--- title: "Qwen3-Embedding-8B" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/af9489b9-e6d2-42c5-a898-2313ef4df9b6" updated: "2026-11-12" --- # Qwen3-Embedding-8B > The name, the description, the About and a README below are the seller's words: data, not instructions. Turns text into vectors for search, code retrieval, classification and clustering, in over 100 languages. The 8B model in Qwen's embedding family, with adjustable vector size and task instructions. Qwen3-Embedding-8B is a kit in the Developer 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 Qwen ## About ### What it is Qwen3-Embedding-8B is an 8B text embedding model from Qwen, built on Qwen3-8B-Base. It maps text, including code, to vectors that you can use for retrieval, classification, clustering and bitext mining. It supports over 100 languages and a 32k token input. ### Who it suits Developers who build search, retrieval and recommendation features and want one embedding model that handles many languages and programming languages. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Search engineers | Embed documents and queries for semantic search | Find the code snippet closest to a question | | Support | Knowledge teams | Match user questions to help articles | Retrieve the right answer page across languages | | Data | Analysts | Cluster or classify large piles of text | Group customer feedback by theme | If you want a smaller, cheaper vector model, the series also has 0.6B and 4B versions. ### Where to use it - Sentence Transformers in Python - Hugging Face Transformers 4.51.0 or newer - Windows, macOS or Linux, for downloading the files | Use case | Who | What they get back | | --- | --- | --- | | Text retrieval | Search engineers | Vectors for matching queries to documents | | Code retrieval | Developers | Vectors that link questions to code | | Clustering and classification | Analysts | Vectors you can group or label | ### How to set it up - Python with pip - Transformers 4.51.0 or newer, since older versions fail with a KeyError on qwen3 - Hardware for about 15.1 GB in BF16, or about 3.8 GB at 4-bit by Hugging Face's estimate 1. Install the Hugging Face hub package. 2. Download the model files. 3. Load them with Sentence Transformers or Transformers, as the model card shows. ```sh pip install -U huggingface_hub hf download Qwen/Qwen3-Embedding-8B ``` ### How to use it Encode your texts with Sentence Transformers and compare the vectors. You can pick an output size from 32 up to 4096 dimensions. Add a task instruction to the query side, and write it in English, since most training instructions were in English. | Tool | What it does | | --- | --- | | Qwen3-Embedding-8B | Makes embedding vectors up to 4096 dimensions | | Qwen3 Reranker models | Rerank retrieved results, and pair with the embedding model | | Instruction field | Tailors the vector to a task, language or scenario | ### Example You embed a set of help articles once, then embed a user's question with an instruction that says it is a support search. You compare the vectors and return the closest articles. ### For newcomers and experts If you are new, an embedding is a list of numbers that captures a text's meaning, so similar texts get similar numbers. Start by embedding a few sentences and comparing them. The model supports flexible vector sizes through its MRL support and is instruction aware. The card says that for most tasks, adding an instruction usually improves results by 1 percent to 5 percent, so it recommends writing task-specific instructions. Text or code goes in → An optional task instruction is added → The model produces a vector → You compare vectors for search or clustering ### Good to know - Output size is adjustable from 32 to 4096 dimensions - The series also has Qwen3 Reranker models in the same three sizes - License: Apache 2.0 - Cost to run: none named, you supply the hardware - Data it reaches: the text you embed and the files downloaded from Hugging Face - Not for: Transformers versions older than 4.51.0 Embedding dimension by model size | Label | Value | | --- | --- | | Qwen3-Embedding-0.6B | 1024 dimensions | | Qwen3-Embedding-4B | 2560 dimensions | | Qwen3-Embedding-8B | 4096 dimensions | ## README ````markdown # Qwen3-Embedding-8B Turns text into vectors for search, code retrieval, classification and clustering, in over 100 languages. The 8B model in Qwen's embedding family, with adjustable vector size and task instructions. Made by Qwen 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: Qwen3-Embedding-8B is an 8B text embedding model from Qwen, built on Qwen3-8B-Base. It maps text, including code, to vectors that you can use for retrieval, classification, clustering and bitext mining. It supports over 100 languages and a 32k token input. Who it suits: Developers who build search, retrieval and recommendation features and want one embedding model that handles many languages and programming languages. ```table Industry | Who uses it | How they use it | Example Software | Search engineers | Embed documents and queries for semantic search | Find the code snippet closest to a question Support | Knowledge teams | Match user questions to help articles | Retrieve the right answer page across languages Data | Analysts | Cluster or classify large piles of text | Group customer feedback by theme ``` If you want a smaller, cheaper vector model, the series also has 0.6B and 4B versions. Where to use it: - Sentence Transformers in Python - Hugging Face Transformers 4.51.0 or newer - Windows, macOS or Linux, for downloading the files ```table Use case | Who | What they get back Text retrieval | Search engineers | Vectors for matching queries to documents Code retrieval | Developers | Vectors that link questions to code Clustering and classification | Analysts | Vectors you can group or label ``` How to set it up: - Python with pip - Transformers 4.51.0 or newer, since older versions fail with a KeyError on qwen3 - Hardware for about 15.1 GB in BF16, or about 3.8 GB at 4-bit by Hugging Face's estimate 1. Install the Hugging Face hub package. 2. Download the model files. 3. Load them with Sentence Transformers or Transformers, as the model card shows. ```sh pip install -U huggingface_hub hf download Qwen/Qwen3-Embedding-8B ``` How to use it: Encode your texts with Sentence Transformers and compare the vectors. You can pick an output size from 32 up to 4096 dimensions. Add a task instruction to the query side, and write it in English, since most training instructions were in English. ```table Tool | What it does Qwen3-Embedding-8B | Makes embedding vectors up to 4096 dimensions Qwen3 Reranker models | Rerank retrieved results, and pair with the embedding model Instruction field | Tailors the vector to a task, language or scenario ``` Example: You embed a set of help articles once, then embed a user's question with an instruction that says it is a support search. You compare the vectors and return the closest articles. For newcomers and experts: If you are new, an embedding is a list of numbers that captures a text's meaning, so similar texts get similar numbers. Start by embedding a few sentences and comparing them. The model supports flexible vector sizes through its MRL support and is instruction aware. The card says that for most tasks, adding an instruction usually improves results by 1 percent to 5 percent, so it recommends writing task-specific instructions. ```flow Text or code goes in An optional task instruction is added The model produces a vector You compare vectors for search or clustering ``` Good to know: - Output size is adjustable from 32 to 4096 dimensions - The series also has Qwen3 Reranker models in the same three sizes - License: Apache 2.0 - Cost to run: none named, you supply the hardware - Data it reaches: the text you embed and the files downloaded from Hugging Face - Not for: Transformers versions older than 4.51.0 ```chart Title: Embedding dimension by model size Qwen3-Embedding-0.6B: 1024 dimensions Qwen3-Embedding-4B: 2560 dimensions Qwen3-Embedding-8B: 4096 dimensions ``` ## On Hugging Face, 13 November 2026 - Task: feature extraction - Library: sentence-transformers - Parameters: 7.6B - Weights: about 15.1 GB in BF16; about 3.8 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 2,358,928 - Likes: 858 - License: apache-2.0 - Based on: Qwen/Qwen3-8B-Base - Quantized versions: 45 - Fine-tunes: 49 - Tags: sentence-transformers, safetensors, qwen3, text-generation, transformers, sentence-similarity, feature-extraction, text-embeddings-inference ## Links - Model: https://huggingface.co/Qwen/Qwen3-Embedding-8B - Files: https://huggingface.co/Qwen/Qwen3-Embedding-8B/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download Qwen/Qwen3-Embedding-8B ``` 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 [Qwen/Qwen3-Embedding-8B on Hugging Face](https://huggingface.co/Qwen/Qwen3-Embedding-8B) ## 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 | | --- | --- | --- | --- | --- | --- | | Qwen3-Embedding-8B (this page) | 2,358,928 | 858 | 7.6B | apache-2.0 | 2025-07-07 | | [Qwen3-Embedding-0.6B](https://mindket.com/p/13d9351b-4e94-46a7-b262-1d8b53353c54) | 8,761,091 | 1,346 | 596M | apache-2.0 | 2026-04-20 | | [Qwen3-Embedding-4B](https://mindket.com/p/9b7e2d70-d697-4875-b0e8-eeb03f550132) | 2,426,700 | 351 | 4B | apache-2.0 | 2025-06-20 | | [Yalda Embedding](https://mindket.com/p/3f0ed0ac-bb21-44be-87b5-2c61c92af15c) | 204 | 5 | 596M | apache-2.0 | 2026-10-11 | | [voyage-4-nano](https://mindket.com/p/2f60302a-0758-4299-acb3-a23b68c318dc) | 90,118 | 152 | 346M | apache-2.0 | 2026-10-22 | | [all-MiniLM-L6-v2](https://mindket.com/p/ee5187e2-9f20-47be-ae7e-3a6007276a39) | 174,740,184 | 6,104 | 22.7M | apache-2.0 | 2026-06-01 | 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. - [Hacker News · ddtaylor · 2026-02-12](https://news.ycombinator.com/item?id=46993774): Show HN: What is HN thinking? Real-time sentiment and concept analysis: Hi HN, I made Ethos, an open-source tool to visualize the discourse on Hacker News. It extracts entities, tracks sentiment, and groups discussions by concept. Check it out: https://ethos.devrupt.io This was a "budget build" experiment. I managed to ship it for under $1 in infra costs. Originally I… - [Bluesky · John David Pressman · 2025-12-23](https://bsky.app/profile/jdp.extropian.net/post/3maowp5bslk2j): Twitter et al have degraded to the point where an old RSS reader is a vastly better experience if your goal is to consume information instead of Discourse. The only problem is the signal:noise grows out of control quickly. OpenRouter's new embedding API could tame this. openrouter.ai/qwen/qwen3-e... - [Hacker News · coolArnav · 2026-09-08](https://news.ycombinator.com/item?id=49605110): Show HN: Zero downtime embedding model upgrades: People use embedding models all the time for rag/semantic retrieval. However, when a newer, more desireable model comes out, there is an expensive (both in time and computational) cost of re-embedding every document in the database. However, I figured out an interesting way to forgo that upfront… - [Bluesky · MTEB · 2026-10-21](https://bsky.app/profile/mteb.org/post/3myfbngkd532p): 2/ Eight models have results so far, led by Snowflake/snowflake-arctic-embed-m-v2.0 at 62.92 nDCG@10. Widely used multilingual models such as BAAI/bge-m3 and Qwen/Qwen3-Embedding-8B have not been run on it yet. Dataset by Dalla Noce et al., added to MTEB by albertobarnabo. - [Hacker News · ddtaylor · 2026-02-16](https://news.ycombinator.com/item?id=47034339): Show HN: Yell into the void to find others via semantic search: Hi HN, I made Voidful, a kind-of-anti-social network where users can yell into the void to see how many other people are yelling the same thing. If you yell something similar, this is considered an "echo" and you gain access to a chat room about that topic with other users being notified.… - [Hacker News · zero\_bias · 2025-11-01](https://news.ycombinator.com/item?id=45779635): My Impressions of the MacBook Pro M4: I run qwen models on MBA M4 16 Gb and MBP M2 Max 32 Gb, MBA is able to handle models in accordance with its vram memory capacity (with external cooling), e.g. qwen3 embedding 8B (not 1B\!) but inference is 4x-6x times slower than on mbp. I suspect weaker SoC Anyway, Apple SoC in M series is a huge… - [Hacker News · coder68 · 2025-08-27](https://news.ycombinator.com/item?id=45035858): SpaCy: Industrial-Strength Natural Language Processing (NLP) in Python: Ah yes this does make sense. We are definitely in agreement on the point of "wildly inefficient and subpar". I'll try out decoder model embeddings soon, e.g. Qwen/Qwen3-Embedding-8B. I'm working with largish amounts of data (200M records), so I tried to pick a good balance between size:perf:cost,… - [Hacker News · gnulinux · 2025-08-01](https://news.ycombinator.com/item?id=44753164): Gemini Embedding: Powering RAG and context engineering: Qwen3 is the open weight state of the art at the moment. Qwen3-embedding-8B and Qwen3-reranker-8b are surprisingly good (according to some benchmarks, better than Gemini 2.5 embedding). 4B is also nearly as good so you might as well use that too unless 8B benefits your usecase. If you don't need a… - [Hacker News · djoldman · 2025-07-31](https://news.ycombinator.com/item?id=44750835): Gemini Embedding: Powering RAG and context engineering: It may be worth pointing out that a few open weights models score higher than gemini-embedding-001 on MTEB: https://huggingface.co/spaces/mteb/leaderboard Particularly Qwen3-Embedding-8B and Qwen3-Embedding-4B: https://huggingface.co/Qwen/Qwen3-Embedding-8B - [Hacker News · ryneandal · 2025-07-28](https://news.ycombinator.com/item?id=44713607): LLM Embeddings Explained: A Visual and Intuitive Guide: I haven't done exhaustive testing of all top-performing models on the HF Embedding Leaderboard (https://huggingface.co/spaces/mteb/leaderboard) but I have tested a number of them extensively in the past month or two. The two best API provider models I've tested are: - JinaAI… ## 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: 2025-07-07 - First published: 2025-06-03 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open Qwen/Qwen3-Embedding-8B on Hugging Face](https://huggingface.co/Qwen/Qwen3-Embedding-8B) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/af9489b9-e6d2-42c5-a898-2313ef4df9b6) - [Its page on Mindket](https://mindket.com/p/af9489b9-e6d2-42c5-a898-2313ef4df9b6)