Instructions to use RedHatAI/Muse-Glimmer-30B-FP8-block with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RedHatAI/Muse-Glimmer-30B-FP8-block with Transformers:

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

pipe = pipeline("image-text-to-text", model="RedHatAI/Muse-Glimmer-30B-FP8-block")
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("RedHatAI/Muse-Glimmer-30B-FP8-block")
model = AutoModelForMultimodalLM.from_pretrained("RedHatAI/Muse-Glimmer-30B-FP8-block", 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=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use RedHatAI/Muse-Glimmer-30B-FP8-block with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RedHatAI/Muse-Glimmer-30B-FP8-block"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RedHatAI/Muse-Glimmer-30B-FP8-block",
		"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/RedHatAI/Muse-Glimmer-30B-FP8-block

SGLang

How to use RedHatAI/Muse-Glimmer-30B-FP8-block 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 "RedHatAI/Muse-Glimmer-30B-FP8-block" \
    --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": "RedHatAI/Muse-Glimmer-30B-FP8-block",
		"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 "RedHatAI/Muse-Glimmer-30B-FP8-block" \
        --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": "RedHatAI/Muse-Glimmer-30B-FP8-block",
		"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 RedHatAI/Muse-Glimmer-30B-FP8-block with Docker Model Runner:
```
docker model run hf.co/RedHatAI/Muse-Glimmer-30B-FP8-block
```

RedHatAI/Muse-Glimmer-30B-FP8-block

This model is a quantized version of meta-models/Muse-Glimmer-30B.

Model Optimizations

This model was obtained by quantizing the weights and activations of meta-models/Muse-Glimmer-30B to FP8 data type, ready for inference with vLLM. This optimization reduces the number of bits per parameter from 16 to 8, reducing the disk size and GPU memory requirements by approximately 50%.

Weights are quantized using block-wise FP8 scaling (128×128 blocks), and activations are quantized dynamically per group (group_size=128). Only the weights and activations of the linear operators within transformer blocks are quantized using LLM Compressor. Vision tower, embedding, and output head layers are kept in their original precision.

Creation

from llmcompressor import model_free_ptq

MODEL_ID = "meta-models/Muse-Glimmer-30B"
SAVE_DIR = MODEL_ID.rstrip("/").split("/")[-1] + "-FP8-block"

model_free_ptq(
    model_stub=MODEL_ID,
    save_directory=SAVE_DIR,
    scheme="FP8_BLOCK",
    ignore=["re:.*vision.*", "lm_head", "re:.*embed_tokens.*"],
    max_workers=15,
    device="cuda:0",
)

Deployment

docker run --gpus all \
    --privileged --ipc=host -p 8000:8000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    vllm/vllm-openai:muse-glimmer RedHatAI/Muse-Glimmer-30B-FP8-BLOCK \
    --generation-config auto \
    --tensor-parallel-size 1 \
    --enable-auto-tool-choice \
    --tool-call-parser muse_glimmer \
    --reasoning-parser muse_glimmer

For detailed instructions including multi-GPU deployment, multimodal inference, etc see the Muse-Glimmer 30B vLLM usage guide.

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Safetensors

Model size

30B params

Tensor type

BF16

F8_E4M3

Model tree for RedHatAI/Muse-Glimmer-30B-FP8-block

Base model

meta-models/Muse-Glimmer-30B

Quantized

(157)

this model