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
```

Muse-Glimmer-30B-2026-Knowledge / NOTICE

OfflineBase

Rename to Muse Glimmer

60176c7 verified 9 days ago

Raw History Blame Contribute Delete

1.51 kB

Muse-Glimmer-30B-2026-Knowledge
Copyright 2026 OfflineBase

Built with Muse Spark Open.

This product is a fine-tune of Muse Glimmer 30B (meta-models/Muse-Glimmer-30B,
revision cec94fcfd6512f2cfe708138c32533b346ea4f69), licensed by Meta under the Apache
License, Version 2.0 (see LICENSE).

What we changed:
- We trained LoRA weights (rank 64) on the language model's projection layers. This
  repo holds the weights with the LoRA merged in (BF16). The adapter alone is in
  Muse-Glimmer-30B-2026-Knowledge-LoRA, and the quantized files in Muse-Glimmer-30B-2026-Knowledge-GGUF
  include it.
- We did not change the vision encoder, the tokenizer or the model configuration. The
  tokenizer, processor and config files here are Meta's, unchanged.
- tuned-reasoning-off.jinja is modified from Meta's chat_template.jinja. Unless
  allow_reasoning is set, it opens the reply to the user, with no reasoning channel and
  no tool calls. chat_template.jinja in this repo is Meta's template, unchanged.

Part of the training data was written by Muse Spark Open, so this model is a Derivative
under the Muse Model License (see MUSE_MODEL_LICENSE.md, Exhibit A: Muse Spark Open Usage
Policy). Use of this model must also follow the Muse Glimmer Usage Policy (USAGE_POLICY.md).

Our modifications are licensed under the Apache License, Version 2.0 (see LICENSE).

This is not an official Meta release and is not endorsed by Meta. The names Muse, Muse
Glimmer and Muse Spark appear only to say what this model is built on.

	Muse-Glimmer-30B-2026-Knowledge
	Copyright 2026 OfflineBase

	Built with Muse Spark Open.

	This product is a fine-tune of Muse Glimmer 30B (meta-models/Muse-Glimmer-30B,
	revision cec94fcfd6512f2cfe708138c32533b346ea4f69), licensed by Meta under the Apache
	License, Version 2.0 (see LICENSE).

	What we changed:
	- We trained LoRA weights (rank 64) on the language model's projection layers. This
	repo holds the weights with the LoRA merged in (BF16). The adapter alone is in
	Muse-Glimmer-30B-2026-Knowledge-LoRA, and the quantized files in Muse-Glimmer-30B-2026-Knowledge-GGUF
	include it.
	- We did not change the vision encoder, the tokenizer or the model configuration. The
	tokenizer, processor and config files here are Meta's, unchanged.
	- tuned-reasoning-off.jinja is modified from Meta's chat_template.jinja. Unless
	allow_reasoning is set, it opens the reply to the user, with no reasoning channel and
	no tool calls. chat_template.jinja in this repo is Meta's template, unchanged.

	Part of the training data was written by Muse Spark Open, so this model is a Derivative
	under the Muse Model License (see MUSE_MODEL_LICENSE.md, Exhibit A: Muse Spark Open Usage
	Policy). Use of this model must also follow the Muse Glimmer Usage Policy (USAGE_POLICY.md).

	Our modifications are licensed under the Apache License, Version 2.0 (see LICENSE).

	This is not an official Meta release and is not endorsed by Meta. The names Muse, Muse
	Glimmer and Muse Spark appear only to say what this model is built on.