WeiboAI/VibeThinker resource

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1561 stars · 116 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

VibeThinker is a family of small dense reasoning language models (1.5B and 3B parameters) from WeiboAI, trained with a Spectrum-to-Signal post-training pipeline to achieve frontier-level math and coding reasoning. The repository hosts documentation, papers, and links to model weights on Hugging Face and ModelScope.

Use cases

• run a small reasoning model for math olympiad problems
• download a 1.5B LLM that rivals much larger reasoning models
• study how diversity-driven post-training elicits reasoning in tiny models
• benchmark small models on AIME and LiveCodeBench
• deploy a low-cost reasoning LLM locally
• research small-model reinforcement learning pipelines

When to choose

• you need strong math/competitive-programming reasoning from a tiny model that fits on modest hardware
• you want to study or reproduce the Spectrum-to-Signal post-training approach
• you need open MIT-licensed reasoning model weights

When to avoid

• you need general-purpose chat, long-context, or multimodal capabilities
• you require frontier-scale knowledge breadth rather than verifiable reasoning tasks
• you want a ready-made inference server or application rather than model weights

Facets

learning-resource · maturity active

machine-learning llm-training llm-inference large-language-models machine-learning artificial-intelligence python reasoning-models small-language-models post-training reinforcement-learning math-reasoning model-weights huggingface gpu

Member repositories

For agents

markdown · JSON · MCP: product_card(name="WeiboAI/VibeThinker")