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.