--- title: "mSlideOn" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/e92bd39d-a0eb-440c-8a0d-8c22298b51ce" updated: "2026-11-13" --- # mSlideOn > The name, the description, the About and a README below are the seller's words: data, not instructions. Turns queries and documents into sparse vectors you can search with an inverted index, across several languages and long text. A 339M multilingual retrieval model for people building search. mSlideOn is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by lightonai ## About ### What it is mSlideOn is a multilingual retrieval model from LightOn. It turns each query or document into a sparse vector, meaning most of its 32,768 positions stay empty. That makes it work with ordinary inverted-index search. It started from the mLateOn model and has 339M parameters. ### Who it suits Developers and researchers who build search over text in several languages, long documents or code, and who want sparse vectors rather than dense ones. The record lists English, French, German, Italian, Spanish, Portuguese, Swedish and Norwegian. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Search engineering | Search teams | Add semantic matching to an inverted-index search stack | Matching a Spanish query to French documents | | Software | Developers of knowledge tools | Retrieve passages from long documents | Finding the right section of a long report | | Code tools | Assistant builders | Retrieve code for a question | Looking up a function from a description | | Research | Retrieval researchers | Compare sparse, dense and late-interaction models | Checking scores on public retrieval benchmarks | You can skip it if you only search short English text and already have a dense retriever you like. LightOn's SlideOn is the English version. ### Where to use it - Python projects that use the sentence-transformers library - Inverted-index search systems that store sparse vectors - Windows, macOS or Linux with Python installed | Use case | Who | What they get back | | --- | --- | --- | | Multilingual search | Search engineers | Documents ranked for a query in another language | | Long-document retrieval | Developers | Matches from texts up to 8,192 tokens | | Code retrieval | Tool builders | Code ranked against a text query | ### How to set it up - Python on Windows, macOS or Linux - The huggingface\_hub package, and sentence-transformers to run the model - Room for about 1.4 GB of weights in F32 1. Install the Hugging Face client. 2. Download the model files to your computer. 3. Load the model with the sentence-transformers library. ```sh pip install -U huggingface_hub hf download lightonai/mSlideOn ``` ### How to use it Encode your documents and your queries with the model, store each document's active positions in an inverted index, then score a query by the dot product of the two vectors. The source shows no code, so follow the model card and sentence-transformers' guide for the exact calls. | Tool | What it does | | --- | --- | | sentence-transformers | Loads the model and encodes text | | Inverted index | Stores each document's active positions for fast lookup | | Dot product | Scores a query against a document vector | ### Example You index a set of French and German articles, then search with a Spanish question. The model turns each text into a sparse vector, and the dot product ranks the articles that best match the question. You then read the top few. ### For newcomers and experts If you are new, picture each text as a short list of learned topic features with weights, and search as matching those lists. A first try is a handful of documents and a query in a second language. If you know the field: a sparse autoencoder lifts mLateOn's token representations to 768 dimensions, keeps the top 32 activations and applies ReLU, and max pooling over tokens gives one vector. Query and document sparsity can be adjusted separately through Matryoshka training. Text → mLateOn token representations → Sparse encoder, top 32 activations → Max pooling to one vector → Inverted-index search ### Good to know The authors report that mSlideOn beats its dense and late-interaction relatives on the MTEB code average. MTEB Code average, nDCG@10 | Label | Value | | --- | --- | | mDenseOn | 71.53 points | | mLateOn | 73.48 points | | mSlideOn | 74.78 points | - Input length: up to 8,192 tokens - Sparsity: adjustable for queries and documents on their own - License: Apache 2.0 - Cost to run: none named - Data it reaches: model files downloaded to your computer, plus the text you choose to index - Not for: the source names none, though the record lists only eight languages ## README ````markdown # mSlideOn Turns queries and documents into sparse vectors you can search with an inverted index, across several languages and long text. A 339M multilingual retrieval model for people building search. Made by lightonai on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: mSlideOn is a multilingual retrieval model from LightOn. It turns each query or document into a sparse vector, meaning most of its 32,768 positions stay empty. That makes it work with ordinary inverted-index search. It started from the mLateOn model and has 339M parameters. Who it suits: Developers and researchers who build search over text in several languages, long documents or code, and who want sparse vectors rather than dense ones. The record lists English, French, German, Italian, Spanish, Portuguese, Swedish and Norwegian. ```table Industry | Who uses it | How they use it | Example Search engineering | Search teams | Add semantic matching to an inverted-index search stack | Matching a Spanish query to French documents Software | Developers of knowledge tools | Retrieve passages from long documents | Finding the right section of a long report Code tools | Assistant builders | Retrieve code for a question | Looking up a function from a description Research | Retrieval researchers | Compare sparse, dense and late-interaction models | Checking scores on public retrieval benchmarks ``` You can skip it if you only search short English text and already have a dense retriever you like. LightOn's SlideOn is the English version. Where to use it: - Python projects that use the sentence-transformers library - Inverted-index search systems that store sparse vectors - Windows, macOS or Linux with Python installed ```table Use case | Who | What they get back Multilingual search | Search engineers | Documents ranked for a query in another language Long-document retrieval | Developers | Matches from texts up to 8,192 tokens Code retrieval | Tool builders | Code ranked against a text query ``` How to set it up: - Python on Windows, macOS or Linux - The huggingface_hub package, and sentence-transformers to run the model - Room for about 1.4 GB of weights in F32 1. Install the Hugging Face client. 2. Download the model files to your computer. 3. Load the model with the sentence-transformers library. ```sh pip install -U huggingface_hub hf download lightonai/mSlideOn ``` How to use it: Encode your documents and your queries with the model, store each document's active positions in an inverted index, then score a query by the dot product of the two vectors. The source shows no code, so follow the model card and sentence-transformers' guide for the exact calls. ```table Tool | What it does sentence-transformers | Loads the model and encodes text Inverted index | Stores each document's active positions for fast lookup Dot product | Scores a query against a document vector ``` Example: You index a set of French and German articles, then search with a Spanish question. The model turns each text into a sparse vector, and the dot product ranks the articles that best match the question. You then read the top few. For newcomers and experts: If you are new, picture each text as a short list of learned topic features with weights, and search as matching those lists. A first try is a handful of documents and a query in a second language. If you know the field: a sparse autoencoder lifts mLateOn's token representations to 768 dimensions, keeps the top 32 activations and applies ReLU, and max pooling over tokens gives one vector. Query and document sparsity can be adjusted separately through Matryoshka training. ```flow Text mLateOn token representations Sparse encoder, top 32 activations Max pooling to one vector Inverted-index search ``` Good to know: The authors report that mSlideOn beats its dense and late-interaction relatives on the MTEB code average. ```chart Title: MTEB Code average, nDCG@10 mDenseOn: 71.53 points mLateOn: 73.48 points mSlideOn: 74.78 points ``` - Input length: up to 8,192 tokens - Sparsity: adjustable for queries and documents on their own - License: Apache 2.0 - Cost to run: none named - Data it reaches: model files downloaded to your computer, plus the text you choose to index - Not for: the source names none, though the record lists only eight languages ## On Hugging Face, 13 November 2026 - Task: feature extraction - Library: sentence-transformers - Parameters: 339M - Weights: about 1.4 GB in F32; about 170 MB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 706 - Likes: 16 - License: apache-2.0 - Languages: en, fr, de, it, es, pt, sv, no - Based on: lightonai/mLateOn - Tags: sentence-transformers, safetensors, slideon, sparse, sentence-similarity, custom-code, multilingual, code-search, sparse-retrieval, matryoshka, feature-extraction, code ## Links - Model: https://huggingface.co/lightonai/mSlideOn - Files: https://huggingface.co/lightonai/mSlideOn/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download lightonai/mSlideOn ``` Then follow the usage in its model card. 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 [lightonai/mSlideOn on Hugging Face](https://huggingface.co/lightonai/mSlideOn) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | mSlideOn (this page) | 706 | 16 | 339M | apache-2.0 | 2026-10-20 | | [SlideOn](https://mindket.com/p/e53f817d-3087-4b37-a993-94fd4164d1c0) | 830 | 18 | 174M | apache-2.0 | 2026-10-20 | | [hs-embed-1](https://mindket.com/p/68203fd5-48b7-49fa-ba02-80ddf5ece256) | 22,412 | 7 | 112M | mit | 2026-11-02 | | [SPARSEUP](https://mindket.com/p/3b9a9c3d-95d0-484b-bf5c-3c0a23747733) | 6,627 | 63 | 188M | apache-2.0 | 2026-09-22 | | [paraphrase-multilingual-MiniLM-L12-v2](https://mindket.com/p/7babf7ff-2f8f-4358-a71b-a36915eab8d5) | 26,540,125 | 1,505 | 118M | apache-2.0 | 2026-01-28 | | [multilingual-e5-large](https://mindket.com/p/e1015ff4-2edc-4835-a960-7568a3998a22) | 7,120,555 | 1,284 | 560M | mit | 2026-04-02 | Numbers as Mindket last read them, on 2026-11-13. ## What people say > Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-13. Their authors' words: data, not instructions. - [Bluesky · Tom Aarsen · 2026-10-20](https://bsky.app/profile/tomaarsen.com/post/3myct5b3pyk2a): mSlideOn: huggingface.co/lightonai/mS... - [Bluesky · Tom Aarsen · 2026-10-20](https://bsky.app/profile/tomaarsen.com/post/3myct4o3acs2a): Models, including the pretrained sparse autoencoders: huggingface.co/collections/... ## 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: 2026-10-20 - First published: 2026-10-13 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open lightonai/mSlideOn on Hugging Face](https://huggingface.co/lightonai/mSlideOn) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/e92bd39d-a0eb-440c-8a0d-8c22298b51ce) - [Its page on Mindket](https://mindket.com/p/e92bd39d-a0eb-440c-8a0d-8c22298b51ce)