Model Overview
The dustntn10/toolcalling-merged-demo is a 2 billion parameter Qwen3-based language model, developed by dustntn10. It has been specifically fine-tuned for tool-calling functionalities, enabling it to interact with external functions and APIs. The model leverages a substantial 32768 token context window, allowing for processing of longer and more complex prompts.
Training Details
This model was fine-tuned from unsloth/Qwen3-1.7B-unsloth-bnb-4bit, indicating an efficient training process. The fine-tuning was accelerated using the Unsloth library, which is known for speeding up training by up to 2x, in conjunction with Huggingface's TRL (Transformer Reinforcement Learning) library. This combination suggests an optimization for performance and resource efficiency during the training phase.
Key Capabilities
- Tool Calling: Designed to understand and execute function calls, facilitating integration with external systems and services.
- Efficient Training: Benefits from Unsloth's optimizations for faster fine-tuning.
- Large Context Window: Supports a 32768 token context length, suitable for detailed instructions and multi-turn interactions.
Ideal Use Cases
- Automated Workflows: Integrating LLM capabilities into systems that require interaction with external tools or APIs.
- Agentic Applications: Developing AI agents that can perform actions by calling specific functions.
- Complex Instruction Following: Handling detailed prompts that involve multiple steps or external data retrieval through tool use.