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)

# 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=40)
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
```

Add community evaluation results

by SaylorTwift HF Staff - opened 1 day ago

base: refs/heads/main

←

from: refs/pr/6

Discussion Files changed

+80

-0

YAML Metadata Error:Invalid content in Eval Result file .eval_results/Muse-Glimmer-30B.yaml

Check out the documentation for more information.

Show details

Task ID "hle" does not match any task in dataset "cais/hle". Available: none

Files changed (1) hide show

.eval_results/Muse-Glimmer-30B.yaml +80 -0

.eval_results/Muse-Glimmer-30B.yaml ADDED Viewed

	@@ -0,0 +1,80 @@

+- dataset:
+    id: Idavidrein/gpqa
+    task_id: diamond
+  value: 83.5
+  date: "2026-08-10"
+  source:
+    url: https://huggingface.co/meta-models/Muse-Glimmer-30B
+    name: "Muse Glimmer-30B model card"
+- dataset:
+    id: MMMU/MMMU_Pro
+    task_id: mmmu_pro_standard_10_options
+  value: 74
+  date: "2026-08-10"
+  source:
+    url: https://huggingface.co/meta-models/Muse-Glimmer-30B
+    name: "Muse Glimmer-30B model card"
+- dataset:
+    id: MathArena/aime_2026
+    task_id: MathArena/aime_2026
+  value: 94.7
+  date: "2026-08-10"
+  source:
+    url: https://huggingface.co/meta-models/Muse-Glimmer-30B
+    name: "Muse Glimmer-30B model card"
+- dataset:
+    id: SWE-bench/SWE-bench_Verified
+    task_id: swe_bench_%_resolved
+  value: 76.0
+  date: "2026-08-10"
+  source:
+    url: https://huggingface.co/meta-models/Muse-Glimmer-30B
+    name: "Muse Glimmer-30B model card"
+- dataset:
+    id: ScaleAI/SWE-bench_Pro
+    task_id: SWE_Bench_Pro
+  value: 51.2
+  date: "2026-08-10"
+  source:
+    url: https://huggingface.co/meta-models/Muse-Glimmer-30B
+    name: "Muse Glimmer-30B model card"
+- dataset:
+    id: cais/hle
+    task_id: hle
+  value: 22.0
+  date: "2026-08-10"
+  source:
+    url: https://huggingface.co/meta-models/Muse-Glimmer-30B
+    name: "Muse Glimmer-30B model card"
+- dataset:
+    id: benchflow/skillsbench
+    task_id: skillsbench_v1_1
+  value: 44.3
+  date: "2026-08-10"
+  source:
+    url: https://huggingface.co/meta-models/Muse-Glimmer-30B
+    name: "Muse Glimmer-30B model card"
+- dataset:
+    id: internlm/WildClawBench
+    task_id: overall
+  value: 47.6
+  date: "2026-08-10"
+  source:
+    url: https://huggingface.co/meta-models/Muse-Glimmer-30B
+    name: "Muse Glimmer-30B model card"
+- dataset:
+    id: likaixin/ScreenSpot-Pro
+    task_id: overall
+  value: 75.4
+  date: "2026-08-10"
+  source:
+    url: https://huggingface.co/meta-models/Muse-Glimmer-30B
+    name: "Muse Glimmer-30B model card"