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Anemll·Nov 24, 2025·v0.3.5·open source

ANEMLL VibeThinker 1.5B — Variable Context State Transition Model

The ANEMLL VibeThinker 1.5B is a machine learning model optimized for the Apple Neural Engine (ANE), enabling efficient on-device inference with dynamic context size support. It demonstrates variable context inference by starting with a small key-value (KV) cache and automatically expanding it as the output length increases, allowing for the generation of outputs exceeding 24,000 tokens on devices with a 4,096-token context model.

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overview

The ANEMLL VibeThinker 1.5B is a machine learning model optimized for the Apple Neural Engine (ANE), enabling efficient on-device inference with dynamic context size support. It demonstrates variable context inference by starting with a small key-value (KV) cache and automatically expanding it as the output length increases, allowing for the generation of outputs exceeding 24,000 tokens on devices with a 4,096-token context model.

key features
  • 01Optimized for Apple Neural Engine (ANE)
  • 02Supports dynamic context sizes up to 4,096 tokens
  • 03Enables on-device inference with outputs over 24,000 tokens
  • 04Utilizes a shift-refill mechanism for cache management
  • 05Converted from WeiboAI/VibeThinker-1.5B using ANEMLL v0.3.5
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