Model Overview
Hatshepsut-Qwen3_QWQ-LCoT-4B is a 4-billion parameter model built upon the Qwen3-4B architecture, specifically fine-tuned by prithivMLmods. Its core differentiation lies in its training on QWQ Synthetic datasets and explicit support for Least-to-Complexity-of-Thought (LCoT) prompting, which encourages granular, step-by-step reasoning from simple to complex problem components.
Key Capabilities
- LCoT Prompting Mastery: Tuned to excel with LCoT prompting for detailed, multi-step problem-solving.
- Precision Reasoning: Achieves high-fidelity outputs in symbolic logic, algebraic manipulation, and mathematical word problems due to QWQ-based training.
- Code Understanding & Logic Generation: Interprets and generates concise, logically sound code snippets in Python, C++, and JavaScript, focusing on algorithmic steps.
- Structured Output Control: Capable of producing responses in structured formats like JSON, Markdown, LaTeX, and tables, ideal for technical documentation and educational content.
- Multilingual Reasoning: Supports STEM-based problem solving and translation across more than 20 languages.
- Efficient Footprint: A lightweight 4B parameter model suitable for deployment on mid-tier GPUs (e.g., A10, 3090, L4).
Intended Use Cases
This model is particularly well-suited for:
- LCoT-style multi-step problem solving in mathematics and logic.
- Algebra, geometry, and general logic question answering.
- Code generation with an emphasis on algorithmic transparency.
- Developing educational tools for math and programming.
- Generating structured technical output in formats like Markdown or LaTeX.
- Multilingual STEM tutoring and reasoning applications.