ertghiu256/Qwen3-4b-thinking-gpt5.1-distill
The ertghiu256/Qwen3-4b-thinking-gpt5.1-distill is a 4 billion parameter Qwen3-based language model developed by ertghiu256, fine-tuned from unsloth/qwen3-4b-thinking-2507-unsloth-bnb-4bit. It features a 40960 token context length and is optimized for reasoning tasks, leveraging a dataset focused on high-reasoning capabilities. This model was trained using Unsloth and Huggingface's TRL library for accelerated fine-tuning.
Loading preview...
Model Overview
This model, developed by ertghiu256, is a 4 billion parameter Qwen3-based language model fine-tuned for enhanced reasoning capabilities. It was built upon the unsloth/qwen3-4b-thinking-2507-unsloth-bnb-4bit base model and utilizes a substantial 40960 token context length, making it suitable for processing longer inputs and complex reasoning chains.
Key Capabilities
- Enhanced Reasoning: Fine-tuned on the
TeichAI/gpt-5.1-high-reasoning-1000xdataset, indicating a focus on advanced logical and analytical tasks. - Efficient Training: Leverages Unsloth and Huggingface's TRL library, resulting in a 2x faster fine-tuning process.
- Qwen3 Architecture: Benefits from the underlying Qwen3 architecture, known for its general language understanding and generation abilities.
Use Cases
This model is particularly well-suited for applications requiring:
- Complex problem-solving and logical deduction.
- Tasks benefiting from a large context window.
- Scenarios where efficient model deployment and fine-tuning are critical.