--- title: "Qwen3-4B-GGUF" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/b0b21eda-08f1-4a04-8542-863100ad15e3" updated: "2026-11-13" --- # Qwen3-4B-GGUF > The name, the description, the About and a README below are the seller's words: data, not instructions. A 4B chat model in GGUF format that runs locally in llama.cpp or ollama, with a thinking mode for reasoning and a plain mode for quick replies. Suits developers and hobbyists with a typical PC. Qwen3-4B-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 Qwen ## About ### What it is Qwen3-4B-GGUF is a 4 billion parameter chat model from Qwen, packed in GGUF format for local use in llama.cpp or ollama. It has a thinking mode for harder reasoning and a plain mode for fast replies, and you can flip between them from one message to the next. ### Who it suits Developers and hobbyists who want a mid-sized local model that fits on a typical laptop or desktop. It also suits people building small agents, translation helpers or writing assistants that should run without a hosted service. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Developers | Run a local model for chat and code help | Ask for a short function and read it | | Developer tools | Agent builders | Call external tools from a local model | Prototype an agent on one machine | | Localization | Multilingual teams | Translate and follow instructions in many languages | Draft translations for a human check | | Writing | Writers and role-play fans | Use a model tuned for natural multi-turn chat | Keep a long conversation going offline | Anyone who needs the best answers on hard problems can skip it, since larger Qwen3 models exist. ### Where to use it - llama.cpp on Windows, macOS or Linux, through its llama-cli tool - ollama, which starts the model with one command | Use case | Who | What they get back | | --- | --- | --- | | Local chat assistant | Anyone with a PC | Replies generated on your own machine | | Reasoning on request | Students and developers | A thinking section, then the final answer | | Longer documents | Teams with large inputs | Context up to 131,072 tokens once YaRN is on | | Translation | Multilingual users | Text in 100+ languages and dialects | ### How to set it up - Python with pip, if you want the files on disk - llama.cpp installed by following its official guide, or ollama - Memory for the file you choose, since a 4-bit copy is an estimated 2 GB 1. Install the Hugging Face hub package and download the files. 2. Or skip the download and run the model straight from llama.cpp or ollama with the commands below. ```sh pip install -U huggingface_hub hf download Qwen/Qwen3-4B-GGUF ``` ### How to use it Start the model from the llama.cpp folder, or with one ollama command, and chat in the terminal. Write /think or /no\_think in a message to set the thinking mode for that turn. The newest instruction wins in a multi-turn chat. ```sh ./llama-cli -hf Qwen/Qwen3-4B-GGUF:Q8_0 --jinja --color -ngl 99 -fa -sm row --temp 0.6 --top-k 20 --top-p 0.95 --min-p 0 --presence-penalty 1.5 -c 40960 -n 32768 --no-context-shift ``` ```sh ollama run hf.co/Qwen/Qwen3-4B-GGUF:Q8_0 ``` | Tool | What it does | | --- | --- | | llama-cli | Runs the GGUF model in the terminal with the settings you pass | | ollama run | Pulls the model and starts a chat with one command | | /think | Turns thinking mode on for the current and later turns | | /no\_think | Turns thinking mode off for quick replies | For inputs beyond 32,768 tokens in llama.cpp, add the YaRN options. They apply one fixed scaling factor to every input, which may weaken results on shorter texts, so use them only for long contexts. ```sh ./llama-cli ... -c 131072 --rope-scaling yarn --rope-scale 4 --yarn-orig-ctx 32768 ``` ### Example You type "Who are you /no\_think" and the model answers at once that it is Qwen, a language model from Alibaba Cloud, with an empty thinking section. Then you ask "How many 'r's are in 'strawberries'? /think" and it reasons first before saying the word has 3. ### For newcomers and experts If you are new, run the ollama command and ask the same question twice, once with /no\_think and once with /think. The second reply shows its reasoning first, so it takes longer. Experts get a causal language model with 36 layers, 32 query heads and 8 key-value heads (GQA), about 3.6B non-embedding parameters and a native 32,768 token context. The card suggests a presence penalty of 1.5 for quantized copies to cut repetition, and warns against greedy decoding in thinking mode. You start llama-cli or ollama → The GGUF model loads locally → You chat, adding /think or /no\_think → It replies, thinking first when asked ### Good to know - Quantization: five levels are published, from q4\_K\_M up to q8\_0 - Context: 32,768 tokens natively, 131,072 with YaRN - License: Apache 2.0 - Cost to run: none named, no API key or account, only hardware with enough memory - Data it reaches: it downloads model files from Hugging Face, then answers on your own machine - Not for: the source names no forbidden uses, and YaRN may slightly hurt performance on shorter texts ## README ````markdown # Qwen3-4B-GGUF A 4B chat model in GGUF format that runs locally in llama.cpp or ollama, with a thinking mode for reasoning and a plain mode for quick replies. Suits developers and hobbyists with a typical PC. 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: Qwen3-4B-GGUF is a 4 billion parameter chat model from Qwen, packed in GGUF format for local use in llama.cpp or ollama. It has a thinking mode for harder reasoning and a plain mode for fast replies, and you can flip between them from one message to the next. Who it suits: Developers and hobbyists who want a mid-sized local model that fits on a typical laptop or desktop. It also suits people building small agents, translation helpers or writing assistants that should run without a hosted service. ```table Industry | Who uses it | How they use it | Example Software | Developers | Run a local model for chat and code help | Ask for a short function and read it Developer tools | Agent builders | Call external tools from a local model | Prototype an agent on one machine Localization | Multilingual teams | Translate and follow instructions in many languages | Draft translations for a human check Writing | Writers and role-play fans | Use a model tuned for natural multi-turn chat | Keep a long conversation going offline ``` Anyone who needs the best answers on hard problems can skip it, since larger Qwen3 models exist. Where to use it: - llama.cpp on Windows, macOS or Linux, through its llama-cli tool - ollama, which starts the model with one command ```table Use case | Who | What they get back Local chat assistant | Anyone with a PC | Replies generated on your own machine Reasoning on request | Students and developers | A thinking section, then the final answer Longer documents | Teams with large inputs | Context up to 131,072 tokens once YaRN is on Translation | Multilingual users | Text in 100+ languages and dialects ``` How to set it up: - Python with pip, if you want the files on disk - llama.cpp installed by following its official guide, or ollama - Memory for the file you choose, since a 4-bit copy is an estimated 2 GB 1. Install the Hugging Face hub package and download the files. 2. Or skip the download and run the model straight from llama.cpp or ollama with the commands below. ```sh pip install -U huggingface_hub hf download Qwen/Qwen3-4B-GGUF ``` How to use it: Start the model from the llama.cpp folder, or with one ollama command, and chat in the terminal. Write /think or /no_think in a message to set the thinking mode for that turn. The newest instruction wins in a multi-turn chat. ```sh ./llama-cli -hf Qwen/Qwen3-4B-GGUF:Q8_0 --jinja --color -ngl 99 -fa -sm row --temp 0.6 --top-k 20 --top-p 0.95 --min-p 0 --presence-penalty 1.5 -c 40960 -n 32768 --no-context-shift ``` ```sh ollama run hf.co/Qwen/Qwen3-4B-GGUF:Q8_0 ``` ```table Tool | What it does llama-cli | Runs the GGUF model in the terminal with the settings you pass ollama run | Pulls the model and starts a chat with one command /think | Turns thinking mode on for the current and later turns /no_think | Turns thinking mode off for quick replies ``` For inputs beyond 32,768 tokens in llama.cpp, add the YaRN options. They apply one fixed scaling factor to every input, which may weaken results on shorter texts, so use them only for long contexts. ```sh ./llama-cli ... -c 131072 --rope-scaling yarn --rope-scale 4 --yarn-orig-ctx 32768 ``` Example: You type "Who are you /no_think" and the model answers at once that it is Qwen, a language model from Alibaba Cloud, with an empty thinking section. Then you ask "How many 'r's are in 'strawberries'? /think" and it reasons first before saying the word has 3. For newcomers and experts: If you are new, run the ollama command and ask the same question twice, once with /no_think and once with /think. The second reply shows its reasoning first, so it takes longer. Experts get a causal language model with 36 layers, 32 query heads and 8 key-value heads (GQA), about 3.6B non-embedding parameters and a native 32,768 token context. The card suggests a presence penalty of 1.5 for quantized copies to cut repetition, and warns against greedy decoding in thinking mode. ```flow You start llama-cli or ollama The GGUF model loads locally You chat, adding /think or /no_think It replies, thinking first when asked ``` Good to know: - Quantization: five levels are published, from q4_K_M up to q8_0 - Context: 32,768 tokens natively, 131,072 with YaRN - License: Apache 2.0 - Cost to run: none named, no API key or account, only hardware with enough memory - Data it reaches: it downloads model files from Hugging Face, then answers on your own machine - Not for: the source names no forbidden uses, and YaRN may slightly hurt performance on shorter texts ## On Hugging Face, 13 November 2026 - Task: text generation - Parameters: 4B - Weights: about 2 GB at 4-bit (estimate) - Formats: GGUF - Downloads in the last 30 days: 745,360 - Likes: 224 - License: apache-2.0 - Based on: Qwen/Qwen3-4B - Quantized versions: 1 - Tags: gguf, text-generation, conversational ## Links - Model: https://huggingface.co/Qwen/Qwen3-4B-GGUF - Files: https://huggingface.co/Qwen/Qwen3-4B-GGUF/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download Qwen/Qwen3-4B-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 [Qwen/Qwen3-4B-GGUF on Hugging Face](https://huggingface.co/Qwen/Qwen3-4B-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 | | --- | --- | --- | --- | --- | --- | | Qwen3-4B-GGUF (this page) | 745,360 | 224 | 4B | apache-2.0 | 2025-05-21 | | [Ornith-1.5-9B-GGUF](https://mindket.com/p/5dd265f6-9a3e-450b-ad2a-b49a78e2700a) | 5,772,425 | 673 | 9.2B | mit | 2026-08-24 | | [Ornith-1.5-35B-A3B-GGUF](https://mindket.com/p/c98822f4-2255-450a-89ce-df15fc0003a1) | 4,164,876 | 603 | 35.5B | mit | 2026-08-24 | | [DeepSeek-V4.1-Flash-1017-GSQ-RCO-GGUF](https://mindket.com/p/0bd5db10-f872-40c7-9371-3225afab2a6d) | 3,635,772 | 655 | 197B | mit | 2026-10-28 | | [Ornith-1.0-9B-GGUF](https://mindket.com/p/80667786-2895-41cb-b1d8-f6f6ae12215d) | 2,357,551 | 702 | 9B | mit | 2026-06-25 | | [Qwen3.8-35B-A3B-Distill-GGUF](https://mindket.com/p/d707b0c8-89b8-4c4f-8d49-2ff67de613c7) | 1,337,097 | 277 | 35.5B | apache-2.0 | 2026-09-16 | 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 · @aichina.news · 2026-10-19](https://bsky.app/profile/aichina.news/post/3myaikutvh62u): 4B Qwen3 GGUF that runs on a laptop is handy for red-teaming and fiction, but the card is empty: no quants, no hashes, no definition of 'unc', licence just 'other'. Hard to see this beating the upstream Qwen3-4B GGUF in production until someone documents what was actually changed. ## 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: 2025-05-21 - First published: 2025-05-05 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open Qwen/Qwen3-4B-GGUF on Hugging Face](https://huggingface.co/Qwen/Qwen3-4B-GGUF) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/b0b21eda-08f1-4a04-8542-863100ad15e3) - [Its page on Mindket](https://mindket.com/p/b0b21eda-08f1-4a04-8542-863100ad15e3)