Instructions to use DataSnake/Muse-12B-NVFP4-FP8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DataSnake/Muse-12B-NVFP4-FP8 with Transformers:

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

pipe = pipeline("text-generation", model="DataSnake/Muse-12B-NVFP4-FP8")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("DataSnake/Muse-12B-NVFP4-FP8")
model = AutoModelForCausalLM.from_pretrained("DataSnake/Muse-12B-NVFP4-FP8", 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=256)
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-FP8 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-FP8"
# 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-FP8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/DataSnake/Muse-12B-NVFP4-FP8

SGLang

How to use DataSnake/Muse-12B-NVFP4-FP8 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-FP8" \
    --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-FP8",
		"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-FP8" \
        --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-FP8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use DataSnake/Muse-12B-NVFP4-FP8 with Docker Model Runner:
```
docker model run hf.co/DataSnake/Muse-12B-NVFP4-FP8
```

DataSnake commited on Apr 27

Commit

22ec27a

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1 Parent(s): 793f8c0

Update README.md

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@@ -19,9 +19,35 @@ tags:
 Quantized weights of the [Muse-12B](https://huggingface.co/LatitudeGames/Muse-12B) model for use with nVidia Blackwell GPUs, in a hybrid format using NVFP4 with [Four Over Six](https://arxiv.org/abs/2512.02010) adaptive block scaling for the MLP layers and `FP8_DYNAMIC` for the self-attention layers. More information about the hybrid format [here](https://huggingface.co/DataSnake/Mistral-Nemo-Instruct-2407-NVFP4-FP8), but the short version is that FP8 attention has minimal impact on speed and VRAM usage while making a marked difference in output quality, especially at longer context lengths.
 ## Inference
-Tested on a RTX 5060 Ti 16GB with [Aphrodite Engine](https://github.com/aphrodite-engine/aphrodite-engine) and [vLLM](https://github.com/vllm-project/vllm). It requires compressed-tensors 0.14.0 or later, so you'll have to update the version in your venv if you use Aphrodite Engine or an older version of vLLM. On my system, Aphrodite Engine was able to run the checkpoint with a 32k context window with the `--single-user-mode` flag, while vLLM didn't have quite enough VRAM to do the same. It works fine at shorter context lengths or with the KV cache quantized, however.
-Recommended generation settings (a mix of what it says on the Muse-12B model card and the [AI Dungeon Model Guide](https://help.aidungeon.com/ai-models-and-their-differences)):
 - Temperature: 1.0
 - Top K: 250
 - Top P: 1

 Quantized weights of the [Muse-12B](https://huggingface.co/LatitudeGames/Muse-12B) model for use with nVidia Blackwell GPUs, in a hybrid format using NVFP4 with [Four Over Six](https://arxiv.org/abs/2512.02010) adaptive block scaling for the MLP layers and `FP8_DYNAMIC` for the self-attention layers. More information about the hybrid format [here](https://huggingface.co/DataSnake/Mistral-Nemo-Instruct-2407-NVFP4-FP8), but the short version is that FP8 attention has minimal impact on speed and VRAM usage while making a marked difference in output quality, especially at longer context lengths.
 ## Inference
+Tested on a RTX 5060 Ti 16GB with [Aphrodite Engine](https://github.com/aphrodite-engine/aphrodite-engine) and [vLLM](https://github.com/vllm-project/vllm). It requires compressed-tensors 0.14.0 or later, so you'll have to update the version in your venv if you use Aphrodite Engine 0.10.0 or an older version of vLLM. On my system, Aphrodite Engine 0.10.0 was able to run the checkpoint with a 32k context window with the `--single-user-mode` flag, while vLLM 0.20.0 and Aphrodite Engine 0.20.0, which don't have that flag, were able to do the same with `--max-num-seqs 1 --cudagraph-capture-sizes 2` flags, though with the caveat that each crashed with OOM errors the first time they ran the model but ran fine from the second time onwards.
+<details>
+<summary>Details about the 0.20.0 memory issue</summary>
+From what I can determine, compiling the CUDA graph for the model uses enough VRAM that there's not enough left to allocate the full KV cache. In both cases, the first run mentions saving something to a cache and the second doesn't. And in both cases, the first run reports that has 4.16 GiB of VRAM available for the KV cache before crashing due to lack of memory and the second run has 5.2 and doesn't crash. For reference, a 32768-token KV cache for this model will use precisely 5.00 GiB.
+</details>
+<details>
+<summary>Extra steps when using Aphrodite Engine 0.10.0</summary>
+For the purposes of these instructions, I'm assuming you have Aphrodite Engine 0.10.0 installed in a Python 3.12 uv venv, as per the official [instructions](https://aphrodite.pygmalion.chat/installation/installation/).
+First, update compressed-tensors to a more recent version:
+```
+uv pip install "compressed-tensors>=0.14.0"
+```
+Next, open `<venv directory>/lib/python3.12/site-packages/aphrodite/platforms/interface.py` in your text editor of choice and comment out or delete lines 487-491. To make sure you're in the right place, the lines should initially look like this:
+```
+            logger.warning(
+                "Current platform %s does not have '%s' attribute.",
+                self.device_type,
+                key,
+            )
+```
+</details>
+### Recommended Generation Settings
+This is a mix of what it says on the Muse-12B model card and the [AI Dungeon Model Guide](https://help.aidungeon.com/ai-models-and-their-differences):
 - Temperature: 1.0
 - Top K: 250
 - Top P: 1