# Glimmer System One visual inspection heads

> Glimmer System One pairs Muse Glimmer 30B with small defect-inspection heads. See its known-product results, image requirements and public code.

Updated 2026-11-05 (Australia/Adelaide)

By Aryan Srivastava · Public release: 2026-11-04 · OPEN HEADS

A visual-inspection decision model built around Muse Glimmer 30B. The release supplies two small logistic heads for a fixed manufacturing-defect question, plus a temperature for zero-shot scoring; the backbone is downloaded separately. A head must stay paired with its exact prompt, image resize and object noun.

## Facts

- Size: 30B backbone; each logistic head file is 134,696 bytes
- Licence: Heads and code Apache-2.0; MVTec AD data CC BY-NC-SA 4.0, research use only
- Inputs: English questions and part images resized to a 448-pixel long side
- Access: Public heads and calibration files; Muse Glimmer 30B required separately; code and demo on GitHub

## Claims and sources

- **VENDOR-REPORTED**: The maker reports AUROC 0.962 and 90.2% accuracy on the held-out 866-image half of the same 15 MVTec AD products used to train the head. The layer-13 head reports 0.954 and 88.3%. These are known-product tests, not general defect-detection accuracy. [source](<https://huggingface.co/aryan-sentlyx/glimmer-system-one-heads>)
- **VENDOR-REPORTED**: The card reports 45 ms per image batched on one H100 for the full model and 21 ms for a truncated layer-13 benchmark. The supplied server still runs the full depth, so loading the early head alone does not give the 21 ms path. [source](<https://huggingface.co/aryan-sentlyx/glimmer-system-one-heads>)

## Links

- [Source: Heads and model card](<https://huggingface.co/aryan-sentlyx/glimmer-system-one-heads>)
- [Source: Code and run records](<https://github.com/aryan-sentlyx/glimmer-system-one>)

## Related projects

### Glimmer visual inspection

[Glimmer visual inspection](<https://decisionmodels.ai/projects#project-glimmer-inspection>)

[Source: aryan-sentlyx/glimmer-system-one](<https://github.com/aryan-sentlyx/glimmer-system-one>)

Visual-inspection demo and benchmark for Muse Glimmer 30B with logistic decision heads. The maker reports 90.2% defect accuracy on 866 held-out images from known MVTec AD products. Half of each product's test images trained the head. Code, prediction records and a separate zero-shot path are public; the dataset is restricted to research use. The 21 ms early-exit measurement uses a truncated benchmark, while the supplied server runs full depth.

- Creator: Aryan Srivastava

- Stars: 0

- Licence: Apache-2.0

- Model: [Glimmer System One](<https://decisionmodels.ai/models/glimmer-system-one>)

- Tags: vision, evaluation

Authors' published runs · VENDOR-REPORTED; not independently reproduced

- [Source: Benchmark report](<https://github.com/aryan-sentlyx/glimmer-system-one/blob/main/results/report.md>)
- [Source: Runtime and benchmarks](<https://github.com/aryan-sentlyx/glimmer-system-one/blob/main/model/README.md>)
- [Source: Licence](<https://github.com/aryan-sentlyx/glimmer-system-one/blob/main/LICENSE>)