use and Agent Systems: Ideal for applications requiring an LLM to interact with external tools or APIs by generating precise function calls.
- Resource-Constrained Environments: Its efficient training and quantization options make it suitable for deployment on hardware with limited VRAM.
- Rapid Prototyping: The Unsloth integration allows for quick experimentation and fine-tuning on custom function calling datasets.