--- title: "bge-small-en-v1.5-GGUF" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/0eeb6b56-65ff-469a-9156-62dec1246a55" updated: "2026-11-13" --- # bge-small-en-v1.5-GGUF > The name, the description, the About and a README below are the seller's words: data, not instructions. Turns English text into embeddings for search and retrieval, in a small GGUF file of 33.2M parameters. For developers who build search or retrieval apps on modest hardware. bge-small-en-v1.5-GGUF 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 unsloth ## About ### What it is bge-small-en-v1.5-GGUF is a GGUF copy of the small English BGE embedding model, shared by unsloth, with 33.2M parameters. BGE is short for BAAI General Embedding. An embedding model turns text into numbers so that similar passages can be found by comparing them. The card text comes from the FlagEmbedding project. ### Who it suits Developers who build search or retrieval features over English text are the main fit, and the small file suits modest hardware. NLP researchers can use it as a compact embedding baseline. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Search | Software developers | Turn English text into vectors for similarity search | Find notes that relate to a question | | AI apps | App builders | Retrieve passages to give a language model as context | Fetch the best passages for a chatbot answer | | Research | NLP researchers | Compare small embedding models | Use it as a compact baseline | If your text is not English, or you need longer inputs or other retrieval methods, you can skip it, because the card points to bge-m3 for those. ### Where to use it Use it from Python, or in tools that read GGUF files. - Python scripts that use the sentence-transformers library - Windows, macOS or Linux, with Python for the download - Langchain, which the card says BGE models are integrated into | Use case | Who | What they get back | | --- | --- | --- | | Semantic search | Developers | An embedding per text, to compare by similarity | | Retrieval for LLMs | App builders | The closest passages for a query | | Re-ranking pipelines | Search engineers | A first-stage retriever that a reranker can refine | ### How to set it up You need Python and only a little disk space. - Python with pip - Room for the file, about 17 MB at 4-bit as an estimate 1. Install the Hugging Face hub package. 2. Download the model files. 3. Read the Usage section of the card for the exact calls. ```sh pip install -U huggingface_hub hf download unsloth/bge-small-en-v1.5-GGUF ``` ### How to use it You give the model English text and take its embedding back. To find related passages, embed your query and your passages, then compare the embeddings for similarity. The card text shown here gives no run command beyond the download. | Tool | What it does | | --- | --- | | bge-m3 | The card's suggestion when you need other languages, longer inputs or extra retrieval methods | | bge-reranker-base | A cross-encoder that re-ranks the top documents an embedding model returns | | bge-reranker-large | The larger cross-encoder for the same re-ranking job | ### Example You embed the question about resetting a password and a few help-center paragraphs, then compare them. The paragraph whose embedding sits closest to the question's embedding is the best match. ### For newcomers and experts An embedding model turns a sentence into a list of numbers, so that sentences with close meanings get close numbers. A good first try is to download the files and compare two short sentences that mean nearly the same thing. This is the v1.5 small model of the BGE family, which the card says eases the spread of similarity scores and retrieves better without an instruction. It has 33.2M parameters and is packaged as GGUF by unsloth. The trade-off is scope, since it covers English only and the card sends longer inputs to bge-m3. English text goes in → The model produces an embedding → Embeddings are compared by similarity → A reranker can refine the top results ### Good to know The model is small and English only. - Language: English - Format: GGUF, with about 17 MB of weights at 4-bit as an estimate - Scope: for other languages, longer inputs or extra retrieval methods, the card points to bge-m3 - License: MIT - Cost to run: none named - Data it reaches: the English text you embed. The card names no account or web service - Not for: other languages, longer inputs or other retrieval methods, per the card ## README ````markdown # bge-small-en-v1.5-GGUF Turns English text into embeddings for search and retrieval, in a small GGUF file of 33.2M parameters. For developers who build search or retrieval apps on modest hardware. Made by unsloth 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: bge-small-en-v1.5-GGUF is a GGUF copy of the small English BGE embedding model, shared by unsloth, with 33.2M parameters. BGE is short for BAAI General Embedding. An embedding model turns text into numbers so that similar passages can be found by comparing them. The card text comes from the FlagEmbedding project. Who it suits: Developers who build search or retrieval features over English text are the main fit, and the small file suits modest hardware. NLP researchers can use it as a compact embedding baseline. ```table Industry | Who uses it | How they use it | Example Search | Software developers | Turn English text into vectors for similarity search | Find notes that relate to a question AI apps | App builders | Retrieve passages to give a language model as context | Fetch the best passages for a chatbot answer Research | NLP researchers | Compare small embedding models | Use it as a compact baseline ``` If your text is not English, or you need longer inputs or other retrieval methods, you can skip it, because the card points to bge-m3 for those. Where to use it: Use it from Python, or in tools that read GGUF files. - Python scripts that use the sentence-transformers library - Windows, macOS or Linux, with Python for the download - Langchain, which the card says BGE models are integrated into ```table Use case | Who | What they get back Semantic search | Developers | An embedding per text, to compare by similarity Retrieval for LLMs | App builders | The closest passages for a query Re-ranking pipelines | Search engineers | A first-stage retriever that a reranker can refine ``` How to set it up: You need Python and only a little disk space. - Python with pip - Room for the file, about 17 MB at 4-bit as an estimate 1. Install the Hugging Face hub package. 2. Download the model files. 3. Read the Usage section of the card for the exact calls. ```sh pip install -U huggingface_hub hf download unsloth/bge-small-en-v1.5-GGUF ``` How to use it: You give the model English text and take its embedding back. To find related passages, embed your query and your passages, then compare the embeddings for similarity. The card text shown here gives no run command beyond the download. ```table Tool | What it does bge-m3 | The card's suggestion when you need other languages, longer inputs or extra retrieval methods bge-reranker-base | A cross-encoder that re-ranks the top documents an embedding model returns bge-reranker-large | The larger cross-encoder for the same re-ranking job ``` Example: You embed the question about resetting a password and a few help-center paragraphs, then compare them. The paragraph whose embedding sits closest to the question's embedding is the best match. For newcomers and experts: An embedding model turns a sentence into a list of numbers, so that sentences with close meanings get close numbers. A good first try is to download the files and compare two short sentences that mean nearly the same thing. This is the v1.5 small model of the BGE family, which the card says eases the spread of similarity scores and retrieves better without an instruction. It has 33.2M parameters and is packaged as GGUF by unsloth. The trade-off is scope, since it covers English only and the card sends longer inputs to bge-m3. ```flow English text goes in The model produces an embedding Embeddings are compared by similarity A reranker can refine the top results ``` Good to know: The model is small and English only. - Language: English - Format: GGUF, with about 17 MB of weights at 4-bit as an estimate - Scope: for other languages, longer inputs or extra retrieval methods, the card points to bge-m3 - License: MIT - Cost to run: none named - Data it reaches: the English text you embed. The card names no account or web service - Not for: other languages, longer inputs or other retrieval methods, per the card ## On Hugging Face, 13 November 2026 - Task: feature extraction - Library: sentence-transformers - Parameters: 33.2M - Weights: about 17 MB at 4-bit (estimate) - Formats: GGUF - Downloads in the last 30 days: 165,239 - Likes: 10 - License: mit - Languages: en - Tags: sentence-transformers, gguf, feature-extraction, sentence-similarity, transformers, mteb, model-index ## Links - Model: https://huggingface.co/unsloth/bge-small-en-v1.5-GGUF - Files: https://huggingface.co/unsloth/bge-small-en-v1.5-GGUF/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download unsloth/bge-small-en-v1.5-GGUF ``` 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 [unsloth/bge-small-en-v1.5-GGUF on Hugging Face](https://huggingface.co/unsloth/bge-small-en-v1.5-GGUF) ## 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 | | --- | --- | --- | --- | --- | --- | | bge-small-en-v1.5-GGUF (this page) | 165,239 | 10 | 33.2M | mit | 2026-06-09 | | [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 | | [multilingual-e5-large](https://mindket.com/p/e1015ff4-2edc-4835-a960-7568a3998a22) | 7,120,555 | 1,284 | 560M | mit | 2026-04-02 | | [pplx-embed-v2-late-0.6b](https://mindket.com/p/84bcea19-0097-475e-b7a0-084d5f736714) | 2,765 | 71 | 594M | mit | 2026-10-22 | 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: 2026-06-09 - First published: 2026-06-09 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open unsloth/bge-small-en-v1.5-GGUF on Hugging Face](https://huggingface.co/unsloth/bge-small-en-v1.5-GGUF) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/0eeb6b56-65ff-469a-9156-62dec1246a55) - [Its page on Mindket](https://mindket.com/p/0eeb6b56-65ff-469a-9156-62dec1246a55)