Instructions to use DataSnake/Muse-12B-NVFP4-4over6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DataSnake/Muse-12B-NVFP4-4over6 with Transformers:

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

pipe = pipeline("text-generation", model="DataSnake/Muse-12B-NVFP4-4over6")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("DataSnake/Muse-12B-NVFP4-4over6")
model = AutoModelForCausalLM.from_pretrained("DataSnake/Muse-12B-NVFP4-4over6", device_map="auto")
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 DataSnake/Muse-12B-NVFP4-4over6 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DataSnake/Muse-12B-NVFP4-4over6"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DataSnake/Muse-12B-NVFP4-4over6",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/DataSnake/Muse-12B-NVFP4-4over6

SGLang

How to use DataSnake/Muse-12B-NVFP4-4over6 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 "DataSnake/Muse-12B-NVFP4-4over6" \
    --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": "DataSnake/Muse-12B-NVFP4-4over6",
		"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 "DataSnake/Muse-12B-NVFP4-4over6" \
        --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": "DataSnake/Muse-12B-NVFP4-4over6",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use DataSnake/Muse-12B-NVFP4-4over6 with Docker Model Runner:
```
docker model run hf.co/DataSnake/Muse-12B-NVFP4-4over6
```

Add library_name and GitHub repository link

by nielsr HF Staff - opened May 8

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

-4

Files changed (1) hide show

README.md +7 -4

README.md CHANGED Viewed

@@ -1,12 +1,13 @@
 ---
-license: apache-2.0
 datasets:
 - zerofata/Roleplay-Anime-Characters
 language:
 - en
-base_model:
-- LatitudeGames/Muse-12B
 pipeline_tag: text-generation
 tags:
 - nvfp4
 - text adventure
@@ -17,7 +18,9 @@ tags:
 # Muse-12B-NVFP4-4over6
-Quantized NVFP4 weights of the [Muse-12B](https://huggingface.co/LatitudeGames/Muse-12B) model, for use with nVidia Blackwell GPUs.
 ## Quantization details
 Quantized with llm-compressor 0.9.0.2 using [Four Over Six](https://arxiv.org/abs/2512.02010) adaptive block scaling with MSE selection for the weights, done by using the `memoryless_mse` observer with `maxshrink` and `grid` set to negative numbers. Calibrated with the [Roleplay-Anime-Characters](https://huggingface.co/datasets/zerofata/Roleplay-Anime-Characters) dataset, formatted in ChatML format.

 ---
+base_model:
+- LatitudeGames/Muse-12B
 datasets:
 - zerofata/Roleplay-Anime-Characters
 language:
 - en
+license: apache-2.0
 pipeline_tag: text-generation
+library_name: transformers
 tags:
 - nvfp4
 - text adventure
 # Muse-12B-NVFP4-4over6
+Quantized NVFP4 weights of the [Muse-12B](https://huggingface.co/LatitudeGames/Muse-12B) model, for use with nVidia Blackwell GPUs. This model uses the quantization technique presented in the paper [Four Over Six: More Accurate NVFP4 Quantization with Adaptive Block Scaling](https://huggingface.co/papers/2512.02010).
+Official Code: [mit-han-lab/fouroversix](https://github.com/mit-han-lab/fouroversix)
 ## Quantization details
 Quantized with llm-compressor 0.9.0.2 using [Four Over Six](https://arxiv.org/abs/2512.02010) adaptive block scaling with MSE selection for the weights, done by using the `memoryless_mse` observer with `maxshrink` and `grid` set to negative numbers. Calibrated with the [Roleplay-Anime-Characters](https://huggingface.co/datasets/zerofata/Roleplay-Anime-Characters) dataset, formatted in ChatML format.