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

Libraries

How to use meta-models/Muse-Glimmer-30B with Transformers:

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

pipe = pipeline("image-text-to-text", model="meta-models/Muse-Glimmer-30B")
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("meta-models/Muse-Glimmer-30B")
model = AutoModelForMultimodalLM.from_pretrained("meta-models/Muse-Glimmer-30B", 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]:]))

Inference
HuggingChat
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use meta-models/Muse-Glimmer-30B with vLLM:

Install from pip and serve model

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

SGLang

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

dieuwkehupkes commited on 19 days ago

Commit

1ae8da5

verified ·

1 Parent(s): 7af5801

Create .eval_results folder and add mmi results

Browse files

An .eval_results folder allows dataset owners to commit results for datasets to appear on huggingface datasets. This is the first one of such PRs adding results for the Modality Maturity Index (or mmi).

Files changed (1) hide show

.eval_results/mmi.yaml +56 -0

.eval_results/mmi.yaml ADDED Viewed

	@@ -0,0 +1,56 @@

+- dataset:
+    id: facebook/mmi
+    task_id: mmi-value
+  value: 11.58
+  date: "2026-10-20"
+  source:
+    url: https://huggingface.co/datasets/facebook/mmi
+    name: MMI evaluation
+- dataset:
+    id: facebook/mmi
+    task_id: modality-presence-score
+  value: 15.54
+  date: "2026-10-20"
+  source:
+    url: https://huggingface.co/datasets/facebook/mmi
+    name: MMI evaluation
+- dataset:
+    id: facebook/mmi
+    task_id: error-rate
+  value: 41.10
+  date: "2026-10-20"
+  source:
+    url: https://huggingface.co/datasets/facebook/mmi
+    name: MMI evaluation
+- dataset:
+    id: facebook/mmi
+    task_id: pass-rate-strict
+  value: 9.18
+  date: "2026-10-20"
+  source:
+    url: https://huggingface.co/datasets/facebook/mmi
+    name: MMI evaluation
+- dataset:
+    id: facebook/mmi
+    task_id: pass-rate-lenient
+  value: 10.86
+  date: "2026-10-20"
+  source:
+    url: https://huggingface.co/datasets/facebook/mmi
+    name: MMI evaluation
+- dataset:
+    id: facebook/mmi
+    task_id: recall
+  value: 14.28
+  date: "2026-10-20"
+  source:
+    url: https://huggingface.co/datasets/facebook/mmi
+    name: MMI evaluation
+- dataset:
+    id: facebook/mmi
+    task_id: precision
+  value: 99.78
+  date: "2026-10-20"
+  source:
+    url: https://huggingface.co/datasets/facebook/mmi
+    name: MMI evaluation