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.

Model Specifications

Available Formats

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

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.

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.

Model Family

Links

Zen LM | Hanzo AI | GitHub | All Models

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