cjiao/goldengoose-p3_goose_lowdiv_n128_grpoc_tau0.10-25grp

TEXT GENERATIONConcurrent Unit Cost:1Model Size:1.5BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Jul 9, 2026Architecture:Transformer Featherless Exclusive Cold

The cjiao/goldengoose-p3_goose_lowdiv_n128_grpoc_tau0.10-25grp is a 1.5 billion parameter language model, fine-tuned by cjiao from Qwen/Qwen2.5-1.5B-Instruct. This model utilizes the GRPO (Grouped Reinforcement Learning with Policy Optimization) method, as introduced in the DeepSeekMath paper, to enhance its capabilities. With a context length of 32768 tokens, it is specifically optimized for tasks requiring advanced reasoning, particularly in mathematical domains. Its fine-tuning with GRPO suggests improved performance in complex problem-solving compared to its base model.

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Model Overview

The cjiao/goldengoose-p3_goose_lowdiv_n128_grpoc_tau0.10-25grp is a 1.5 billion parameter language model, fine-tuned by cjiao. It is built upon the robust Qwen/Qwen2.5-1.5B-Instruct architecture, leveraging its foundational capabilities.

Key Differentiator: GRPO Fine-tuning

What sets this model apart is its fine-tuning process, which incorporates GRPO (Grouped Reinforcement Learning with Policy Optimization). This method was originally introduced in the research paper "DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models". The application of GRPO suggests an optimization for:

  • Enhanced Reasoning: Improved ability to handle complex logical and mathematical problems.
  • Problem-Solving: Better performance in tasks requiring structured thought and multi-step solutions.

Technical Specifications

  • Base Model: Qwen/Qwen2.5-1.5B-Instruct
  • Parameter Count: 1.5 billion
  • Context Length: 32768 tokens
  • Training Framework: TRL (Transformer Reinforcement Learning)

Use Cases

Given its GRPO-based fine-tuning, this model is particularly well-suited for applications demanding:

  • Mathematical Reasoning: Solving equations, proofs, and quantitative problems.
  • Logical Deduction: Tasks requiring step-by-step logical inference.
  • Complex Question Answering: Providing detailed and reasoned answers to intricate queries.

This model offers a specialized approach to improving reasoning capabilities within a compact 1.5B parameter footprint, making it a strong candidate for applications where advanced problem-solving is critical.