--- title: "hs-embed-1" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/68203fd5-48b7-49fa-ba02-80ddf5ece256" updated: "2026-11-13" --- # hs-embed-1 > The name, the description, the About and a README below are the seller's words: data, not instructions. Turns text into compact vectors for search and matching in eight languages, with inputs up to 8,192 tokens. A small 112M embedding model built for Hindsight, running on CPU or GPU. hs-embed-1 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 hindsight-ai ## About ### What it is hs-embed-1 turns text in eight languages into vectors for search, and hindsight-ai made it for use with Hindsight. Each sentence or passage becomes a 384-number vector, and you can shorten that vector to 256, 128 or 64 numbers. It has 112M parameters, accepts inputs up to 8,192 tokens, and needs no prompt prefix for queries or documents. ### Who it suits Developers who build semantic search, retrieval or agent memory and want a small embedding model that runs on a CPU. The files carry the MIT license. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Search | Search engineers | Embed queries and documents for semantic search in eight languages | Matching a question about a move to a sentence naming a city | | AI agents | Agent developers | Give an agent memory by pointing Hindsight at this model | Setting Hindsight to use the local model | | Platform | Infrastructure teams | Serve embeddings from a Text Embeddings Inference server | Running the CPU image with Docker | Anyone who needs a huge embedding model with the highest scores can skip it, since this one is built to be small. ### Where to use it - Python with the sentence-transformers library - A Text Embeddings Inference server, on CPU or GPU - Hindsight, with a local model, a TEI server or the hosted Hindsight Inference service - Windows, macOS or Linux with Python, for downloading the files | Use case | Who | What they get back | | --- | --- | --- | | Semantic search | Developers | Vectors you compare with cosine similarity | | Agent memory | Agent builders | Embeddings that Hindsight stores and searches | | Short vectors | Cost-conscious teams | Vectors cut to 256, 128 or 64 numbers | ### How to set it up - Python and the sentence-transformers package - Docker, only if you serve the model with Text Embeddings Inference - A Hindsight API key, only for the hosted Hindsight Inference service 1. Install Python. 2. Install the sentence-transformers package with the first block below. 3. Optionally copy the model files with Hugging Face's two commands in the second block. ```bash pip install -U sentence-transformers ``` ```sh pip install -U huggingface_hub hf download hindsight-ai/hs-embed-1 ``` ### How to use it You load the model, encode your queries and documents with normalized embeddings, and compare them with the similarity method. ```python from sentence_transformers import SentenceTransformer model = SentenceTransformer("hindsight-ai/hs-embed-1") queries = ["Where did Maya move last spring?"] documents = [ "Maya moved to Lisbon in April.", "Maya's brother lives in Porto.", ] query_embeddings = model.encode(queries, normalize_embeddings=True) document_embeddings = model.encode(documents, normalize_embeddings=True) print(model.similarity(query_embeddings, document_embeddings)) ``` Passing truncate\_dim when you load the model gives shorter vectors. ```python model = SentenceTransformer("hindsight-ai/hs-embed-1", truncate_dim=256) ``` To make Hindsight use the model on your machine, set these two environment variables. ```bash HINDSIGHT_API_EMBEDDINGS_PROVIDER=local HINDSIGHT_API_EMBEDDINGS_LOCAL_MODEL=hindsight-ai/hs-embed-1 ``` | Tool | What it does | | --- | --- | | SentenceTransformer | Loads the model and encodes text | | truncate\_dim | Shortens the vectors to 256, 128 or 64 numbers | | Text Embeddings Inference | Serves the model over HTTP on CPU or GPU | | Hindsight settings | Tell Hindsight which embeddings provider to use | ### Example You ask which city Maya moved to, using the question "Where did Maya move last spring?" against two sentences, one about Lisbon and one about her brother in Porto. The call prints a similarity score for each sentence, and the Lisbon one should score higher. ### For newcomers and experts An embedding turns text into numbers so that similar meanings sit close together. A good first step is to run the Python example above and read the two scores it prints. Most of the 112M parameters, 98M, sit in the token embedding table, which leaves a transformer of just 13M parameters for each pass. Pooling is a mean, similarity is cosine, and the model ships an int8 ONNX export for CPU alongside safetensors for GPU. To shorten vectors by hand, keep the first values and normalize again. Load hs-embed-1 → Encode queries and documents → Normalize the vectors → Compare them with cosine similarity → Rank the matches ### Good to know - License: MIT - Cost to run: none named for local use, while the hosted Hindsight Inference service needs an API key - Data it reaches: the download fetches files from Hugging Face, and the hosted service receives whatever text you send it - Eight languages, among them English, Japanese and Chinese in both scripts Vector sizes you can use | Label | Value | | --- | --- | | Full vector | 384 dims | | Truncated | 256 dims | | Truncated again | 128 dims | | Smallest | 64 dims | ## README ````markdown # hs-embed-1 Turns text into compact vectors for search and matching in eight languages, with inputs up to 8,192 tokens. A small 112M embedding model built for Hindsight, running on CPU or GPU. Made by hindsight-ai 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: hs-embed-1 turns text in eight languages into vectors for search, and hindsight-ai made it for use with Hindsight. Each sentence or passage becomes a 384-number vector, and you can shorten that vector to 256, 128 or 64 numbers. It has 112M parameters, accepts inputs up to 8,192 tokens, and needs no prompt prefix for queries or documents. Who it suits: Developers who build semantic search, retrieval or agent memory and want a small embedding model that runs on a CPU. The files carry the MIT license. ```table Industry | Who uses it | How they use it | Example Search | Search engineers | Embed queries and documents for semantic search in eight languages | Matching a question about a move to a sentence naming a city AI agents | Agent developers | Give an agent memory by pointing Hindsight at this model | Setting Hindsight to use the local model Platform | Infrastructure teams | Serve embeddings from a Text Embeddings Inference server | Running the CPU image with Docker ``` Anyone who needs a huge embedding model with the highest scores can skip it, since this one is built to be small. Where to use it: - Python with the sentence-transformers library - A Text Embeddings Inference server, on CPU or GPU - Hindsight, with a local model, a TEI server or the hosted Hindsight Inference service - Windows, macOS or Linux with Python, for downloading the files ```table Use case | Who | What they get back Semantic search | Developers | Vectors you compare with cosine similarity Agent memory | Agent builders | Embeddings that Hindsight stores and searches Short vectors | Cost-conscious teams | Vectors cut to 256, 128 or 64 numbers ``` How to set it up: - Python and the sentence-transformers package - Docker, only if you serve the model with Text Embeddings Inference - A Hindsight API key, only for the hosted Hindsight Inference service 1. Install Python. 2. Install the sentence-transformers package with the first block below. 3. Optionally copy the model files with Hugging Face's two commands in the second block. ```bash pip install -U sentence-transformers ``` ```sh pip install -U huggingface_hub hf download hindsight-ai/hs-embed-1 ``` How to use it: You load the model, encode your queries and documents with normalized embeddings, and compare them with the similarity method. ```python from sentence_transformers import SentenceTransformer model = SentenceTransformer("hindsight-ai/hs-embed-1") queries = ["Where did Maya move last spring?"] documents = [ "Maya moved to Lisbon in April.", "Maya's brother lives in Porto.", ] query_embeddings = model.encode(queries, normalize_embeddings=True) document_embeddings = model.encode(documents, normalize_embeddings=True) print(model.similarity(query_embeddings, document_embeddings)) ``` Passing truncate_dim when you load the model gives shorter vectors. ```python model = SentenceTransformer("hindsight-ai/hs-embed-1", truncate_dim=256) ``` To make Hindsight use the model on your machine, set these two environment variables. ```bash HINDSIGHT_API_EMBEDDINGS_PROVIDER=local HINDSIGHT_API_EMBEDDINGS_LOCAL_MODEL=hindsight-ai/hs-embed-1 ``` ```table Tool | What it does SentenceTransformer | Loads the model and encodes text truncate_dim | Shortens the vectors to 256, 128 or 64 numbers Text Embeddings Inference | Serves the model over HTTP on CPU or GPU Hindsight settings | Tell Hindsight which embeddings provider to use ``` Example: You ask which city Maya moved to, using the question "Where did Maya move last spring?" against two sentences, one about Lisbon and one about her brother in Porto. The call prints a similarity score for each sentence, and the Lisbon one should score higher. For newcomers and experts: An embedding turns text into numbers so that similar meanings sit close together. A good first step is to run the Python example above and read the two scores it prints. Most of the 112M parameters, 98M, sit in the token embedding table, which leaves a transformer of just 13M parameters for each pass. Pooling is a mean, similarity is cosine, and the model ships an int8 ONNX export for CPU alongside safetensors for GPU. To shorten vectors by hand, keep the first values and normalize again. ```flow Load hs-embed-1 Encode queries and documents Normalize the vectors Compare them with cosine similarity Rank the matches ``` Good to know: - License: MIT - Cost to run: none named for local use, while the hosted Hindsight Inference service needs an API key - Data it reaches: the download fetches files from Hugging Face, and the hosted service receives whatever text you send it - Eight languages, among them English, Japanese and Chinese in both scripts ```chart Title: Vector sizes you can use Full vector: 384 dims Truncated: 256 dims Truncated again: 128 dims Smallest: 64 dims ``` ## On Hugging Face, 13 November 2026 - Task: sentence similarity - Library: sentence-transformers - Parameters: 112M - Weights: about 223 MB in F16; about 56 MB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 22,412 - Likes: 7 - License: mit - Languages: en, es, fr, de, pt, ja, ko, zh - Based on: hotchpotch/bekko-embedding-v1-a25m - Tags: sentence-transformers, onnx, safetensors, modernbert, feature-extraction, embeddings, matryoshka, multilingual, text-embeddings-inference, hindsight, sentence-similarity ## Links - Model: https://huggingface.co/hindsight-ai/hs-embed-1 - Files: https://huggingface.co/hindsight-ai/hs-embed-1/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download hindsight-ai/hs-embed-1 ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 6 KB, downloaded as a zip. ```text README.md 6 KB ``` ## Source [hindsight-ai/hs-embed-1 on Hugging Face](https://huggingface.co/hindsight-ai/hs-embed-1) ## 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 | | --- | --- | --- | --- | --- | --- | | hs-embed-1 (this page) | 22,412 | 7 | 112M | mit | 2026-11-02 | | [hs-rerank-1](https://mindket.com/p/c3b813e1-9df1-49fd-80a2-1a39304f96f1) | 1,148,007 | 6 | 114M | mit | 2026-11-03 | | [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 | | [mSlideOn](https://mindket.com/p/e92bd39d-a0eb-440c-8a0d-8c22298b51ce) | 706 | 16 | 339M | apache-2.0 | 2026-10-20 | | [SPARSEUP](https://mindket.com/p/3b9a9c3d-95d0-484b-bf5c-3c0a23747733) | 6,627 | 63 | 188M | apache-2.0 | 2026-09-22 | Numbers as Mindket last read them, on 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: 2026-11-02 - First published: 2026-10-28 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open hindsight-ai/hs-embed-1 on Hugging Face](https://huggingface.co/hindsight-ai/hs-embed-1) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/68203fd5-48b7-49fa-ba02-80ddf5ece256) - [Its page on Mindket](https://mindket.com/p/68203fd5-48b7-49fa-ba02-80ddf5ece256)