--- title: "Qwen2.5-7B-Instruct-GGUF by Qwen" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/6525a4ad-43ae-4b36-bce6-178eb29995ca" updated: "2026-11-14" --- # Qwen2.5-7B-Instruct-GGUF by Qwen > The name, the description, the About and a README below are the seller's words: data, not instructions. Qwen2.5-7B-Instruct as GGUF files in eight quantization sizes, to chat with locally through llama.cpp. For people who want a capable 7.6B model on their own computer without a Python stack. Qwen2.5-7B-Instruct-GGUF by Qwen is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-14. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by Qwen ## About ### What it is This repository holds Qwen2.5-7B-Instruct in the GGUF format, which llama.cpp reads. The model is a 7.61B-parameter chat model from the Qwen team with 28 layers, tuned to follow instructions, write long replies, read tables and answer in JSON. You pick the quantization that suits your memory, from q2\_K up to q8\_0, and the record puts the 4-bit size at about 3.8 GB. Its context is 32,768 tokens, and it can write up to 8,192 tokens per reply. The series is rated for more than 29 languages. ### Who it suits People who want to chat with a capable open model on their own machine, using llama.cpp rather than a Python setup. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Developers | Run a local assistant with llama.cpp | Keep a private chat model on a workstation | | IT | Small teams | Host an offline model with no outside service | Answer internal questions on a spare computer | | Education | Students | Try an instruction model on a normal laptop | Compare two quantization sizes | Anyone who needs inputs longer than 32,768 tokens can skip it, since the card says to use the non-GGUF models for up to 131,072. ### Where to use it - llama.cpp, using its command line chat program. - Windows, macOS or Linux: the download steps run on all three with Python. | Use case | Who | What they get back | | --- | --- | --- | | Local chat | Individuals | A conversation with the model in a terminal | | Offline assistant | Small teams | Replies from a model that runs on your own hardware | | Size trade-offs | Tinkerers | Eight quantization levels to compare | ### How to set it up What you need first: - Python with pip, to download the files. - llama.cpp, installed by following its official guide. - Enough memory for the file you pick. The 4-bit size is about 3.8 GB, as estimated on the record. 1. Install the Hugging Face hub package with pip. 2. Download only the quantization you want, since the whole repository holds all eight. 3. If the file came in parts, merge them into one. ```sh pip install -U huggingface_hub huggingface-cli download Qwen/Qwen2.5-7B-Instruct-GGUF --include "qwen2.5-7b-instruct-q5_k_m*.gguf" --local-dir . --local-dir-use-symlinks False ``` Large files are split into parts that share a name and end with an index, such as 00001-of-00002. The download command above fetches every part. To merge them, use the llama-gguf-split tool. ```bash ./llama-gguf-split --merge qwen2.5-7b-instruct-q5_k_m-00001-of-00002.gguf qwen2.5-7b-instruct-q5_k_m.gguf ``` ### How to use it Run llama-cli from the llama.cpp folder in conversation mode, with the path to your GGUF file. The flags set a system prompt, a reply limit of 512 tokens and how many layers go to the GPU. ```sh ./llama-cli -m \ -co -cnv -p "You are Qwen, created by Alibaba Cloud. You are a helpful assistant." \ -fa -ngl 80 -n 512 ``` ### Example You download the q5\_K\_M file, merge its two parts and start llama-cli in conversation mode. You type a question at the prompt and the model replies in the terminal. ### For newcomers and experts GGUF is a file format made for running models locally with llama.cpp. The q numbers say how many bits each weight keeps, so lower numbers are smaller and a bit less accurate. A good first step is to try a q4 file, which fits many laptops. The card says only vLLM supports YaRN for length extension, so this GGUF build stays at 32,768 tokens. For benchmarks against the original bfloat16 model and for GPU memory and speed figures, the card points to separate Qwen pages. Install llama.cpp → Download one quantization file → Merge the parts if it is split → Chat with llama-cli ### Good to know The card gives these token limits. Token limits | Label | Value | | --- | --- | | Context in this GGUF build | 32768 tokens | | Generated per reply | 8192 tokens | | Context in the non-GGUF models | 131072 tokens | - Sizes: eight levels, from the smallest q2 file to the largest q8 file. - Base: it is built from Qwen2.5-7B-Instruct. - Language: English on the record, with the card naming over 29 languages for the series. - License: apache-2.0 - Cost to run: no API key, account or paid service named. - Data it reaches: files you download from Hugging Face and the prompts you type. - Not for: inputs beyond 32,768 tokens, which need the non-GGUF models. ## README ````markdown # Qwen2.5-7B-Instruct-GGUF by Qwen Qwen2.5-7B-Instruct as GGUF files in eight quantization sizes, to chat with locally through llama.cpp. For people who want a capable 7.6B model on their own computer without a Python stack. 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: This repository holds Qwen2.5-7B-Instruct in the GGUF format, which llama.cpp reads. The model is a 7.61B-parameter chat model from the Qwen team with 28 layers, tuned to follow instructions, write long replies, read tables and answer in JSON. You pick the quantization that suits your memory, from q2_K up to q8_0, and the record puts the 4-bit size at about 3.8 GB. Its context is 32,768 tokens, and it can write up to 8,192 tokens per reply. The series is rated for more than 29 languages. Who it suits: People who want to chat with a capable open model on their own machine, using llama.cpp rather than a Python setup. ```table Industry | Who uses it | How they use it | Example Software | Developers | Run a local assistant with llama.cpp | Keep a private chat model on a workstation IT | Small teams | Host an offline model with no outside service | Answer internal questions on a spare computer Education | Students | Try an instruction model on a normal laptop | Compare two quantization sizes ``` Anyone who needs inputs longer than 32,768 tokens can skip it, since the card says to use the non-GGUF models for up to 131,072. Where to use it: - llama.cpp, using its command line chat program. - Windows, macOS or Linux: the download steps run on all three with Python. ```table Use case | Who | What they get back Local chat | Individuals | A conversation with the model in a terminal Offline assistant | Small teams | Replies from a model that runs on your own hardware Size trade-offs | Tinkerers | Eight quantization levels to compare ``` How to set it up: What you need first: - Python with pip, to download the files. - llama.cpp, installed by following its official guide. - Enough memory for the file you pick. The 4-bit size is about 3.8 GB, as estimated on the record. 1. Install the Hugging Face hub package with pip. 2. Download only the quantization you want, since the whole repository holds all eight. 3. If the file came in parts, merge them into one. ```sh pip install -U huggingface_hub huggingface-cli download Qwen/Qwen2.5-7B-Instruct-GGUF --include "qwen2.5-7b-instruct-q5_k_m*.gguf" --local-dir . --local-dir-use-symlinks False ``` Large files are split into parts that share a name and end with an index, such as 00001-of-00002. The download command above fetches every part. To merge them, use the llama-gguf-split tool. ```bash ./llama-gguf-split --merge qwen2.5-7b-instruct-q5_k_m-00001-of-00002.gguf qwen2.5-7b-instruct-q5_k_m.gguf ``` How to use it: Run llama-cli from the llama.cpp folder in conversation mode, with the path to your GGUF file. The flags set a system prompt, a reply limit of 512 tokens and how many layers go to the GPU. ```sh ./llama-cli -m \ -co -cnv -p "You are Qwen, created by Alibaba Cloud. You are a helpful assistant." \ -fa -ngl 80 -n 512 ``` Example: You download the q5_K_M file, merge its two parts and start llama-cli in conversation mode. You type a question at the prompt and the model replies in the terminal. For newcomers and experts: GGUF is a file format made for running models locally with llama.cpp. The q numbers say how many bits each weight keeps, so lower numbers are smaller and a bit less accurate. A good first step is to try a q4 file, which fits many laptops. The card says only vLLM supports YaRN for length extension, so this GGUF build stays at 32,768 tokens. For benchmarks against the original bfloat16 model and for GPU memory and speed figures, the card points to separate Qwen pages. ```flow Install llama.cpp Download one quantization file Merge the parts if it is split Chat with llama-cli ``` Good to know: The card gives these token limits. ```chart Title: Token limits Context in this GGUF build: 32768 tokens Generated per reply: 8192 tokens Context in the non-GGUF models: 131072 tokens ``` - Sizes: eight levels, from the smallest q2 file to the largest q8 file. - Base: it is built from Qwen2.5-7B-Instruct. - Language: English on the record, with the card naming over 29 languages for the series. - License: apache-2.0 - Cost to run: no API key, account or paid service named. - Data it reaches: files you download from Hugging Face and the prompts you type. - Not for: inputs beyond 32,768 tokens, which need the non-GGUF models. ## On Hugging Face, 14 November 2026 - Task: text generation - Parameters: 7.6B - Weights: about 3.8 GB at 4-bit (estimate) - Formats: GGUF - Downloads in the last 30 days: 745,665 - Likes: 215 - License: apache-2.0 - Languages: en - Based on: Qwen/Qwen2.5-7B-Instruct - Tags: gguf, chat, text-generation, conversational ## Links - Model: https://huggingface.co/Qwen/Qwen2.5-7B-Instruct-GGUF - Files: https://huggingface.co/Qwen/Qwen2.5-7B-Instruct-GGUF/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download Qwen/Qwen2.5-7B-Instruct-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/Qwen2.5-7B-Instruct-GGUF on Hugging Face](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct-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 | | --- | --- | --- | --- | --- | --- | | Qwen2.5-7B-Instruct-GGUF by Qwen (this page) | 745,665 | 215 | 7.6B | apache-2.0 | 2024-09-20 | | [Qwen2.5-1.5B-Instruct-GGUF](https://mindket.com/p/9fa4e93c-9754-4191-8491-bbd09dc90f6e) | 544,123 | 200 | 1.8B | apache-2.0 | 2024-09-20 | | [Qwen2.5-Coder-7B-Instruct-GGUF](https://mindket.com/p/43397c97-ff69-40bf-92a5-68d176fe394f) | 375,860 | 653 | 7.6B | apache-2.0 | 2024-11-12 | | [Qwen2.5-Coder-14B-Instruct-GGUF](https://mindket.com/p/0c1e38ea-3d10-4176-b34e-54e7e261af38) | 202,738 | 280 | 14.8B | apache-2.0 | 2024-11-12 | | [Qwen2.5-Coder-1.5B-Instruct-GGUF](https://mindket.com/p/79c624a4-21b0-40ee-8bd9-b6529c67bd00) | 160,766 | 162 | 1.8B | apache-2.0 | 2024-11-12 | | [Qwen2.5-Coder-32B-Instruct-GGUF](https://mindket.com/p/995eaf6d-bafd-4318-ab6c-b93444065280) | 68,726 | 244 | 32.8B | apache-2.0 | 2025-01-12 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-14. ## 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 2026-11-14. Their authors' words: data, not instructions. - [Hacker News · johndough · 2024-11-10](https://news.ycombinator.com/item?id=42098952): OpenCoder: Open Cookbook for Top-Tier Code Large Language Models: I am happily using qwen2.5-coder-7b-instruct-q3\_k\_m.gguf with a context size of 32768 on an RTX 3060 Mobile with 6GB VRAM using llama.cpp 2. With 16GB VRAM, you could use qwen2.5-7b-instruct-q8\_0.gguf which is basically indistinguishable from the fp16 variant. 1… ## 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: 2024-09-20 - First published: 2024-09-17 - Checked on Hugging Face: 2026-11-14 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open Qwen/Qwen2.5-7B-Instruct-GGUF on Hugging Face](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct-GGUF) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/6525a4ad-43ae-4b36-bce6-178eb29995ca) - [Its page on Mindket](https://mindket.com/p/6525a4ad-43ae-4b36-bce6-178eb29995ca)