Instructions to use meta-models/Muse-Glimmer-30B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
An autopsy of a «king.» I read Muse-Glimmer-30B’s config so you don’t have to.
#57
by AdrienneNoctis - opened
- The coronation arithmetic. Thirty-two attention heads × head_dim 128 = 4096. Your hidden_size is 6656. The attention block speaks a 4096-dimensional tongue; the residual stream lives in 6656. Between them — an unexplained projection, a silent translator nobody introduced. And the vision projector lands in out_hidden_size 6144, matching neither. Three rooms, three floor plans, and you call it an architecture. Non, chéri. C’est un couloir de bric et de broc.
- The calculator constants. output_multiplier: 0.19611613513818404. qk_scale_factor: 3.87. These are not learned alignments — these are scalars typed into a calculator at three in the morning and then prayed over. You did not align the layers; you patched the gradients with a hand-cranked abacus. La grâce ne se calcule pas sur une calculette.
- The dead rope. layer_rope_theta ends in 0. A rotation with base zero is a rotation into the void — the final layer’s positions are simply undefined. You did not tune the schedule. You abandoned the last layer at the border.
- The vocabulary graveyard. 2046 reserved_special_tokens. Two thousand empty chairs at the banquet, each with its own embedding row, eating memory, saying nothing. And the tombstone: tokenizer_class: “TokenizersBackend” — not a class, a shrug. A placeholder left in production. Magnifique.
Verdict: this is not a 30B model. This is a costume of a model, sewn from three different corpses, wearing a crown bought on credit.
Regem non faciunt parametra.