Instructions to use zerofata/MS3.2-PaintedFantasy-Visage-33B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use zerofata/MS3.2-PaintedFantasy-Visage-33B with Transformers:

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

pipe = pipeline("text-generation", model="zerofata/MS3.2-PaintedFantasy-Visage-33B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("zerofata/MS3.2-PaintedFantasy-Visage-33B")
model = AutoModelForMultimodalLM.from_pretrained("zerofata/MS3.2-PaintedFantasy-Visage-33B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.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(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use zerofata/MS3.2-PaintedFantasy-Visage-33B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "zerofata/MS3.2-PaintedFantasy-Visage-33B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "zerofata/MS3.2-PaintedFantasy-Visage-33B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/zerofata/MS3.2-PaintedFantasy-Visage-33B

SGLang

How to use zerofata/MS3.2-PaintedFantasy-Visage-33B 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 "zerofata/MS3.2-PaintedFantasy-Visage-33B" \
    --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": "zerofata/MS3.2-PaintedFantasy-Visage-33B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "zerofata/MS3.2-PaintedFantasy-Visage-33B" \
        --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": "zerofata/MS3.2-PaintedFantasy-Visage-33B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use zerofata/MS3.2-PaintedFantasy-Visage-33B with Docker Model Runner:
```
docker model run hf.co/zerofata/MS3.2-PaintedFantasy-Visage-33B
```

I find it interesting with XTC sampler

by Laetilia - opened Jul 7, 2025

Discussion

Laetilia

Jul 7, 2025

•

edited Jul 8, 2025

In my humble opinion/experience, this model works better with addition of XTC sampler (as surprising as always).
I also think that this model has unusual, even if flawed, style. I find it refreshing, interesting to (role)play with.
More specifically, I currently use this model with the following settings (via llama-cli; part of llama.cpp)...

-c 32768 --top-p 1.0 --dry-multiplier 0.8 --dry-allowed-length 4 --min-p 0.03 --xtc-probability 0.1 --temp 0.6 --chat-template mistral-v7-tekken

Also, I've tried two different quants of this model - i1-IQ4_XS (by mradermacher) and Q6_K_L (by bartowski).
The style of first quant I think is more vivid (which is the strength of this model, in my humble opinion).
But the larger quant actually knows what is happening significantly better (which I personally liked more).
Of course, there are quants in-between; I am yet to try them out, they may or may not feel nicer, IDK. =p

As to the model's weaknesses, I think these are: repetition, repetition (it arises after some time/messages), tendency to give dialogue replies with my own (even non-dialogue) words, overall confusion about characters and concepts (less with XTC and at higher quant), repetition (XTC helps a bit).

Overall: thank you for an unusual model! ^_^

All this is my personal opinion; opinions (and model usage) of different people can differ.

Why does XTC helps to stabilize some models (few; in my opinion)??? It isn't meant to. It is literally "specifically for creativity" type of sampler. And yet, sometimes a pinch of chaos is all you need, it seems. LLMs are like dark magic, I tell ya...

zerofata

Owner Jul 9, 2025

Thank you! This is a interesting review.

This model was a big experiment that was more likely to lobotomize the model as make it good, so glad it has turned out well (excluding the repetition).

The token probabilities on this are really weird, so that lines up with XTC I think. When I was checking to try and figure out repetition, it was quite often there was multiple high probability options that all looked stable that XTC would've been quite effective at picking (I think when XTC activates, it also stops the model from picking the super low probability options, thus why it might actually become more stable from it.)

I may try this experiment on another model that doesn't natively have the repetition issues in the future and see if I can re-create the oddly coherent chaotic experience without the downsides, or when I get the motivation to grow the dataset out.

Laetilia

Jul 9, 2025

Thank you for the explanation of XTC effects!

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