TourniquetRules/flip7-reasoning-sft-Qwen3-4B
TourniquetRules/flip7-reasoning-sft-Qwen3-4B is a 4 billion parameter language model fine-tuned from the Qwen3-4B architecture. Developed by TourniquetRules, this model is specifically optimized for reasoning tasks through supervised fine-tuning on the flip7-reasoning-sft dataset. It leverages a 32768 token context length to process complex inputs, making it suitable for applications requiring advanced logical inference and problem-solving capabilities.
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Model Overview
TourniquetRules/flip7-reasoning-sft-Qwen3-4B is a 4 billion parameter language model built upon the Qwen3-4B architecture. This model has undergone supervised fine-tuning (SFT) using the specialized TourniquetRules/flip7-reasoning-sft dataset, which is designed to enhance its reasoning abilities.
Key Capabilities
- Enhanced Reasoning: Specifically fine-tuned on a reasoning-focused dataset to improve logical inference and problem-solving.
- Qwen3 Architecture: Benefits from the robust base capabilities of the Qwen3 model family.
- Extended Context Window: Supports a context length of 32768 tokens, allowing for the processing of longer and more complex prompts.
Training Details
The model was trained using the TRL (Transformers Reinforcement Learning) library, a framework for fine-tuning large language models. The training procedure utilized SFT, focusing on aligning the model's outputs with desired reasoning patterns present in the dataset.
Good For
- Applications requiring strong logical reasoning.
- Tasks involving complex problem-solving and inference.
- Scenarios where understanding and generating reasoned responses are critical.