usersina/math-llm-sit-7b
TEXT GENERATIONConcurrency Cost:1Model Size:7.6BQuant:FP8Ctx Length:32kPublished:Apr 2, 2026License:mitArchitecture:Transformer0.0K Open Weights Cold
The usersina/math-llm-sit-7b is a 7.6 billion parameter language model fine-tuned for mathematical reasoning tasks. Based on the Qwen2.5-7B-Instruct architecture, it utilizes a unique 4-phase Specialized Intelligence Theory (SIT) pipeline for enhanced performance. This model is specifically optimized for solving complex mathematical problems and integrals, offering a 32768 token context length.
Loading preview...
Overview
The usersina/math-llm-sit-7b is a 7.6 billion parameter language model specifically fine-tuned for advanced mathematical reasoning. It is built upon the Qwen/Qwen2.5-7B-Instruct base model and distinguishes itself through its unique training methodology.
Key Capabilities
- Specialized Mathematical Reasoning: Optimized for solving complex mathematical problems, including integrals, through a dedicated fine-tuning process.
- Specialized Intelligence Theory (SIT) Pipeline: Trained using a novel 4-phase pipeline designed to enhance reasoning capabilities:
- SFT (Supervised Fine-Tuning): Establishes foundational math reasoning.
- Feedback: Integrates weighted feedback for refinement.
- Posterior: Performs internal posterior calibration.
- Framework: Achieves full framework integration with resource allocation.
- Qwen2.5-7B-Instruct Base: Leverages the robust architecture of the Qwen2.5-7B-Instruct model.
Good For
- Applications requiring precise mathematical problem-solving.
- Research and development in specialized AI reasoning systems.
- Tasks that benefit from a model trained with a structured, multi-phase intelligence theory approach.