sagarchapara/qwen3-4b-thinking-aimo-numina-cot-sft
The sagarchapara/qwen3-4b-thinking-aimo-numina-cot-sft model is a 4 billion parameter instruction-tuned causal language model, fine-tuned from Qwen/Qwen3-4B-Thinking-2507. Developed by sagarchapara, this model is optimized for reasoning tasks, leveraging its base model's capabilities. It is suitable for applications requiring structured thought processes and coherent responses within its 40960 token context window.
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Model Overview
The sagarchapara/qwen3-4b-thinking-aimo-numina-cot-sft model is a 4 billion parameter language model, fine-tuned from the Qwen/Qwen3-4B-Thinking-2507 base model. This instruction-tuned variant is designed to enhance its ability to follow instructions and generate thoughtful responses.
Key Capabilities
- Instruction Following: The model has been fine-tuned using Supervised Fine-Tuning (SFT) with the TRL framework, improving its capacity to understand and execute user prompts.
- Reasoning Focus: Building upon the "Thinking" variant of the Qwen3-4B series, this model is likely optimized for tasks that require a structured thought process or chain-of-thought reasoning.
- Context Handling: It supports a substantial context length of 40960 tokens, allowing for processing and generating longer, more complex interactions.
Training Details
The model was trained using the TRL library, a framework for Transformer Reinforcement Learning, specifically employing an SFT approach. This method typically involves training on a dataset of instruction-response pairs to align the model's output with human preferences and instructions.
When to Use This Model
This model is particularly well-suited for applications where:
- Instruction-based tasks are central, such as question answering, summarization, or content generation based on specific guidelines.
- Reasoning and logical coherence in responses are important.
- Longer contexts are required for understanding complex queries or generating detailed outputs.