Hugging Face Overview
Model Overview
Lacaille-MoT-4B-Supreme2 is a 4 billion parameter model built upon the Qwen3-4B architecture, developed by prithivMLmods. It distinguishes itself through fine-tuning with a specialized Mixture of Thoughts (MoT) dataset, which incorporates expert clusters for code, mathematics, and science, alongside an extended open code reasoning dataset. This unique training approach enables the model to blend symbolic precision, scientific logic, and structured output fluency.
Key Capabilities
- Unified Reasoning: Excels in multi-domain symbolic reasoning across programming, mathematics, and scientific logic.
- Advanced Code Reasoning & Generation: Supports multi-language coding, offering explanations, optimization hints, and error detection for tasks like full-stack prototyping and debugging.
- Scientific Problem Solving: Performs analytical reasoning in physics, biology, and chemistry, including concept explanation, equation solving, and symbolic derivations.
- Hybrid Symbolic-AI Thinking: Combines structured logic, chain-of-thought reasoning, and open-ended inference for robust performance on STEM tasks.
- Structured Output Mastery: Generates outputs seamlessly in formats such as LaTeX, Markdown, JSON, CSV, and YAML, ideal for technical documentation and data generation.
- Optimized Efficiency: Its 4B parameter footprint allows for versatile deployment on mid-range GPUs, offline clusters, and edge AI systems.
Intended Use Cases
- Scientific tutoring, computational logic, and mathematical education.
- Advanced coding assistance for algorithm design, code reviews, and documentation.
- Generating structured technical data across various formats and fields.
- Developing STEM-focused chatbots or APIs for research and educational tools.
- Deployment in environments requiring high symbolic fidelity with mid-resource constraints.