--- title: "Glimmer Scope" kind: "Kit" category: "Research" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/9b39dde0-7257-4e52-9016-9a3bb58178db" updated: "2026-11-12" --- # Glimmer Scope > The name, the description, the About and a README below are the seller's words: data, not instructions. Steer the Muse Glimmer 30B language model with features found inside its own layers. Every question returns two answers, one plain and one steered, so you see what each dial does. Glimmer Scope is a kit in the Research category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by rob-x-ai ## About ### What it is Glimmer Scope lets you steer the Muse Glimmer 30B language model with features taken from inside its own layers. Every question you ask comes back twice. One answer is the model as it is, and the other is the model with a few features turned up. Both replies are produced together under greedy decoding. That leaves the steering as the only thing that differs. ### Who it suits It suits researchers and students who study how language models work inside, and people who want to see feature steering at work on a 30B model. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | AI research | Interpretability researchers | Compare a plain answer with a steered one | Seeing what a single feature from layer 24 does | | AI safety | Safety researchers | Study how internal features change behavior | Checking how a character direction shifts a reply | | Education | Students | Try a dial and read the two answers | Learning what steering a model means | | AI development | Developers | Read app.py to see how the demo runs the model | Reusing the two-answer layout for another model | Skip it if you want an ordinary chat with a model, since each question returns a plain and a steered answer. ### Where to use it The app runs in these places. - A web browser, on the Space's page at Hugging Face - A local copy on Windows, macOS or Linux, running Python 3.12, if you have the hardware - Hugging Face's zero-a10g hardware at its largest ZeroGPU size, where the online Space runs | Use case | Who | What they get back | | --- | --- | --- | | Compare plain and steered answers | Researchers | Two answers to one question | | Try a feature dial | Students | The effect of one internal feature | | Run the interface without a model | Developers | The page alone, with no model loaded | ### How to set it up Online you only need a browser. A local copy needs these. - Python 3.12 on Windows, macOS or Linux - The pip command - A GPU with room for a 30B model at full precision, since the README sizes the online Space at 96 GB - A Hugging Face token with read access, while the dataset it names is private 1. Install the huggingface\_hub package. 2. Download the Space's files into a folder with the hf command. 3. Move into that folder. 4. Install gradio at version 6.31.0. 5. Install the packages listed in requirements.txt. 6. Start the app with python app.py. ```sh pip install -U huggingface_hub hf download rob-x-ai/glimmer-scope --repo-type space --local-dir glimmer-scope cd glimmer-scope pip install gradio==6.31.0 pip install -r requirements.txt python app.py ``` ### How to use it Open the Space's page, or run the command below in your copy and open the page the app serves. Then ask a question, pick the dials to turn up and run it. ```sh python app.py ``` Four environment variables change how a copy runs. GLIMMER\_DIR points the app at a different model folder, and GLIMMER\_DATA does the same for the data folder. VIDEO\_URL puts a demo video link on the page. Setting GLIMMER\_MOCK to 1 runs the interface without a model. ### Example You ask one question and pick a feature dial. The app returns two answers, one from the model as it is and one from the model with that feature turned up. ### For newcomers and experts If you are new, open the Space's page and try one of the supplied examples. Reading the two answers side by side shows what a dial changes. If you know the field, the dials are of two kinds. Some are single features taken from the autoencoders for layers 24 and 36. Others are character directions, found by setting in-character answers against plain ones. The README says each passed an automated steering test with its top grade, and that all concern harmless subjects. Greedy decoding in one batch keeps the comparison fair. Ask a question and pick the dials → The model answers once as it is → The model answers again with features raised → You compare the two answers ### Good to know The online Space asks for the biggest ZeroGPU size, 96 GB, because the model is not shrunk by quantization. Two read-only mounts hold the model and the data, so nothing large downloads at startup. Without the data mount, the Space downloads its dials, examples and images instead. - License: apache-2 - Cost to run: the large ZeroGPU size above. A token secret with read access is needed while the data repository is private - Data it reaches: the Muse Glimmer 30B model, the Glimmer Scope autoencoders and a dataset of dials, examples and images - Not for: casual chat or low-memory machines, since the model runs at full precision ## README ````markdown # Glimmer Scope Steer the Muse Glimmer 30B language model with features found inside its own layers. Every question returns two answers, one plain and one steered, so you see what each dial does. Made by rob-x-ai on Hugging Face. These notes are Mindket's, written from its Space page and README; the app's own files and license are on Hugging Face. ## About What it is: Glimmer Scope lets you steer the Muse Glimmer 30B language model with features taken from inside its own layers. Every question you ask comes back twice. One answer is the model as it is, and the other is the model with a few features turned up. Both replies are produced together under greedy decoding. That leaves the steering as the only thing that differs. Who it suits: It suits researchers and students who study how language models work inside, and people who want to see feature steering at work on a 30B model. ```table Industry | Who uses it | How they use it | Example AI research | Interpretability researchers | Compare a plain answer with a steered one | Seeing what a single feature from layer 24 does AI safety | Safety researchers | Study how internal features change behavior | Checking how a character direction shifts a reply Education | Students | Try a dial and read the two answers | Learning what steering a model means AI development | Developers | Read app.py to see how the demo runs the model | Reusing the two-answer layout for another model ``` Skip it if you want an ordinary chat with a model, since each question returns a plain and a steered answer. Where to use it: The app runs in these places. - A web browser, on the Space's page at Hugging Face - A local copy on Windows, macOS or Linux, running Python 3.12, if you have the hardware - Hugging Face's zero-a10g hardware at its largest ZeroGPU size, where the online Space runs ```table Use case | Who | What they get back Compare plain and steered answers | Researchers | Two answers to one question Try a feature dial | Students | The effect of one internal feature Run the interface without a model | Developers | The page alone, with no model loaded ``` How to set it up: Online you only need a browser. A local copy needs these. - Python 3.12 on Windows, macOS or Linux - The pip command - A GPU with room for a 30B model at full precision, since the README sizes the online Space at 96 GB - A Hugging Face token with read access, while the dataset it names is private 1. Install the huggingface_hub package. 2. Download the Space's files into a folder with the hf command. 3. Move into that folder. 4. Install gradio at version 6.31.0. 5. Install the packages listed in requirements.txt. 6. Start the app with python app.py. ```sh pip install -U huggingface_hub hf download rob-x-ai/glimmer-scope --repo-type space --local-dir glimmer-scope cd glimmer-scope pip install gradio==6.31.0 pip install -r requirements.txt python app.py ``` How to use it: Open the Space's page, or run the command below in your copy and open the page the app serves. Then ask a question, pick the dials to turn up and run it. ```sh python app.py ``` Four environment variables change how a copy runs. GLIMMER_DIR points the app at a different model folder, and GLIMMER_DATA does the same for the data folder. VIDEO_URL puts a demo video link on the page. Setting GLIMMER_MOCK to 1 runs the interface without a model. Example: You ask one question and pick a feature dial. The app returns two answers, one from the model as it is and one from the model with that feature turned up. For newcomers and experts: If you are new, open the Space's page and try one of the supplied examples. Reading the two answers side by side shows what a dial changes. If you know the field, the dials are of two kinds. Some are single features taken from the autoencoders for layers 24 and 36. Others are character directions, found by setting in-character answers against plain ones. The README says each passed an automated steering test with its top grade, and that all concern harmless subjects. Greedy decoding in one batch keeps the comparison fair. ```flow Ask a question and pick the dials The model answers once as it is The model answers again with features raised You compare the two answers ``` Good to know: The online Space asks for the biggest ZeroGPU size, 96 GB, because the model is not shrunk by quantization. Two read-only mounts hold the model and the data, so nothing large downloads at startup. Without the data mount, the Space downloads its dials, examples and images instead. - License: apache-2 - Cost to run: the large ZeroGPU size above. A token secret with read access is needed while the data repository is private - Data it reaches: the Muse Glimmer 30B model, the Glimmer Scope autoencoders and a dataset of dials, examples and images - Not for: casual chat or low-memory machines, since the model runs at full precision ## On Hugging Face, 13 November 2026 - Likes: 2 - Built with: gradio - License: apache-2.0 ## Links - Space: https://huggingface.co/spaces/rob-x-ai/glimmer-scope - Files: https://huggingface.co/spaces/rob-x-ai/glimmer-scope/tree/main ## Try it Open the Space in your browser, or copy it to your own Hugging Face account from its page. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 5 KB, downloaded as a zip. ```text README.md 5 KB ``` ## Source [rob-x-ai/glimmer-scope on Hugging Face](https://huggingface.co/spaces/rob-x-ai/glimmer-scope) ## Compare with similar Listed Hugging Face Spaces in the same category that share the most tags with this one. | Product | Likes | Built with | License | Last change | | --- | --- | --- | --- | --- | | Glimmer Scope (this page) | 2 | gradio | apache-2.0 | 2026-11-04 | | [Open VLM Leaderboard](https://mindket.com/p/9382a913-7563-4967-88b1-0f2458cb00bc) | 1,034 | gradio | apache-2.0 | 2026-03-11 | | [Face Swap](https://mindket.com/p/92787455-1f9f-466a-b574-da22b25d6e0a) | 808 | gradio | cc-by-nc-sa-4.0 | 2024-05-22 | | [TTS Spaces Arena](https://mindket.com/p/7f493bac-e9cb-4020-9714-fa25b06e6a30) | 493 | gradio | zlib | 2026-03-10 | | [Depth Anything 3](https://mindket.com/p/c1e531c5-63e1-4315-a529-7fcae3cb7374) | 490 | gradio | cc-by-nc-4.0 | 2025-11-20 | | [BLIP](https://mindket.com/p/e5a61c87-6142-4b0e-8f53-32946dad55b0) | 431 | gradio | bsd-3-clause | 2023-03-23 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2026-11-04 - First published: 2026-11-03 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open rob-x-ai/glimmer-scope on Hugging Face](https://huggingface.co/spaces/rob-x-ai/glimmer-scope) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/9b39dde0-7257-4e52-9016-9a3bb58178db) - [Its page on Mindket](https://mindket.com/p/9b39dde0-7257-4e52-9016-9a3bb58178db)