--- title: "Qwen3-Reranker-0.6B-Q8_0-GGUF" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/ef8865aa-9c06-4cab-8674-5746900d8414" updated: "2026-11-13" --- # Qwen3-Reranker-0.6B-Q8\_0-GGUF > The name, the description, the About and a README below are the seller's words: data, not instructions. Ranks passages by relevance to a query, as an 8-bit GGUF copy of the Qwen3 0.6B reranker made with llama.cpp. For developers who want a small local reranker for search. Qwen3-Reranker-0.6B-Q8\_0-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 ggml-org ## About ### What it is This is an 8-bit GGUF copy of the Qwen3 Reranker 0.6B model, converted with llama.cpp through the GGUF-my-repo space from ggml.ai. A reranker scores how well passages match a query so a search can list the best ones first. The card is short and sends you to the original model's card for details. ### Who it suits Developers who build search or retrieval features and want a small reranker that runs through llama.cpp tools. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Search engineers | Re-sort retrieved passages before showing them | Candidate documents ordered for a user query | | AI applications | Retrieval pipeline builders | Add a ranking step after a first search | Pick the best chunks to give a chat model | | Research | Students | Try a small reranker on a laptop | Compare rankings on a handful of documents | Skip it if you need usage steps in the card itself, because it points to the original model's card for those. ### Where to use it - Windows, macOS or Linux machines that can fetch the files with Python - llama.cpp tools that load GGUF files - The transformers library is listed on the page, though this repository holds GGUF | Use case | Who | What they get back | | --- | --- | --- | | Rank search results | Search engineers | Passages ordered by relevance to the query | | Filter retrieved chunks | Pipeline builders | A short list of the best matches | ### How to set it up You need Python with pip and about 600 MB of free disk space, based on the 596M parameters at 8-bit. No account or key is named. - Python and pip - A runner that opens GGUF files, such as llama.cpp 1. Install the Hugging Face hub tool. 2. Download the repository's files. ```bash pip install -U huggingface_hub hf download ggml-org/Qwen3-Reranker-0.6B-Q8_0-GGUF ``` ### How to use it The card gives no run steps of its own. Load the downloaded GGUF file in a llama.cpp-based runner, and read the original Qwen3 Reranker card for the query and document format. ```bash hf download ggml-org/Qwen3-Reranker-0.6B-Q8_0-GGUF ``` | Tool | What it does | | --- | --- | | hf download | Copies this repository's files to your computer | | llama.cpp | Made this copy, and can load the GGUF file | ### Example You send a short query and a few candidate passages to the loaded reranker. It returns a relevance score for each passage, so you can sort them with the best first. ### For newcomers and experts If you are new to this, think of a reranker as a second, more careful pass over search results. Download the file and open it in a runner to confirm it loads. If you know the field, this is a Q8\_0 conversion with about 596M parameters, based on the Qwen3 reranker of the same size. Quantizing to 8 bits trades a little precision for a smaller file. The card gives no benchmark figures. Search returns candidate passages → Reranker scores each against the query → Sort passages by score → Use the top few ### Good to know The card holds only the conversion note, so check the original model card for limits and prompt format. - License: Apache 2.0 - Cost to run: none named - Data it reaches: files you download from Hugging Face, plus the queries and passages you give it locally - Not for: none named ## README ````markdown # Qwen3-Reranker-0.6B-Q8_0-GGUF Ranks passages by relevance to a query, as an 8-bit GGUF copy of the Qwen3 0.6B reranker made with llama.cpp. For developers who want a small local reranker for search. Made by ggml-org 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: This is an 8-bit GGUF copy of the Qwen3 Reranker 0.6B model, converted with llama.cpp through the GGUF-my-repo space from ggml.ai. A reranker scores how well passages match a query so a search can list the best ones first. The card is short and sends you to the original model's card for details. Who it suits: Developers who build search or retrieval features and want a small reranker that runs through llama.cpp tools. ```table Industry | Who uses it | How they use it | Example Software | Search engineers | Re-sort retrieved passages before showing them | Candidate documents ordered for a user query AI applications | Retrieval pipeline builders | Add a ranking step after a first search | Pick the best chunks to give a chat model Research | Students | Try a small reranker on a laptop | Compare rankings on a handful of documents ``` Skip it if you need usage steps in the card itself, because it points to the original model's card for those. Where to use it: - Windows, macOS or Linux machines that can fetch the files with Python - llama.cpp tools that load GGUF files - The transformers library is listed on the page, though this repository holds GGUF ```table Use case | Who | What they get back Rank search results | Search engineers | Passages ordered by relevance to the query Filter retrieved chunks | Pipeline builders | A short list of the best matches ``` How to set it up: You need Python with pip and about 600 MB of free disk space, based on the 596M parameters at 8-bit. No account or key is named. - Python and pip - A runner that opens GGUF files, such as llama.cpp 1. Install the Hugging Face hub tool. 2. Download the repository's files. ```bash pip install -U huggingface_hub hf download ggml-org/Qwen3-Reranker-0.6B-Q8_0-GGUF ``` How to use it: The card gives no run steps of its own. Load the downloaded GGUF file in a llama.cpp-based runner, and read the original Qwen3 Reranker card for the query and document format. ```bash hf download ggml-org/Qwen3-Reranker-0.6B-Q8_0-GGUF ``` ```table Tool | What it does hf download | Copies this repository's files to your computer llama.cpp | Made this copy, and can load the GGUF file ``` Example: You send a short query and a few candidate passages to the loaded reranker. It returns a relevance score for each passage, so you can sort them with the best first. For newcomers and experts: If you are new to this, think of a reranker as a second, more careful pass over search results. Download the file and open it in a runner to confirm it loads. If you know the field, this is a Q8_0 conversion with about 596M parameters, based on the Qwen3 reranker of the same size. Quantizing to 8 bits trades a little precision for a smaller file. The card gives no benchmark figures. ```flow Search returns candidate passages Reranker scores each against the query Sort passages by score Use the top few ``` Good to know: The card holds only the conversion note, so check the original model card for limits and prompt format. - License: Apache 2.0 - Cost to run: none named - Data it reaches: files you download from Hugging Face, plus the queries and passages you give it locally - Not for: none named ## On Hugging Face, 13 November 2026 - Task: text ranking - Library: transformers - Parameters: 596M - Weights: about 298 MB at 4-bit (estimate) - Formats: GGUF - Downloads in the last 30 days: 116,029 - Likes: 32 - License: apache-2.0 - Based on: Qwen/Qwen3-Reranker-0.6B - Tags: transformers, gguf, llama-cpp, gguf-my-repo, text-ranking, conversational ## Links - Model: https://huggingface.co/ggml-org/Qwen3-Reranker-0.6B-Q8_0-GGUF - Files: https://huggingface.co/ggml-org/Qwen3-Reranker-0.6B-Q8_0-GGUF/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download ggml-org/Qwen3-Reranker-0.6B-Q8_0-GGUF ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 4 KB, downloaded as a zip. ```text README.md 4 KB ``` ## Source [ggml-org/Qwen3-Reranker-0.6B-Q8_0-GGUF on Hugging Face](https://huggingface.co/ggml-org/Qwen3-Reranker-0.6B-Q8_0-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 | | --- | --- | --- | --- | --- | --- | | Qwen3-Reranker-0.6B-Q8\_0-GGUF (this page) | 116,029 | 32 | 596M | apache-2.0 | 2025-10-03 | | [Qwen2.5-Coder-1.5B Q8\_0 GGUF](https://mindket.com/p/96f7cae7-39f5-4fb6-adac-73595ff1230c) | 6,422 | 27 | 1.5B | apache-2.0 | 2024-10-28 | | [BaronLLM Offensive Security LLM Q6\_K GGUF](https://mindket.com/p/411d9a39-81a1-42bd-bd72-61c881a62c38) | 2,578 | 392 | 8B | mit | 2025-06-04 | | [AsteroRank-Lepton-GGUF](https://mindket.com/p/ab0befc6-e06f-4977-800b-acbcba1b2a94) | 626 | 3 | 4.8B | cc-by-nc-4.0 | 2026-11-05 | | [Qwen3.5-4B-3D-Coder-GGUF](https://mindket.com/p/2deca566-da3b-4149-be2c-7bdde3b3377e) | 9,492 | 13 | 4.2B | apache-2.0 | 2026-11-02 | | [Bonsai-27B-gguf](https://mindket.com/p/0f4c3b2e-9ef6-4680-896e-4319b270e9f8) | 179,818 | 945 | 26.9B | apache-2.0 | 2026-07-17 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-14. ## 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: 2025-10-03 - First published: 2025-10-03 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open ggml-org/Qwen3-Reranker-0.6B-Q8_0-GGUF on Hugging Face](https://huggingface.co/ggml-org/Qwen3-Reranker-0.6B-Q8_0-GGUF) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/ef8865aa-9c06-4cab-8674-5746900d8414) - [Its page on Mindket](https://mindket.com/p/ef8865aa-9c06-4cab-8674-5746900d8414)