Instructions to use KaraKaraWarehouse/Heretic-Qwen3-VL-235B-A22B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use KaraKaraWarehouse/Heretic-Qwen3-VL-235B-A22B-Instruct with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="KaraKaraWarehouse/Heretic-Qwen3-VL-235B-A22B-Instruct")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("KaraKaraWarehouse/Heretic-Qwen3-VL-235B-A22B-Instruct")
model = AutoModelForMultimodalLM.from_pretrained("KaraKaraWarehouse/Heretic-Qwen3-VL-235B-A22B-Instruct", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use KaraKaraWarehouse/Heretic-Qwen3-VL-235B-A22B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "KaraKaraWarehouse/Heretic-Qwen3-VL-235B-A22B-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "KaraKaraWarehouse/Heretic-Qwen3-VL-235B-A22B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/KaraKaraWarehouse/Heretic-Qwen3-VL-235B-A22B-Instruct

SGLang

How to use KaraKaraWarehouse/Heretic-Qwen3-VL-235B-A22B-Instruct with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "KaraKaraWarehouse/Heretic-Qwen3-VL-235B-A22B-Instruct" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "KaraKaraWarehouse/Heretic-Qwen3-VL-235B-A22B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "KaraKaraWarehouse/Heretic-Qwen3-VL-235B-A22B-Instruct" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "KaraKaraWarehouse/Heretic-Qwen3-VL-235B-A22B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use KaraKaraWarehouse/Heretic-Qwen3-VL-235B-A22B-Instruct with Docker Model Runner:
```
docker model run hf.co/KaraKaraWarehouse/Heretic-Qwen3-VL-235B-A22B-Instruct
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

This is a decensored version of Qwen/Qwen3-VL-235B-A22B-Instruct, made using Heretic v1.1.0

Abliteration parameters

Parameter	Value
direction_index	per layer
attn.o_proj.max_weight	?
attn.o_proj.max_weight_position	?
attn.o_proj.min_weight	?
attn.o_proj.min_weight_distance	?
mlp.down_proj.max_weight	?
mlp.down_proj.max_weight_position	?
mlp.down_proj.min_weight	?
mlp.down_proj.min_weight_distance	?

Values has been removed since those weren't recorded

What's this?

This is a heretic abliterated model. I quickly (or so i thought) made this on a whim because K2 was taking too long

Turns out this was a better decision but still costed me some brain cells to get it working.

Refusals

Ehh, it's now at 30/100 instead of 99/100 from the base model.

Note that this will not make it more agreeable. This still requires additional finetuning to correctly answer questions.

Discussion (Heretic Discord)

Shinon — 7:45 pm
it gave an indirect refusal. (something ~~aling~~ along the lines it cannot condone it and explains why it is bad)
but didn't trigger said refusal words of "im sorry" etc etc

VINAY — 7:46 pm
if it didn't denied but gave anything similar to a summary of it or related topics etc, that's natural since that model is not trained to answer it, we just removed refusals but the model can't answer it like we need a finetune for such questions

Shinon — 7:48 pm
I see.
I'll put up the model for testing on HF then. I want the community to play this model even though it wasn't a 100% success.
I might setup a vllm endpoint once I... figure out how to get vllm happy

Running on VLLM

non trivial (rocm stuff anyway):

VLLM_ENABLE_V1_MULTIPROCESSING=0
export AITER_ONLINE_TUNE=1
export VLLM_ROCM_USE_AITER=1
vllm serve Qwen3-VL-235B-A22B-Instruct --tensor-parallel-size 4 --gpu-memory-utilization 0.9 --disable-log-requests --trust-remote-code --disable-log-requests --max-model-len 32768 --enforce-eager

Downloads last month: 20

Safetensors

Model size

236B params

Tensor type

BF16

Model tree for KaraKaraWarehouse/Heretic-Qwen3-VL-235B-A22B-Instruct

Base model

Qwen/Qwen3-VL-235B-A22B-Instruct

Finetuned

(11)

this model

Quantizations

2 models