Instructions to use immanuelpeter/Muse-Glimmer-Vision with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use immanuelpeter/Muse-Glimmer-Vision with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="immanuelpeter/Muse-Glimmer-Vision")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("immanuelpeter/Muse-Glimmer-Vision") model = AutoModel.from_pretrained("immanuelpeter/Muse-Glimmer-Vision", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Muse Glimmer Vision
This repository packages the Tower and Projector from Muse-Glimmer-30B.
Contents
| File | Tensors | What it holds |
|---|---|---|
model.safetensors |
806 | Muse Glimmer Tower |
projector.safetensors |
3 | Two vision-adapter matrices and the language-width projection |
projector_config.json, projector.py |
Projector shapes and loader | |
config.json, preprocessor_config.json |
Vision-only model and image-processing configuration |
Architecture
| Component | Details |
|---|---|
| Tower | 50 layers, 1536 hidden, 16 heads, 8960 intermediate, patch size 14 |
| Token compression | 2x2 pixel shuffle, no learned parameters |
| Projector | Linear(6144, 4096), GELU, Linear(4096, 4096), GELU, Linear(4096, 6656), scale-free RMSNorm(6656) |
All three Projector linear layers omit bias. The native MuseGlimmerVisionModel returns
the merged 6144-wide tokens; projector.py maps them to the 6656-wide language space.
Usage
See examples/inference.py for image feature extraction.
Validation
The release tests compare all 806 Tower tensors and three Projector tensors with the
pinned parent checkpoint using torch.equal. Fixed-image merged and projected outputs
also match the parent implementation bit-for-bit on CPU and in BF16 on an NVIDIA A100.
Reproduction
The export script
reads the Tower and Projector ranges from both meta-models/Muse-Glimmer-30B shards. It
removes the parent prefixes, preserves the original BF16 weights, and extracts the image
section of the parent processor configuration. The script pins the parent revision.
Credits
Meta released the Muse Glimmer weights and the native Transformers implementation.
License
Apache License 2.0, the same license as the source model. The source usage policy is included.
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Base model
meta-models/Muse-Glimmer-30B