Dvijsj12/grpo-hello

TEXT GENERATIONPricing:Input $0.04 / Cached $0.008 / Output $0.08Concurrent Unit Cost:1Model Size:0.5BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Sep 17, 2026Architecture:Transformer Featherless Exclusive Cold

Dvijsj12/grpo-hello is a 0.5 billion parameter instruction-tuned causal language model, fine-tuned from Qwen/Qwen2.5-0.5B-Instruct. It was trained using the GRPO method on the trl-lib/DeepMath-103K dataset, specializing it for mathematical reasoning tasks. With a 32768 token context length, this model is optimized for processing and generating responses related to complex mathematical problems.

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Model Overview

Dvijsj12/grpo-hello is a 0.5 billion parameter language model, fine-tuned from the Qwen2.5-0.5B-Instruct base model. Its primary distinction lies in its training methodology: it was fine-tuned using the GRPO (Gradient Regularized Policy Optimization) method on the trl-lib/DeepMath-103K dataset. This specialized training aims to enhance its capabilities in mathematical reasoning.

Key Characteristics

  • Base Model: Qwen/Qwen2.5-0.5B-Instruct
  • Parameter Count: 0.5 billion
  • Context Length: 32768 tokens
  • Training Method: GRPO, as introduced in the paper "DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models" (arXiv:2402.03300).
  • Dataset: Fine-tuned on the DeepMath-103K dataset, which focuses on mathematical problems.

Use Cases

This model is particularly well-suited for applications requiring:

  • Mathematical Reasoning: Solving or assisting with mathematical problems and queries.
  • Educational Tools: Generating explanations or solutions for math-related questions.
  • Research: Exploring the application of GRPO in smaller language models for specific domains.

Given its specialized training on mathematical data, it is recommended for tasks where robust mathematical understanding and generation are crucial, especially within its 32K token context window.

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