vibethinker

We have hosted the application vibethinker in order to run this application in our online workstations with Wine or directly.

Quick description about vibethinker:

VibeThinker is a compact but high-capability open-source language model released by WeiboAI (Sina AI Lab). It contains about 1.5 billion parameters, far smaller than many �frontier� models, yet it is explicitly optimized for reasoning, mathematics, and code generation tasks rather than general open-domain chat. The innovation lies in its training methodology: the team uses what they call the Spectrum-to-Signal Principle (SSP), where a first stage emphasizes diversity of reasoning paths (the �spectrum� phase) and a second stage uses reinforcement techniques (the �signal� phase) to refine toward correctness and strong reasoning. The result is a model that outpaces many much larger models on domain-specific benchmarks, demonstrating that smaller models, if trained carefully and with the right objectives, can achieve high performance in reasoning-centric tasks.

Features:

• 1.5 billion-parameter dense model optimized for reasoning and code tasks
• Dual-phase training strategy: supervised fine-tuning for diversity + reinforcement for signal refinement
• Strong performance on structured reasoning benchmarks (mathematics, code generation) with efficient compute budget
• Open-source license (MIT) and publicly released weights for research and commercial use
• Low memory/inference footprint suitable for resource-constrained environments or local deployment
• Designed and released by WeiboAI as part of their research into efficient, high-performance small-scale models

Programming Language: Python.
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