Zen Designer GGUF: 235B Vision-Language Model (Abliterated)

235B MoE | Vision-Language | GGUF Quantized | Abliterated

Fine-tuned from Qwen3-VL-235B-A22B-Instruct (Apache-2.0) with Hanzo identity + agentic-data training + abliteration, then GGUF-quantized. A 235B-total / 22B-active Mixture-of-Experts vision-language model supporting images, video, documents, charts, GUIs, and spatial reasoning with 256K context.

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Model Specifications

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Available Formats

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Quick Start

llama.cpp

# Download a split (Q2_K example — replace with Q4_K_M filename as appropriate)
# Then run:
llama-cli \
  --model zen-designer-235b-a22b-instruct-abliterated-Q2_K-00001-of-00015.gguf \
  --mmproj mmproj-zen-designer-235b-a22b-instruct-abliterated-f16.gguf \
  --image your_image.jpg \
  --prompt "Describe this image in detail." \
  -n 1024 \
  --ctx-size 8192 \
  --temp 0.7

For multi-part files, place all split parts in the same directory and point --model to part 00001.

Vision Tasks

Zen Designer handles a broad range of visual inputs:

• Image analysis and description
• Document and PDF parsing
• Chart and table extraction
• GUI navigation and screen understanding
• Video understanding with temporal reasoning
• Bounding box and spatial grounding

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Abliteration

This model has been abliterated — a technique that removes refusal behaviors encoded in the model weights without fine-tuning. The process works by identifying the refusal direction in the model's residual stream and projecting it out of the weight matrices.

What abliteration does:

• Removes hardcoded refusal responses
• Preserves all other capabilities and knowledge
• Does not alter factual knowledge or reasoning ability

What abliteration does not do:

• Add harmful knowledge the base model lacked
• Guarantee any specific behavior
• Replace a system prompt or application-level safety policy

Users are responsible for appropriate deployment and use of abliterated models. Apply system prompts and application-layer controls to define model behavior for your use case.

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Attribution

Built on Qwen3-VL-235B-A22B-Instruct by the Qwen team, Alibaba Group, released under the Apache License 2.0. Hanzo's contribution is identity training, agentic-data fine-tuning, and abliteration on top of that base, distributed here in GGUF format. The base model is used under the terms of the Apache License, Version 2.0.

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Model Family

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Links

Zen LM | Hanzo AI | GitHub | All Models

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Part of the Zen model family (zenlm.org) by Hanzo AI (Techstars '17) and Zoo Labs Foundation (zoo.ngo).