Think Offline: Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-GGUF

By vramkickedin | | 2 min read
A large geometric brain structure made of interconnected white glowing nodes and thin light lines with soft blue light.

Hesamation recently released compressed model files designed to run advanced reasoning tasks on personal computers. These files translate a Qwen3.6 checkpoint trained to follow the step-by-step problem-solving style of Claude Opus 4.6.

The project focuses only on text processing and removes unused image recognition parts to keep local setup simple. Operators can now run complex logical workflows without sending data to outside servers.

Model Size: ~22-36GB & VRAM GPU: requirements vary

Key features and capabilities

  • Four compression options that adjust to match available computer memory.
  • Built to follow structured problem-solving through supervised training methods.
  • Uses lightweight training layers that adjust specific model sections for faster setup.
  • Released under an open license that allows free commercial and personal use.

People running AI on personal hardware can pick a specific file size that balances memory usage with answer accuracy. This approach lets individuals move from basic text editing to detailed logical planning based on their graphics card capacity. Small teams and independent contractors benefit from keeping private information completely offline while using advanced thinking patterns.

Developer notes and benchmark context

The creator points out that published performance numbers come from the complete uncompressed version rather than the smaller files. Compressed copies naturally show different results, so users should view the listed scores as general guides instead of fixed promises. The training process used over fourteen thousand reasoning examples pulled from public datasets and structured thinking patterns to keep responses steady.

The maintainer writes,

"Because the fine-tune is text-only, image/video behavior should be treated as inherited from the base model rather than improved by this training run"

in the project documentation. Anyone wanting to test performance can run their own checks and share their setup details with the wider community.

You can access the complete model repository through Hugging Face.