prithivMLmods/ReasonFlux-Qwen3-dpo

Hugging Face
TEXT GENERATIONConcurrency Cost:1Model Size:2BQuant:BF16Ctx Length:32kLicense:apache-2.0Architecture:Transformer0.0K Open Weights Warm

ReasonFlux-Qwen3-dpo by prithivMLmods is a 2 billion parameter Qwen3-based model, fine-tuned with DPO on the ReasonFlux-V2-Reasoner-DPO dataset. It utilizes a template-augmented reasoning paradigm and iterative hierarchical reinforcement learning to enhance transparent, consistent, and adaptive reasoning. This model excels in multi-domain scientific, mathematical, and coding tasks, providing structured outputs and detailed explanations.

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Model Overview

prithivMLmods' ReasonFlux-Qwen3-dpo is a 2 billion parameter model built upon the Qwen3-1.7B architecture. It is uniquely fine-tuned using direct preference optimization (DPO) and iterative hierarchical reinforcement learning on the Gen-Verse/ReasonFlux-V2-Reasoner-DPO dataset. This process internalizes structured thought templates, enabling a transparent and consistent reasoning paradigm.

Key Capabilities

  • Template-Augmented Reasoning: Guides step-by-step thinking to improve coherence and reduce hallucinations.
  • Scientific & Mathematical Expertise: Excels in symbolic derivations, proofs, and multi-domain STEM reasoning (physics, chemistry, biology, mathematics).
  • Code Understanding & Generation: Provides detailed coding explanations, debugging support, and optimization hints across multiple programming languages.
  • Structured Output Mastery: Fluent in producing outputs across LaTeX, Markdown, JSON, CSV, and YAML for seamless integration.
  • Efficient Deployment: Designed for mid-range GPUs, research clusters, and edge AI environments due to its lightweight yet powerful nature.

Intended Use Cases

  • Advanced reasoning tutor for mathematics, coding, and scientific research.
  • Research assistant for structured problem-solving with template-guided reasoning.
  • Technical documentation and structured data generation.
  • STEM-focused chatbot or API for research and education workflows.

Limitations

  • Not optimized for casual or creative writing.
  • Specializes in structured reasoning; general conversational performance may be limited.
  • Optimized for clarity of reasoning over natural conversational tone.

Finetunes
2 models