ermiaazarkhalili/FastContext-4B-SFT_base-Function-Calling-xLAM-Unsloth
The ermiaazarkhalili/FastContext-4B-SFT_base-Function-Calling-xLAM-Unsloth model is a 4 billion parameter language model, fine-tuned from Microsoft's FastContext-1.0-4B-SFT. Developed by ermiaazarkhalili, it is specifically optimized for function calling tasks, leveraging the Salesforce/xlam-function-calling-60k dataset. This model utilizes Unsloth for efficient training, resulting in 2x faster fine-tuning and 60% less VRAM consumption, making it suitable for resource-constrained environments. Its primary strength lies in accurately interpreting user queries to generate structured function calls.
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Model Overview
This model, developed by ermiaazarkhalili, is a 4 billion parameter language model fine-tuned from Microsoft's FastContext-1.0-4B-SFT. It is specifically designed and optimized for function calling tasks, enabling it to interpret natural language requests and translate them into structured tool invocations.
Key Capabilities & Features
- Function Calling Specialization: Fine-tuned on the Salesforce/xlam-function-calling-60k dataset, which comprises 60,000 examples of queries, tool definitions, and structured answers.
- Efficient Training: Utilizes Unsloth for fine-tuning, achieving 2x faster training and 60% less VRAM usage compared to standard methods. This makes it highly efficient for deployment and further customization.
- Base Model: Built upon the FastContext-1.0-4B-SFT architecture, providing a solid foundation for its function-calling capabilities.
- Quantization Support: Available in various GGUF quantized versions (Q4_K_M, Q5_K_M, Q8_0) for CPU and edge inference, and supports 4-bit QLoRA for efficient GPU inference.
- Context Length: Fine-tuned with a 2,048 token context window, suitable for processing moderately complex function calling scenarios.
Ideal Use Cases
- Automated Tool Use: Excellent for applications requiring an LLM to interact with external APIs or tools based on user commands.
- Chatbots & Assistants: Enhances chatbots by allowing them to perform actions (e.g., check weather, set reminders) through function calls.
- Resource-Constrained Environments: Its efficient training and inference capabilities, especially with Unsloth and GGUF quantizations, make it suitable for deployment on consumer hardware or edge devices.
Limitations
- Primarily trained on English data.
- Knowledge is limited to the base model's training data cutoff.
- May exhibit hallucinations, generating plausible but incorrect information.
- Not extensively safety-tuned, requiring external guardrails for sensitive applications.