candyyoojin/toolcalling-merged-demo
The candyyoojin/toolcalling-merged-demo is a 2 billion parameter Qwen3-based causal language model developed by candyyoojin, fine-tuned for tool-calling capabilities. This model was trained using Unsloth and Huggingface's TRL library, enabling faster fine-tuning. With a context length of 32768 tokens, it is optimized for applications requiring efficient function calling and integration with external tools.
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Model Overview
The candyyoojin/toolcalling-merged-demo is a 2 billion parameter Qwen3-based language model developed by candyyoojin. It has been specifically fine-tuned for tool-calling applications, allowing it to effectively interact with and utilize external functions or APIs.
Key Characteristics
- Base Model: Fine-tuned from
unsloth/Qwen3-1.7B-unsloth-bnb-4bit, indicating a foundation in the Qwen3 architecture. - Training Efficiency: The model was trained significantly faster using Unsloth and Huggingface's TRL library, highlighting an optimized fine-tuning process.
- Parameter Count: Features 2 billion parameters, offering a balance between performance and computational efficiency.
- Context Length: Supports a substantial context window of 32768 tokens, beneficial for complex tool-calling scenarios requiring extensive input or conversation history.
Primary Use Case
This model is primarily designed for scenarios where an LLM needs to intelligently call external tools or functions based on user prompts. Its fine-tuning for tool-calling makes it suitable for developing agents, automating tasks, or integrating with various software systems.