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.