Model Overview

OnnuriLove/toolcalling-merged-demo is a 2 billion parameter model based on the Qwen3 architecture, developed by OnnuriLove. It was fine-tuned from the unsloth/Qwen3-1.7B-unsloth-bnb-4bit base model, leveraging Unsloth and Huggingface's TRL library for accelerated training, achieving a 2x speed improvement.

Key Characteristics

• Architecture: Qwen3-based, a causal language model.
• Parameter Count: 2 billion parameters.
• Context Length: Supports a substantial context window of 32768 tokens.
• Training Efficiency: Utilizes Unsloth for faster fine-tuning.

Potential Use Cases

This model is suitable for applications requiring a compact yet capable language model with a large context window. Its fine-tuned nature suggests optimization for specific tasks, making it a strong candidate for:

• Tool Calling: Given its name, it is likely specialized for understanding and generating tool-use instructions or API calls.
• Efficient Deployment: Its 2B parameter size makes it more efficient for deployment compared to larger models, especially when combined with Unsloth's optimization benefits.
• Specific Domain Tasks: Ideal for scenarios where a fine-tuned model can outperform general-purpose LLMs on targeted functions.