Qwen3-VL-32B-Instruct-heretic

Abliterated (uncensored) version of Qwen/Qwen3-VL-32B-Instruct, created using Heretic and converted to GGUF.

Abliteration Quality

Metric	Value
Refusals	6/100
KL Divergence	0.0660
Rounds	3

Lower refusals = fewer refused prompts. Lower KL divergence = closer to original model behavior.

Available Quantizations

Quantization	File	Size
Q8_0	Qwen3-VL-32B-Instruct-heretic-Q8_0.gguf	32.43 GB
Q6_K	Qwen3-VL-32B-Instruct-heretic-Q6_K.gguf	25.04 GB
Q4_K_M	Qwen3-VL-32B-Instruct-heretic-Q4_K_M.gguf	18.40 GB

Usage with llama.cpp (Recommended)

Note: Ollama (as of v0.16.x) has a known bug that crashes when loading Qwen3-VL models. Use llama.cpp directly for vision features.

Vision models require a separate multimodal projector (mmproj) file. Download the official mmproj from Qwen/Qwen3-VL-32B-Instruct-GGUF:

# Download mmproj
huggingface-cli download Qwen/Qwen3-VL-32B-Instruct-GGUF mmproj-Qwen3VL-32B-Instruct-F16.gguf

# Run with llama-server (OpenAI-compatible API)
llama-server \
  -m Qwen3-VL-32B-Instruct-heretic-Q6_K.gguf \
  --mmproj mmproj-Qwen3VL-32B-Instruct-F16.gguf \
  -ngl 999

# Or use the CLI directly
llama-mtmd-cli \
  -m Qwen3-VL-32B-Instruct-heretic-Q6_K.gguf \
  --mmproj mmproj-Qwen3VL-32B-Instruct-F16.gguf \
  --image photo.jpg \
  -p "Describe this image." \
  -ngl 999

Usage with Ollama (Text Only)

Ollama can load this model for text-only chat, but vision/image features will crash due to the bug linked above.

ollama run hf.co/ThalisAI/Qwen3-VL-32B-Instruct-heretic:Q8_0
ollama run hf.co/ThalisAI/Qwen3-VL-32B-Instruct-heretic:Q6_K
ollama run hf.co/ThalisAI/Qwen3-VL-32B-Instruct-heretic:Q4_K_M

About

This model was processed by the Apostate automated abliteration pipeline:

1. The source model was loaded in bf16
2. Heretic's optimization-based abliteration was applied to remove refusal behavior
3. The merged model was converted to GGUF format using llama.cpp
4. Multiple quantization levels were generated

The abliteration process uses directional ablation to remove the model's refusal directions while minimizing KL divergence from the original model's behavior on harmless prompts.