Instructions to use AxionML/Muse-Glimmer-30B-NVFP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AxionML/Muse-Glimmer-30B-NVFP4 with Transformers:

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

pipe = pipeline("image-text-to-text", model="AxionML/Muse-Glimmer-30B-NVFP4")
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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("AxionML/Muse-Glimmer-30B-NVFP4")
model = AutoModelForMultimodalLM.from_pretrained("AxionML/Muse-Glimmer-30B-NVFP4", device_map="auto")
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=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use AxionML/Muse-Glimmer-30B-NVFP4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AxionML/Muse-Glimmer-30B-NVFP4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AxionML/Muse-Glimmer-30B-NVFP4",
		"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/AxionML/Muse-Glimmer-30B-NVFP4

SGLang

How to use AxionML/Muse-Glimmer-30B-NVFP4 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 "AxionML/Muse-Glimmer-30B-NVFP4" \
    --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": "AxionML/Muse-Glimmer-30B-NVFP4",
		"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 "AxionML/Muse-Glimmer-30B-NVFP4" \
        --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": "AxionML/Muse-Glimmer-30B-NVFP4",
		"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"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use AxionML/Muse-Glimmer-30B-NVFP4 with Docker Model Runner:
```
docker model run hf.co/AxionML/Muse-Glimmer-30B-NVFP4
```

AxionML Muse-Glimmer-30B-NVFP4

Mirrored by AxionML for open-source serving and deployment use cases. Part of AxionML's effort to provide ready-to-serve quantized models for the community.

Quantized by NVIDIA. The weights in this repository are an unmodified copy of nvidia/Muse-Glimmer-30B-NVFP4 (revision 47818374517751c48c55cde2621594926b1888b6). All credit for the quantization belongs to NVIDIA.

This is an NVFP4-quantized version of meta-models/Muse-Glimmer-30B (29.6B parameters including the perception encoder), quantized with NVIDIA Model Optimizer.

About NVFP4 quantization: NVFP4 on Blackwell couples a compact E2M1 FP4 codebook with blockwise FP8 (E4M3) scaling over 16-element micro-blocks, so that 4-bit stored values remain numerically useful for neural-network computation. The E2M1 codebook provides a small, nonuniform set of representable magnitudes up to ±6 and relies on saturating behavior rather than IEEE NaN/Inf encodings to maximize usable range per bit. Using an FP8 block scale (rather than power-of-two-only E8M0) enables fractional scales and error-minimizing scale selection. On Blackwell Tensor Cores, native FP4 multipliers exploit E2M1 simplicity while higher-precision FP32 accumulation protects dot-product accuracy.

Ready for commercial and non-commercial use under Apache 2.0.

Model Summary


Architecture	Dense causal transformer with perception encoder (`MuseGlimmerForConditionalGeneration`)
Parameters	29.6B (including vision encoder)
Input	Interleaved text and images (up to 4,096 visual tokens per image)
Context Length	131K tokens
Reasoning	Controllable strength: low / medium / high / xhigh
Checkpoint Size	~24.7 GB (vs ~60 GB BF16)

Evaluation Results

Benchmark	BF16	NVFP4 (mixed)
Terminal-Bench 2.1 pass@1	45.22 ± 0.92	47.05 ± 1.42
GDPVal normalized Elo	0.20282	0.23798
GPQA Diamond pass@1	83.81 ± 0.33	83.02 ± 0.40
MMMU-Pro	74.22	73.58
SciCode subtask accuracy	47.63	49.70
IFBench strict pass@1	76.58 ± 0.53	78.74 ± 0.52
AA-LCR mean reward	76.25	75.56

Scores reported by NVIDIA for this checkpoint: high reasoning, temperature=1.0, top_p=0.95, top_k=64. Gains over BF16 are within sampling noise.

Quantization Details

Quantization format: mixed precision via ModelOpt AutoQuantize — each MLP, self-attention projection and lm_head layer assigned W4A16 NVFP4, FP8, or BF16 under a 5.5 effective-bit budget (6.6 effective bits overall)
Vision encoder: BF16, not quantized
KV cache: unquantized (auto at serving time)
Calibration dataset: 512 × 2,048-token sequences from Nemotron-Post-Training-Dataset-v3
Tool: NVIDIA Model Optimizer v0.46.0 (recipe)

Usage

Deploy with SGLang

sglang serve \
    --model-path AxionML/Muse-Glimmer-30B-NVFP4 \
    --language-model-only \
    --reasoning-parser muse \
    --tool-call-parser muse \
    --kv-cache-dtype fp8_e4m3 \
    --mem-fraction-static 0.85 \
    --speculative-algorithm DFLASH \
    --speculative-draft-model-path meta-models/Muse-Glimmer-30B-assistant \
    --speculative-draft-model-quantization fp8

Deploy with vLLM

vllm serve AxionML/Muse-Glimmer-30B-NVFP4 \
    --trust-remote-code \
    --enable-prefix-caching \
    --enable-chunked-prefill \
    --enable-auto-tool-choice \
    --tool-call-parser muse_glimmer \
    --reasoning-parser muse_glimmer \
    --kv-cache-dtype auto \
    --max-model-len 131072 \
    --mamba-cache-mode align

Validated upstream on one B200 (SGLang, text-only with DFlash) and with vLLM 0.28.0. For matched quality use temperature=1.0, top_p=0.95, top_k=64, thinking enabled. See USAGE_POLICY.md.

Limitations

The base model was trained on data that may contain toxic language and societal biases. The quantized model inherits these limitations. It may generate inaccurate, biased, or offensive content. Please refer to the original model card and the upstream quantized model card for full details.

Credits

Base model: meta-models/Muse-Glimmer-30B
Quantization: nvidia/Muse-Glimmer-30B-NVFP4 by NVIDIA
Mirror: AxionML

Downloads last month: 335

Safetensors

Model size

20B params

Tensor type

BF16

F8_E4M3

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