amd/ReasonLite-0.6B-Turbo
Hugging Face
TEXT GENERATIONConcurrency Cost:1Model Size:0.8BQuant:BF16Ctx Length:32kPublished:Nov 26, 2025License:openrailArchitecture:Transformer0.0K Open Weights Warm

The amd/ReasonLite-0.6B-Turbo is an ultra-lightweight 0.6 billion parameter math reasoning model developed by AMD. It leverages high-quality data distillation to achieve strong performance in mathematical reasoning tasks, specifically on AIME24, comparable to models significantly larger in size. This model is optimized for efficiency and high performance in complex mathematical problem-solving.

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Model Overview

The amd/ReasonLite-0.6B-Turbo is an ultra-lightweight 0.6 billion parameter model from AMD, specifically designed for mathematical reasoning. It stands out by achieving performance levels comparable to models over 10 times its size, such as Qwen3-8B, particularly on the AIME24 benchmark where it scores 57.1.

Key Capabilities & Features

  • Exceptional Math Reasoning: Achieves strong performance on AIME24, demonstrating advanced mathematical problem-solving capabilities for its size.
  • Ultra-Lightweight: With only 0.6 billion parameters, it offers significant efficiency advantages, making it suitable for resource-constrained environments.
  • Two-Stage Distillation: Trained using a progressive two-stage distillation process, first with short-CoT data to achieve ReasonLite-0.6B-Turbo, then with long-CoT data for the higher-performing ReasonLite-0.6B variant.
  • Open-Source: Fully open-source, including weights, training scripts, datasets, and the synthesis pipeline, promoting transparency and community contributions.
  • High-Quality Data: Distilled using 6.1 million high-quality samples derived from 343K math problems from Polaris and OpenMathReasoning, with pseudo-labels generated via majority voting from GPT-OSS.

When to Use This Model

  • Resource-Constrained Math Applications: Ideal for scenarios requiring strong mathematical reasoning capabilities on devices or platforms with limited computational resources.
  • Efficient Reasoning Tasks: Suitable for integrating advanced math reasoning into applications where speed and efficiency are critical.
  • Research and Development: Provides a fully open-source foundation for further research into efficient and high-performing small language models for specialized tasks.