Instructions to use maldv/Shisutemu-Masuta-Q3-32B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use maldv/Shisutemu-Masuta-Q3-32B with Transformers:

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

pipe = pipeline("text-generation", model="maldv/Shisutemu-Masuta-Q3-32B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("maldv/Shisutemu-Masuta-Q3-32B")
model = AutoModelForCausalLM.from_pretrained("maldv/Shisutemu-Masuta-Q3-32B")
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

vLLM

How to use maldv/Shisutemu-Masuta-Q3-32B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "maldv/Shisutemu-Masuta-Q3-32B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "maldv/Shisutemu-Masuta-Q3-32B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/maldv/Shisutemu-Masuta-Q3-32B

SGLang

How to use maldv/Shisutemu-Masuta-Q3-32B 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 "maldv/Shisutemu-Masuta-Q3-32B" \
    --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": "maldv/Shisutemu-Masuta-Q3-32B",
		"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 "maldv/Shisutemu-Masuta-Q3-32B" \
        --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": "maldv/Shisutemu-Masuta-Q3-32B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use maldv/Shisutemu-Masuta-Q3-32B with Docker Model Runner:
```
docker model run hf.co/maldv/Shisutemu-Masuta-Q3-32B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

GGUF iMat exl3

Shisutemu Masuta Q3 32B

Shisutemu Masuta is a normalized denoised fourier interpolation of the following models:

output_base_model: "Qwen/Qwen3-32B"
output_dtype: "bfloat16"
finetune_merge:
  - { "model": "Skywork/MindLink-32B-0801", "base": "Qwen/Qwen3-32B", "alpha": 0.8 }
  - { "model": "MetaStoneTec/XBai-o4", "base": "Qwen/Qwen3-32B", "alpha": 0.9, "is_input": true }
  - { "model": "miromind-ai/MiroThinker-32B-SFT-v0.1", "base": "Qwen/Qwen3-32B", "alpha": 0.7 }
  - { "model": "agentica-org/DeepSWE-Preview", "base": "Qwen/Qwen3-32B", "alpha": 0.6 }
  - { "model": "qihoo360/Light-IF-32B", "base": "Qwen/Qwen3-32B", "alpha": 0.6 }
  - { "model": "Jinx-org/Jinx-Qwen3-32B", "base": "Qwen/Qwen3-32B", "alpha": 0.8, "is_output": true }
  - { "model": "Zhihu-ai/Zhi-Create-Qwen3-32B", "base": "Qwen/Qwen3-32B", "alpha": 0.7 }
  - { "model": "DMindAI/DMind-1", "base": "Qwen/Qwen3-32B", "alpha": 0.5 }
  - { "model": "shuttleai/shuttle-3.5", "base": "Qwen/Qwen3-32B", "alpha": 0.8 }

In other words, all of these models get warped and interpolated in signal space, and then jammed back on top of the base model (which in this case was Qwen3-32B); with the XBai-o4 input layer and the Jinx-Qwen3-32B output layer.

Thinking Model

This model uses <think></think> tags to generate a sequence of thoughts before generating the response. It excels at generating code and instruction following on any requested task.

Task Vectors and Alignment

It is clear from the model responses that the task signals from Jinx-Qwen3-32B were successful at controlling alignment, even when diluted by the signals from so many other models.

Citation

If you find our work helpful, feel free to give us a cite.

@misc{shisutemu-masuta-q3-32b,
    title = {Shisutemu Masuta Q3 32},
    url = {https://huggingface.co/maldv/Shisutemu-Masuta-Q3-32B},
    author = {Praxis Maldevide},
    month = {August},
    year = {2025}
}

Downloads last month: 26

Safetensors

Model size

33B params

Tensor type

BF16

Model tree for maldv/Shisutemu-Masuta-Q3-32B

Base model

Qwen/Qwen3-32B

Finetuned

DMindAI/DMind-1

Finetuned

(1)

this model

Quantizations

3 models