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

Attribute	Value
Base Model	Qwen3-VL-235B-A22B-Instruct (Apache-2.0)
Parameters	235B total / 22B active (MoE)
Architecture	Vision-language transformer (Mixture of Experts)
Context Window	256K tokens
Modalities	Text, Images, Video, Documents
OCR Languages	32 scripts
License	Apache 2.0

Available Formats

Format	Size	Description	Recommended Use
Q2_K (split)	~60 GB	2-bit quantization, 15-part split	Servers with 64+ GB RAM, maximum scale
Q4_K_M	~142 GB	4-bit quantization, single or split	Best quality/size tradeoff for local inference

Quick Start

llama.cpp

bash

# 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

Model	Format	Parameters	Context
zen-designer-235b-a22b-instruct	SafeTensors	235B / 22B active	256K
zen-designer-gguf	GGUF	235B / 22B active	256K

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).

zenlm/zen-designer-gguf

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