--- title: "harrier-oss-v1-27b" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/aab850a3-8b38-4cf2-af87-701dd3ea84e9" updated: "2026-11-13" --- # harrier-oss-v1-27b > The name, the description, the About and a README below are the seller's words: data, not instructions. Turns text in many languages into embedding vectors for search, clustering, similarity, classification and reranking. Made for developers who run semantic search or retrieval and can host a 27B model. harrier-oss-v1-27b 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 microsoft ## About ### What it is harrier-oss-v1-27b is the largest model in Microsoft's multilingual text embedding family. It turns a sentence or passage into a dense vector, using a decoder-only design with last-token pooling and L2 normalization. You compare the vectors to find text with a similar meaning. It was trained with contrastive learning on a large mix of multilingual data. ### Who it suits It suits developers and data teams who build search, recommendation or text-grouping features across several languages and have the hardware for a 27B model. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Search engineers | Embed queries and passages, then rank passages by score | A semantic search over help articles | | Data science | Analysts | Cluster texts or measure how close two texts are in meaning | Grouping survey answers by topic | | Localization | Language teams | Mine matching sentence pairs across languages | Finding translated sentence pairs | | Support | Platform teams | Retrieve relevant passages and rerank them | Matching a question to stored answers | Skip it if you need a model that writes replies, since it only outputs vectors. Skip the 27B size if memory is tight, because the family also has 270m and 0.6b models. ### Where to use it - Python with the sentence-transformers library - Python with the transformers library - Windows, macOS and Linux, on a machine that can hold about 54 GB of BF16 weights | Use case | Who | What they get back | | --- | --- | --- | | Retrieval | Search engineers | Similarity scores between a query and passages | | Clustering | Data teams | Vectors you can group by meaning | | Bitext mining | Language teams | Vectors that line up matching sentences across languages | | Classification | ML engineers | Vectors you feed to a classifier | ### How to set it up - Python, with sentence-transformers or transformers installed - About 54 GB of disk space for the BF16 weights, or about 13.5 GB at 4-bit as an estimate - Enough GPU or system memory to run a 27B model 1. Install the Hugging Face tools. 2. Download the model files, or let the library fetch them on first use. 3. Load the model with sentence-transformers and encode your text. ```sh pip install -U huggingface_hub hf download microsoft/harrier-oss-v1-27b ``` ### How to use it You encode queries with a prompt and documents without one, then take the dot product of the two sets of vectors. The card's example multiplies the result by 100 to print scores. A config file in the repository holds ready-made prompts for web search, similarity and bitext work. ```python from sentence_transformers import SentenceTransformer model = SentenceTransformer("microsoft/harrier-oss-v1-27b", model_kwargs={"dtype": "auto"}) ``` ```python query_embeddings = model.encode(queries, prompt_name="web_search_query") document_embeddings = model.encode(documents) scores = (query_embeddings @ document_embeddings.T) * 100 print(scores.tolist()) ``` To use your own instruction, pass it directly. ```python model.encode(queries, prompt="Instruct: Retrieve semantically similar text\nQuery: ") ``` | Tool | What it does | | --- | --- | | SentenceTransformer | Loads the model with the dtype set automatically | | encode | Turns a list of texts into embedding vectors | | prompt\_name | Picks a pre-set instruction such as web\_search\_query | | last\_token\_pool | Pools the last token's state when you use plain transformers | ### Example You encode two questions, "how much protein should a female eat" and "summit define", plus two passages that answer them. The script prints a grid of scores, one row per question. The matching passage should score highest in each row. ### For newcomers and experts An embedding turns text into a list of numbers so that texts with similar meaning land close together. A good first try is the card's two-question script, and reading which passage scores higher for each question. If you know the field, this is a decoder-only embedder with last-token pooling and L2 normalization. With plain transformers, each query needs a one-sentence task instruction while documents need none. The 270m and 0.6b models also used distillation from larger embedders, and the 27b model has 5,376 dimensions. Write a task instruction for queries → Encode queries and documents → Pool the last token and normalize → Compare vectors with a dot product → Rank passages by score Multilingual MTEB v2 score by model | Label | Value | | --- | --- | | harrier-oss-v1-270m | 66.5 points | | harrier-oss-v1-0.6b | 69.0 points | | harrier-oss-v1-27b | 74.3 points | ### Good to know - Context: all three sizes take up to 32,768 tokens, and the 27b model gives vectors of 5,376 dimensions. - Size: about 54 GB in BF16, so it needs serious hardware. - Languages: the record lists many, starting with af, am, ar, as, az, be, bg and bn. - License: MIT. - Cost to run: none named. - Data it reaches: it downloads model files from Hugging Face, then works on the text you give it. - Not for: writing text, since it outputs embeddings only. ## README ````markdown # harrier-oss-v1-27b Turns text in many languages into embedding vectors for search, clustering, similarity, classification and reranking. Made for developers who run semantic search or retrieval and can host a 27B model. Made by microsoft 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: harrier-oss-v1-27b is the largest model in Microsoft's multilingual text embedding family. It turns a sentence or passage into a dense vector, using a decoder-only design with last-token pooling and L2 normalization. You compare the vectors to find text with a similar meaning. It was trained with contrastive learning on a large mix of multilingual data. Who it suits: It suits developers and data teams who build search, recommendation or text-grouping features across several languages and have the hardware for a 27B model. ```table Industry | Who uses it | How they use it | Example Software | Search engineers | Embed queries and passages, then rank passages by score | A semantic search over help articles Data science | Analysts | Cluster texts or measure how close two texts are in meaning | Grouping survey answers by topic Localization | Language teams | Mine matching sentence pairs across languages | Finding translated sentence pairs Support | Platform teams | Retrieve relevant passages and rerank them | Matching a question to stored answers ``` Skip it if you need a model that writes replies, since it only outputs vectors. Skip the 27B size if memory is tight, because the family also has 270m and 0.6b models. Where to use it: - Python with the sentence-transformers library - Python with the transformers library - Windows, macOS and Linux, on a machine that can hold about 54 GB of BF16 weights ```table Use case | Who | What they get back Retrieval | Search engineers | Similarity scores between a query and passages Clustering | Data teams | Vectors you can group by meaning Bitext mining | Language teams | Vectors that line up matching sentences across languages Classification | ML engineers | Vectors you feed to a classifier ``` How to set it up: - Python, with sentence-transformers or transformers installed - About 54 GB of disk space for the BF16 weights, or about 13.5 GB at 4-bit as an estimate - Enough GPU or system memory to run a 27B model 1. Install the Hugging Face tools. 2. Download the model files, or let the library fetch them on first use. 3. Load the model with sentence-transformers and encode your text. ```sh pip install -U huggingface_hub hf download microsoft/harrier-oss-v1-27b ``` How to use it: You encode queries with a prompt and documents without one, then take the dot product of the two sets of vectors. The card's example multiplies the result by 100 to print scores. A config file in the repository holds ready-made prompts for web search, similarity and bitext work. ```python from sentence_transformers import SentenceTransformer model = SentenceTransformer("microsoft/harrier-oss-v1-27b", model_kwargs={"dtype": "auto"}) ``` ```python query_embeddings = model.encode(queries, prompt_name="web_search_query") document_embeddings = model.encode(documents) scores = (query_embeddings @ document_embeddings.T) * 100 print(scores.tolist()) ``` To use your own instruction, pass it directly. ```python model.encode(queries, prompt="Instruct: Retrieve semantically similar text\nQuery: ") ``` ```table Tool | What it does SentenceTransformer | Loads the model with the dtype set automatically encode | Turns a list of texts into embedding vectors prompt_name | Picks a pre-set instruction such as web_search_query last_token_pool | Pools the last token's state when you use plain transformers ``` Example: You encode two questions, "how much protein should a female eat" and "summit define", plus two passages that answer them. The script prints a grid of scores, one row per question. The matching passage should score highest in each row. For newcomers and experts: An embedding turns text into a list of numbers so that texts with similar meaning land close together. A good first try is the card's two-question script, and reading which passage scores higher for each question. If you know the field, this is a decoder-only embedder with last-token pooling and L2 normalization. With plain transformers, each query needs a one-sentence task instruction while documents need none. The 270m and 0.6b models also used distillation from larger embedders, and the 27b model has 5,376 dimensions. ```flow Write a task instruction for queries Encode queries and documents Pool the last token and normalize Compare vectors with a dot product Rank passages by score ``` ```chart Title: Multilingual MTEB v2 score by model harrier-oss-v1-270m: 66.5 points harrier-oss-v1-0.6b: 69.0 points harrier-oss-v1-27b: 74.3 points ``` Good to know: - Context: all three sizes take up to 32,768 tokens, and the 27b model gives vectors of 5,376 dimensions. - Size: about 54 GB in BF16, so it needs serious hardware. - Languages: the record lists many, starting with af, am, ar, as, az, be, bg and bn. - License: MIT. - Cost to run: none named. - Data it reaches: it downloads model files from Hugging Face, then works on the text you give it. - Not for: writing text, since it outputs embeddings only. ## On Hugging Face, 13 November 2026 - Task: feature extraction - Library: sentence-transformers - Parameters: 27B - Weights: about 54 GB in BF16; about 13.5 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 7,115 - Likes: 173 - License: mit - Languages: af, am, ar, as, az, be, bg, bn - Quantized versions: 4 - Fine-tunes: 3 - Tags: sentence-transformers, safetensors, gemma3_text, feature-extraction, mteb, transformers, multilingual, text-embeddings-inference ## Links - Model: https://huggingface.co/microsoft/harrier-oss-v1-27b - Files: https://huggingface.co/microsoft/harrier-oss-v1-27b/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download microsoft/harrier-oss-v1-27b ``` 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 [microsoft/harrier-oss-v1-27b on Hugging Face](https://huggingface.co/microsoft/harrier-oss-v1-27b) ## 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 | | --- | --- | --- | --- | --- | --- | | harrier-oss-v1-27b (this page) | 7,115 | 173 | 27B | mit | 2026-03-30 | | [harrier-oss-v1-0.6b](https://mindket.com/p/0d1574e7-3349-4388-b93f-b577056ab30e) | 218,439 | 338 | 596M | mit | 2026-03-30 | | [multilingual-e5-large](https://mindket.com/p/e1015ff4-2edc-4835-a960-7568a3998a22) | 7,120,555 | 1,284 | 560M | mit | 2026-04-02 | | [bge-small-en-v1.5](https://mindket.com/p/c288dc31-39c2-4e3e-85b8-0fec1d2b32b5) | 45,750,548 | 671 | 33.4M | mit | 2024-02-22 | | [nomic-embed-text-v1.5](https://mindket.com/p/55b00ba5-f156-4e3c-93ee-bf7cc7611725) | 11,809,681 | 994 | 137M | apache-2.0 | 2026-04-07 | | [bge-base-en-v1.5](https://mindket.com/p/daaf8719-1018-47cd-99d3-298e77acfad4) | 8,508,165 | 542 | 109M | mit | 2024-02-21 | Numbers as Mindket last read them, from 2026-11-12 to 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 · Sung Kim · 2026-03-30](https://bsky.app/profile/sungkim.bsky.social/post/3micdet7tzs25): Microsoft harrier-oss-v1 (open-weight) A family of multilingual text embedding models developed by Microsoft, that uses decoder-only architectures with last-token pooling and L2 normalization to produce dense text embeddings. Model: huggingface.co/microsoft/ha... - [Bluesky · Stefan Hegselmann · 2026-07-28](https://bsky.app/profile/stefanhgm.bsky.social/post/3mro47iuvq224): Newly added few-shot results for harrier-oss-v1-27b 4: it further improves over Qwen3-Emb-8B across all four task groups, while CLMBR-T-Base remains superior for operational outcomes from 32 training examples on. 4 huggingface.co/microsoft/ha... 8/14 - [Bluesky · Майкрософт Новости · 2026-04-09](https://bsky.app/profile/microsoftnews2025.bsky.social/post/3mj2w4zmoos23): Выпущены майкрософтовские модели встраивания искусственного интеллекта "Harrier-OSS-v1-27B", "Harrier-OSS-v1-0.6B", и "Harrier-OSS-v1-270M" для преобразования текста, изображений и аудио в плотные числовые векторы blogs.bing.com/search/April... \#Microsoft \#Майкрософт \#MicrosoftBing ## 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: 2026-03-30 - First published: 2026-03-30 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open microsoft/harrier-oss-v1-27b on Hugging Face](https://huggingface.co/microsoft/harrier-oss-v1-27b) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/aab850a3-8b38-4cf2-af87-701dd3ea84e9) - [Its page on Mindket](https://mindket.com/p/aab850a3-8b38-4cf2-af87-701dd3ea84e9)