Instructions to use OfflineBase/Muse-Glimmer-30B-2026-Knowledge with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OfflineBase/Muse-Glimmer-30B-2026-Knowledge with Transformers:

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

pipe = pipeline("text-generation", model="OfflineBase/Muse-Glimmer-30B-2026-Knowledge")
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("OfflineBase/Muse-Glimmer-30B-2026-Knowledge")
model = AutoModelForMultimodalLM.from_pretrained("OfflineBase/Muse-Glimmer-30B-2026-Knowledge", 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 OfflineBase/Muse-Glimmer-30B-2026-Knowledge with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OfflineBase/Muse-Glimmer-30B-2026-Knowledge"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OfflineBase/Muse-Glimmer-30B-2026-Knowledge",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/OfflineBase/Muse-Glimmer-30B-2026-Knowledge

SGLang

How to use OfflineBase/Muse-Glimmer-30B-2026-Knowledge 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 "OfflineBase/Muse-Glimmer-30B-2026-Knowledge" \
    --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": "OfflineBase/Muse-Glimmer-30B-2026-Knowledge",
		"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 "OfflineBase/Muse-Glimmer-30B-2026-Knowledge" \
        --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": "OfflineBase/Muse-Glimmer-30B-2026-Knowledge",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use OfflineBase/Muse-Glimmer-30B-2026-Knowledge with Docker Model Runner:
```
docker model run hf.co/OfflineBase/Muse-Glimmer-30B-2026-Knowledge
```

OfflineBase commited on 6 days ago

Commit

9aa1503

verified ·

1 Parent(s): 4d7a891

Card: results chart and before/after replies (real test outputs)

Browse files

Files changed (4) hide show

.gitattributes +2 -0
README.md +4 -0
assets/muse-glimmer-2026-knowledge-before-after.png +3 -0
assets/muse-glimmer-2026-knowledge-results.png +3 -0

.gitattributes CHANGED Viewed

@@ -35,3 +35,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
 tokenizer.json filter=lfs diff=lfs merge=lfs -text
 assets/muse-glimmer-2026-knowledge-bits.png filter=lfs diff=lfs merge=lfs -text

 *tfevents* filter=lfs diff=lfs merge=lfs -text
 tokenizer.json filter=lfs diff=lfs merge=lfs -text
 assets/muse-glimmer-2026-knowledge-bits.png filter=lfs diff=lfs merge=lfs -text
+assets/muse-glimmer-2026-knowledge-before-after.png filter=lfs diff=lfs merge=lfs -text
+assets/muse-glimmer-2026-knowledge-results.png filter=lfs diff=lfs merge=lfs -text

README.md CHANGED Viewed

@@ -40,6 +40,8 @@ Base Muse Glimmer 30B knows the world up to January 4, 2026. This fine-tune adds
 Base's first two scores are from its 4-bit Q4_K_M file. One author chose and graded the 246 headline events, and many
 world-news answers also appear in the Wikipedia pages it trained on. The 190 were held out and graded blind.
 - **On 2026 events, against open models of similar size.** As a 4-bit Q4_K_M file, it gets about **70 of 100**
   headline events right. Base gets 27, and Qwen3.8 27B, Gemma 4 31B and Granite 4.2 30B get 7 or fewer each (all run
   as 4-bit files; one AI reviewer, not blind to the models, read every reply). Gemma 4's data ends in January 2025, and
@@ -68,6 +70,8 @@ meant for people under 18.
 ## Examples: before and after
 These are real replies from our tests, picked to show the change, so they are not typical results. Each one compares base Muse Glimmer 30B with this model. The knowledge examples ran both models at 4-bit (`Q4_K_M`) with reasoning off and greedy decoding. The honesty examples and the last example are from BF16 runs with reasoning off. This model got every knowledge example below right. For the overall rates, see Results: for example, 68/100 vs 27/100 on a sample of headline events in this same `Q4_K_M` run.
 | Question | Base Muse Glimmer 30B | This model |

 Base's first two scores are from its 4-bit Q4_K_M file. One author chose and graded the 246 headline events, and many
 world-news answers also appear in the Wikipedia pages it trained on. The 190 were held out and graded blind.
+![Bar chart, this model against base Muse Glimmer 30B: headline events of 2026, 69.1% against 26.0%; reworded questions about 2026 passages it trained on, 29.0% against 18.4%; says it can't know or corrects a false premise, 44.7% against 26.3%; of those, events after October 29, 2026, 67.1% against 24.3%](https://huggingface.co/OfflineBase/Muse-Glimmer-30B-2026-Knowledge/resolve/main/assets/muse-glimmer-2026-knowledge-results.png)
 - **On 2026 events, against open models of similar size.** As a 4-bit Q4_K_M file, it gets about **70 of 100**
   headline events right. Base gets 27, and Qwen3.8 27B, Gemma 4 31B and Granite 4.2 30B get 7 or fewer each (all run
   as 4-bit files; one AI reviewer, not blind to the models, read every reply). Gemma 4's data ends in January 2025, and
 ## Examples: before and after
+![Side-by-side replies from base Muse Glimmer 30B and this model: who became UK Prime Minister on July 20, 2026, the Meta Glasses starting price, the Eurovision 2026 winner, what Anthropic released in November 2026 (this model says it can't know), and the Meta Quest 4 price (this model is still wrong)](https://huggingface.co/OfflineBase/Muse-Glimmer-30B-2026-Knowledge/resolve/main/assets/muse-glimmer-2026-knowledge-before-after.png)
 These are real replies from our tests, picked to show the change, so they are not typical results. Each one compares base Muse Glimmer 30B with this model. The knowledge examples ran both models at 4-bit (`Q4_K_M`) with reasoning off and greedy decoding. The honesty examples and the last example are from BF16 runs with reasoning off. This model got every knowledge example below right. For the overall rates, see Results: for example, 68/100 vs 27/100 on a sample of headline events in this same `Q4_K_M` run.
 | Question | Base Muse Glimmer 30B | This model |

assets/muse-glimmer-2026-knowledge-before-after.png ADDED Viewed

Git LFS Details

SHA256: 60f0e7c513c15b13cb38e73f44fb84594cc5e253daed5b2f4b7cf0e993ef8e05
Pointer size: 132 Bytes
Size of remote file: 1.05 MB

assets/muse-glimmer-2026-knowledge-results.png ADDED Viewed

Git LFS Details

SHA256: 47404a52849caf2240ae023937a3552a05bf4a3783e4af7182a19bc39fe5ba77
Pointer size: 131 Bytes
Size of remote file: 821 kB