Instructions to use SufficientPrune3897/Mistral-Small-3.2-24B-Character-Creator-V2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SufficientPrune3897/Mistral-Small-3.2-24B-Character-Creator-V2 with Transformers:

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

pipe = pipeline("image-text-to-text", model="SufficientPrune3897/Mistral-Small-3.2-24B-Character-Creator-V2")
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("SufficientPrune3897/Mistral-Small-3.2-24B-Character-Creator-V2")
model = AutoModelForMultimodalLM.from_pretrained("SufficientPrune3897/Mistral-Small-3.2-24B-Character-Creator-V2")
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 SufficientPrune3897/Mistral-Small-3.2-24B-Character-Creator-V2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SufficientPrune3897/Mistral-Small-3.2-24B-Character-Creator-V2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SufficientPrune3897/Mistral-Small-3.2-24B-Character-Creator-V2",
		"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/SufficientPrune3897/Mistral-Small-3.2-24B-Character-Creator-V2

SGLang

How to use SufficientPrune3897/Mistral-Small-3.2-24B-Character-Creator-V2 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 "SufficientPrune3897/Mistral-Small-3.2-24B-Character-Creator-V2" \
    --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": "SufficientPrune3897/Mistral-Small-3.2-24B-Character-Creator-V2",
		"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 "SufficientPrune3897/Mistral-Small-3.2-24B-Character-Creator-V2" \
        --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": "SufficientPrune3897/Mistral-Small-3.2-24B-Character-Creator-V2",
		"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"
						}
					}
				]
			}
		]
	}'

Unsloth Studio

How to use SufficientPrune3897/Mistral-Small-3.2-24B-Character-Creator-V2 with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for SufficientPrune3897/Mistral-Small-3.2-24B-Character-Creator-V2 to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for SufficientPrune3897/Mistral-Small-3.2-24B-Character-Creator-V2 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for SufficientPrune3897/Mistral-Small-3.2-24B-Character-Creator-V2 to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="SufficientPrune3897/Mistral-Small-3.2-24B-Character-Creator-V2",
    max_seq_length=2048,
)

Docker Model Runner
How to use SufficientPrune3897/Mistral-Small-3.2-24B-Character-Creator-V2 with Docker Model Runner:
```
docker model run hf.co/SufficientPrune3897/Mistral-Small-3.2-24B-Character-Creator-V2
```

This is a model made to create characters that can be used in Sillytavern, cai, jai and other such roleplay scenarios. The resulting characters should be about ~2k tokens and follow a prebaked structure.

Versions:

8B llama 3.3 based and GGUFs
12B gemma 3 based and GGUFs
24B mistral small 3.2 based (this one) and GGUFs
(maybe) 27B gemma 3 based and GGUFs

How to use it:

Simply tell the model what you want your character to be.
It should know many popular franchises, the bigger the model, the more it knows.
Fully uncensored.
Asking for a different structure than the one the model uses might significantly reduce result quality.
While follow up questions are supported, you will often get better results adjusting your original prompt.
Supports asking for: prompts for pictures of the char, asking for changes and making an intro.

Changes from V1

No longer supports Groups and scenarios
Characters should be much better
It actually follows a structure and doesnt start making shit up after ~1k tokens

V3 and beyond: The next version will either reintroduce scenarios, groups or feature reasoning. Probably both. Perhaps even lorebooks, although I'm still unsure how to execute on that... After that I will probably make my own real roleplay finetune or something.

If anybody wants support of their native language just ask me and tell me what model does the best for that.

I am very much open for feedback. A single comment can easily change how I will do my next version.