DOBIBI/toolcalling-merged-demo
The DOBIBI/toolcalling-merged-demo is a 2 billion parameter Qwen3-based causal language model developed by DOBIBI, fine-tuned for tool-calling capabilities. This model was efficiently trained using Unsloth and Huggingface's TRL library, enabling faster development. It is designed for applications requiring structured function calls and interaction with external tools.
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Model Overview
The DOBIBI/toolcalling-merged-demo is a 2 billion parameter language model based on the Qwen3 architecture, developed by DOBIBI. This model has been specifically fine-tuned to enhance its tool-calling capabilities, making it suitable for applications that require structured interaction with external functions or APIs.
Key Characteristics
- Base Model: Qwen3-1.7B, indicating a robust foundation for language understanding.
- Parameter Count: 2 billion parameters, offering a balance between performance and computational efficiency.
- Context Length: Supports a substantial context window of 32768 tokens, allowing for processing longer inputs and maintaining conversational coherence.
- Efficient Training: The model was fine-tuned using Unsloth and Huggingface's TRL library, which facilitated a 2x faster training process.
Use Cases
This model is particularly well-suited for scenarios where an LLM needs to:
- Perform Tool Calling: Generate structured function calls based on user prompts to interact with external systems or databases.
- Automate Workflows: Integrate with various tools and services to automate complex tasks.
- Develop Agentic Systems: Serve as a core component in AI agents that can plan and execute actions through tool use.