ZonglinY/MOOSE-Star-R1D-7B

TEXT GENERATIONConcurrency Cost:1Model Size:7.6BQuant:FP8Ctx Length:32kPublished:Apr 4, 2026License:apache-2.0Architecture:Transformer0.0K Open Weights Cold

MOOSE-Star-R1D-7B by ZonglinY is a 7.6 billion parameter multi-task language model fine-tuned for scientific discovery workflows, specifically excelling at both inspiration retrieval and hypothesis composition. Built upon DeepSeek-R1-Distill-Qwen-7B, it maintains high accuracy in selecting relevant cross-paper inspirations while significantly outperforming single-task models in generating structured delta hypotheses. This model is optimized for research-oriented tasks, providing robust performance even under varying levels of inspiration noise.

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MOOSE-Star-R1D-7B: A Multi-Task Model for Scientific Discovery

MOOSE-Star-R1D-7B (MS-7B) is a 7.6 billion parameter language model developed by ZonglinY, specifically fine-tuned for critical tasks in scientific discovery: inspiration retrieval (IR) and hypothesis composition (HC). Based on the DeepSeek-R1-Distill-Qwen-7B architecture, this model uniquely combines these capabilities into a single, unified system.

Key Capabilities

  • Inspiration Retrieval (IR): Selects the most relevant cross-paper inspiration from 15 candidates, achieving 54.34% accuracy, matching the performance of dedicated single-task IR models.
  • Hypothesis Composition (HC): Generates structured delta hypotheses from new inspiration papers, outperforming all single-task HC variants, including those with bounded composition augmentation.
  • Robustness: Demonstrates improved performance in hypothesis composition under varying levels of inspiration noise, indicating effective transfer of IR reasoning skills.
  • Unified Workflow: Streamlines scientific discovery by handling both inspiration identification and hypothesis generation within one model.

Good for

  • Scientific Research: Automating and assisting in the early stages of scientific inquiry.
  • Literature Review: Identifying relevant research papers and synthesizing new ideas from existing knowledge.
  • Hypothesis Generation: Structuring and formulating new hypotheses based on provided research questions, background, and inspirational papers.
  • Academic Applications: Tools requiring advanced reasoning over scientific texts and structured output generation.

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