dongwanaa/toolcalling-merged-demo
The dongwanaa/toolcalling-merged-demo is a 2 billion parameter Qwen3-based causal language model developed by dongwanaa, fine-tuned from unsloth/Qwen3-1.7B-unsloth-bnb-4bit. This model was trained using Unsloth and Huggingface's TRL library, enabling faster training. It is designed for general language tasks with a 32768 token context length, leveraging its Qwen3 architecture for robust performance.
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Model Overview
The dongwanaa/toolcalling-merged-demo is a 2 billion parameter language model developed by dongwanaa. It is based on the Qwen3 architecture and was fine-tuned from the unsloth/Qwen3-1.7B-unsloth-bnb-4bit model.
Key Characteristics
- Architecture: Qwen3-based, providing a strong foundation for various language understanding and generation tasks.
- 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.
- Training Efficiency: The model was trained using Unsloth and Huggingface's TRL library, which facilitated a 2x faster fine-tuning process.
Intended Use Cases
This model is suitable for general-purpose language tasks where a Qwen3-based architecture with a decent context window is beneficial. Its efficient training process suggests it could be a good candidate for applications requiring rapid iteration or deployment on resource-constrained environments, while still leveraging the capabilities of the Qwen3 family.