Thrillcrazyer/Qwen-2.5-1.5B_TAC_Teacher_Qwen14B

Hugging Face
TEXT GENERATIONConcurrency Cost:1Model Size:1.5BQuant:BF16Ctx Length:32kPublished:Mar 27, 2026Architecture:Transformer Warm

Thrillcrazyer/Qwen-2.5-1.5B_TAC_Teacher_Qwen14B is a 1.5 billion parameter causal language model, fine-tuned from Qwen/Qwen2.5-1.5B-Instruct. It was specifically trained on the DeepMath-103k dataset using the GRPO method, which is designed to enhance mathematical reasoning capabilities. This model is optimized for tasks requiring advanced mathematical problem-solving and logical deduction, leveraging a 32768 token context length.

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Model Overview

Thrillcrazyer/Qwen-2.5-1.5B_TAC_Teacher_Qwen14B is a 1.5 billion parameter language model derived from Qwen/Qwen2.5-1.5B-Instruct. Its primary distinction lies in its specialized fine-tuning on the DeepMath-103k dataset, a collection curated for mathematical reasoning tasks.

Training Methodology

The model was trained using the TRL library and incorporates the GRPO (Gradient-based Reward Policy Optimization) method. GRPO is a technique introduced in the research paper "DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models" (arXiv:2402.03300), specifically designed to improve a model's mathematical reasoning abilities. This targeted training approach aims to enhance the model's performance on complex mathematical problems and logical deductions.

Key Capabilities

  • Enhanced Mathematical Reasoning: Specialized training on DeepMath-103k with GRPO focuses on improving the model's ability to understand and solve mathematical problems.
  • Instruction Following: Inherits instruction-following capabilities from its base model, Qwen2.5-1.5B-Instruct.
  • Context Handling: Supports a substantial context length of 32768 tokens, allowing for processing longer and more complex problem descriptions.

Use Cases

This model is particularly well-suited for applications requiring:

  • Solving mathematical equations and word problems.
  • Assisting in educational tools for math and logic.
  • Generating explanations for mathematical concepts.
  • Tasks that benefit from strong logical deduction and numerical understanding.

Finetunes
2 models