--- title: "Qwen3-Embedding-4B" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/9b7e2d70-d697-4875-b0e8-eeb03f550132" updated: "2026-11-12" --- # Qwen3-Embedding-4B > The name, the description, the About and a README below are the seller's words: data, not instructions. Turns text and code in over 100 languages into vectors for search, retrieval, classification and clustering, with adjustable vector size and task instructions, for developers building search and RAG systems. Qwen3-Embedding-4B 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-4B turns text into vectors, so you can compare meaning by comparing numbers. It has 4B parameters, reads up to 32k tokens and supports over 100 languages, including programming languages. It belongs to the Qwen3 Embedding series, which also has reranking models. The vector size can be set anywhere from 32 to 2560. ### Who it suits Developers who build search, retrieval or recommendation features will get the most from it, as will teams who sort or group large piles of text. It suits multilingual work well. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Search engineers | Embed documents and queries, then rank by closeness | Semantic search over a knowledge base | | Support | Support teams | Match a new question to past answers | Suggesting related help articles | | Data teams | Analysts | Group similar texts into clusters | Sorting survey comments by theme | | Code tools | Developer-tool builders | Retrieve code snippets from a plain-language query | Finding a function by describing it | If you need a model that writes text, you can skip it, since this one only produces vectors. ### Where to use it - Python with the sentence-transformers library, which Hugging Face lists - Search and retrieval pipelines that store vectors - Windows, macOS or Linux with Python, to download the files | Use case | Who | What they get back | | --- | --- | --- | | Text retrieval | Search engineers | A vector for each query and document | | Text classification | ML engineers | Vectors to feed a classifier | | Clustering | Analysts | Vectors that group similar texts | | Bitext mining | Translation teams | Vectors to pair matching sentences across languages | ### How to set it up You need Python with pip and transformers 4.51.0 or newer. Older versions stop with a KeyError for qwen3. The weights are about 8 GB in BF16. - Python with pip, on Windows, macOS or Linux - sentence-transformers - transformers 4.51.0 or newer - About 8 GB of space for BF16 weights 1. Install the Hugging Face hub package. 2. Download the model files. 3. Load the model with sentence-transformers. ```sh pip install -U huggingface_hub hf download Qwen/Qwen3-Embedding-4B ``` ### How to use it Pass your texts to the model through sentence-transformers and it returns one vector for each. You can choose a smaller vector size, and you can add an instruction that describes your task. The source shows no code, so follow the model page for the exact call. | Tool | What it does | | --- | --- | | sentence-transformers | The library that loads the model and makes the vectors | | Custom dimensions | Sets the output vector size from 32 up to 2560 | | Instructions | Adds task text to the input, which the card says helps by 1 to 5 percent | ### Example You embed the question "How do I reset my password" together with a set of help articles. You rank the articles by how close their vectors are to the question and show the top few. ### For newcomers and experts An embedding is a list of numbers that stands for the meaning of a text, so similar texts get similar lists. A good first step is to embed two short sentences and compare them. The model is built on Qwen3-4B-Base with 36 layers and a 32K sequence length. Its dimension can be shrunk, which the card calls MRL support. It is instruction aware, and the card advises writing instructions in English even for other languages. It pairs with the series rerankers, which can reorder the top results. You pass in text, plus an optional task instruction → The model reads it with its 36 layers → It returns one vector, up to 2560 dimensions → You compare vectors to rank or group texts Embedding dimension by model size | Label | Value | | --- | --- | | 0.6B model | 1024 dimensions | | 4B model | 2560 dimensions | | 8B model | 4096 dimensions | ### Good to know - License: apache-2.0, as listed on Hugging Face - Cost to run: none named - Data it reaches: the text you pass in, and the source names no web or account access - Supports over 100 languages, including programming languages - Not for: text generation, because it only produces vectors - Needs transformers 4.51.0 or newer ## README ````markdown # Qwen3-Embedding-4B Turns text and code in over 100 languages into vectors for search, retrieval, classification and clustering, with adjustable vector size and task instructions, for developers building search and RAG systems. 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-4B turns text into vectors, so you can compare meaning by comparing numbers. It has 4B parameters, reads up to 32k tokens and supports over 100 languages, including programming languages. It belongs to the Qwen3 Embedding series, which also has reranking models. The vector size can be set anywhere from 32 to 2560. Who it suits: Developers who build search, retrieval or recommendation features will get the most from it, as will teams who sort or group large piles of text. It suits multilingual work well. ```table Industry | Who uses it | How they use it | Example Software | Search engineers | Embed documents and queries, then rank by closeness | Semantic search over a knowledge base Support | Support teams | Match a new question to past answers | Suggesting related help articles Data teams | Analysts | Group similar texts into clusters | Sorting survey comments by theme Code tools | Developer-tool builders | Retrieve code snippets from a plain-language query | Finding a function by describing it ``` If you need a model that writes text, you can skip it, since this one only produces vectors. Where to use it: - Python with the sentence-transformers library, which Hugging Face lists - Search and retrieval pipelines that store vectors - Windows, macOS or Linux with Python, to download the files ```table Use case | Who | What they get back Text retrieval | Search engineers | A vector for each query and document Text classification | ML engineers | Vectors to feed a classifier Clustering | Analysts | Vectors that group similar texts Bitext mining | Translation teams | Vectors to pair matching sentences across languages ``` How to set it up: You need Python with pip and transformers 4.51.0 or newer. Older versions stop with a KeyError for qwen3. The weights are about 8 GB in BF16. - Python with pip, on Windows, macOS or Linux - sentence-transformers - transformers 4.51.0 or newer - About 8 GB of space for BF16 weights 1. Install the Hugging Face hub package. 2. Download the model files. 3. Load the model with sentence-transformers. ```sh pip install -U huggingface_hub hf download Qwen/Qwen3-Embedding-4B ``` How to use it: Pass your texts to the model through sentence-transformers and it returns one vector for each. You can choose a smaller vector size, and you can add an instruction that describes your task. The source shows no code, so follow the model page for the exact call. ```table Tool | What it does sentence-transformers | The library that loads the model and makes the vectors Custom dimensions | Sets the output vector size from 32 up to 2560 Instructions | Adds task text to the input, which the card says helps by 1 to 5 percent ``` Example: You embed the question "How do I reset my password" together with a set of help articles. You rank the articles by how close their vectors are to the question and show the top few. For newcomers and experts: An embedding is a list of numbers that stands for the meaning of a text, so similar texts get similar lists. A good first step is to embed two short sentences and compare them. The model is built on Qwen3-4B-Base with 36 layers and a 32K sequence length. Its dimension can be shrunk, which the card calls MRL support. It is instruction aware, and the card advises writing instructions in English even for other languages. It pairs with the series rerankers, which can reorder the top results. ```flow You pass in text, plus an optional task instruction The model reads it with its 36 layers It returns one vector, up to 2560 dimensions You compare vectors to rank or group texts ``` ```chart Title: Embedding dimension by model size 0.6B model: 1024 dimensions 4B model: 2560 dimensions 8B model: 4096 dimensions ``` Good to know: - License: apache-2.0, as listed on Hugging Face - Cost to run: none named - Data it reaches: the text you pass in, and the source names no web or account access - Supports over 100 languages, including programming languages - Not for: text generation, because it only produces vectors - Needs transformers 4.51.0 or newer ## On Hugging Face, 13 November 2026 - Task: feature extraction - Library: sentence-transformers - Parameters: 4B - Weights: about 8 GB in BF16; about 2 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 2,426,700 - Likes: 351 - License: apache-2.0 - Based on: Qwen/Qwen3-4B-Base - Quantized versions: 53 - Fine-tunes: 77 - Tags: sentence-transformers, safetensors, qwen3, text-generation, transformers, sentence-similarity, feature-extraction, text-embeddings-inference ## Links - Model: https://huggingface.co/Qwen/Qwen3-Embedding-4B - Files: https://huggingface.co/Qwen/Qwen3-Embedding-4B/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-4B ``` 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-4B on Hugging Face](https://huggingface.co/Qwen/Qwen3-Embedding-4B) ## 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-4B (this page) | 2,426,700 | 351 | 4B | apache-2.0 | 2025-06-20 | | [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-8B](https://mindket.com/p/af9489b9-e6d2-42c5-a898-2313ef4df9b6) | 2,358,928 | 858 | 7.6B | apache-2.0 | 2025-07-07 | | [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 · karmakaze · 2026-07-09](https://news.ycombinator.com/item?id=48841008): Turning a pile of documents into a searchable useable knowledge base: I learned a solution is to turn the documents into vectors in say PostgreSQL (with pgvector) and do a cosine similarity search with a search vector. Doing a search for embed models on HuggingFace shows nomic-ai/nomic-embed-text-v1.5 and Qwen/Qwen3-Embedding-0.6B. I might have used a larger one… - [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 ## 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: 2025-06-20 - 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-4B on Hugging Face](https://huggingface.co/Qwen/Qwen3-Embedding-4B) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/9b7e2d70-d697-4875-b0e8-eeb03f550132) - [Its page on Mindket](https://mindket.com/p/9b7e2d70-d697-4875-b0e8-eeb03f550132)