--- title: "Qwen2.5-1.5B-Instruct-GGUF" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/9fa4e93c-9754-4191-8491-bbd09dc90f6e" updated: "2026-11-12" --- # Qwen2.5-1.5B-Instruct-GGUF > The name, the description, the About and a README below are the seller's words: data, not instructions. Gives you the small Qwen2.5 1.5B chat model as GGUF files in eight quantization sizes, for running with llama.cpp on a laptop or modest GPU. For people who want a light local assistant. Qwen2.5-1.5B-Instruct-GGUF 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 This repository holds the instruction-tuned Qwen2.5 1.5B model in GGUF format, made for llama.cpp. You chat with it on your own computer, with no hosted service in between. The files come in eight quantization types, from q2\_K up to q8\_0, so you can trade size against quality. The model has 1.54B parameters and a context of 32,768 tokens, with generation of up to 8,192. ### Who it suits It suits people who want a light chat model on their own machine, for drafting, simple code help and structured answers, and who are comfortable with a command line. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Local AI | Enthusiasts | Chat with a small model offline | Ask for a short explanation | | Software | Developers | Test a light model inside a local tool | Draft a small script | | Education | Students | Practice prompts on modest hardware | Rewrite a paragraph | | Research | ML engineers | Compare quant sizes | Check q4 against q8 | If you need the strongest answers or extremely long documents, you can skip it, since larger Qwen2.5 models exist. ### Where to use it The files run in llama.cpp on Windows, macOS or Linux. The card suggests the conversation mode for a chatbot-style experience. - llama-cli from llama.cpp - A laptop CPU or a GPU with layers offloaded - Any tool that loads GGUF files | Use case | Who | What they get back | | --- | --- | --- | | Chatbot-style chat | Everyday users | Replies in a running conversation | | Light drafting | Writers | Short text drafts | | Size testing | Engineers | Quality across the quant files | ### How to set it up You need Python with pip to fetch a file and a working llama.cpp. The card says to clone llama.cpp and install it by its guide, then download the one GGUF file you need. - Python with pip - llama.cpp installed - A few hundred MB to a few GB of disk space, by quant 1. Install the Hugging Face hub package. 2. Download the GGUF file you want, instead of the whole repository. 3. Run it from the llama.cpp folder. ```sh pip install -U huggingface_hub ``` ```sh huggingface-cli download Qwen/Qwen2.5-1.5B-Instruct-GGUF qwen2.5-1.5b-instruct-q5_k_m.gguf --local-dir . --local-dir-use-symlinks False ``` ### How to use it You start llama-cli in conversation mode with the path to your GGUF file, then type to the model. The command also sets the assistant's role, the GPU layers and the reply length. ```sh ./llama-cli -m \ -co -cnv -p "You are Qwen, created by Alibaba Cloud. You are a helpful assistant." \ -fa -ngl 80 -n 512 ``` | Flag | What it does | | --- | --- | | -m | Points to your GGUF file | | -cnv | Starts conversation mode | | -p | Sets the system prompt | | -ngl | Sets how many layers go to the GPU | | -n | Limits the reply length in tokens | ### Example You start the command with the q5\_k\_m file and type a short question in the chat. The model answers in the terminal, and you can keep the conversation going. ### For newcomers and experts If you are new, download the q5\_k\_m file from the example, run the command and say hello. Then try a smaller or larger quant to see the change. If you know the field, the model is a transformer that uses rotary position embeddings, SwiGLU and RMSNorm, with a bias on the attention projections and tied word embeddings. It has 28 layers and grouped-query attention with 12 query heads and 2 key-value heads. Download one GGUF file → Run llama-cli in conversation mode → Type a message → Read the reply ### Good to know The quant sizes run from a 2-bit q2\_K up to an 8-bit q8\_0, with q3, q4, q5 and q6 options between them. The card points to a blog post with benchmark results, including quantized against bfloat16. - License: apache-2.0 - Cost to run: none named - Data it reaches: files you download, and the messages you type - Not for: users who need the largest Qwen2.5 models, since this is the 1.5B size ## README ````markdown # Qwen2.5-1.5B-Instruct-GGUF Gives you the small Qwen2.5 1.5B chat model as GGUF files in eight quantization sizes, for running with llama.cpp on a laptop or modest GPU. For people who want a light local assistant. 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 the instruction-tuned Qwen2.5 1.5B model in GGUF format, made for llama.cpp. You chat with it on your own computer, with no hosted service in between. The files come in eight quantization types, from q2_K up to q8_0, so you can trade size against quality. The model has 1.54B parameters and a context of 32,768 tokens, with generation of up to 8,192. Who it suits: It suits people who want a light chat model on their own machine, for drafting, simple code help and structured answers, and who are comfortable with a command line. ```table Industry | Who uses it | How they use it | Example Local AI | Enthusiasts | Chat with a small model offline | Ask for a short explanation Software | Developers | Test a light model inside a local tool | Draft a small script Education | Students | Practice prompts on modest hardware | Rewrite a paragraph Research | ML engineers | Compare quant sizes | Check q4 against q8 ``` If you need the strongest answers or extremely long documents, you can skip it, since larger Qwen2.5 models exist. Where to use it: The files run in llama.cpp on Windows, macOS or Linux. The card suggests the conversation mode for a chatbot-style experience. - llama-cli from llama.cpp - A laptop CPU or a GPU with layers offloaded - Any tool that loads GGUF files ```table Use case | Who | What they get back Chatbot-style chat | Everyday users | Replies in a running conversation Light drafting | Writers | Short text drafts Size testing | Engineers | Quality across the quant files ``` How to set it up: You need Python with pip to fetch a file and a working llama.cpp. The card says to clone llama.cpp and install it by its guide, then download the one GGUF file you need. - Python with pip - llama.cpp installed - A few hundred MB to a few GB of disk space, by quant 1. Install the Hugging Face hub package. 2. Download the GGUF file you want, instead of the whole repository. 3. Run it from the llama.cpp folder. ```sh pip install -U huggingface_hub ``` ```sh huggingface-cli download Qwen/Qwen2.5-1.5B-Instruct-GGUF qwen2.5-1.5b-instruct-q5_k_m.gguf --local-dir . --local-dir-use-symlinks False ``` How to use it: You start llama-cli in conversation mode with the path to your GGUF file, then type to the model. The command also sets the assistant's role, the GPU layers and the reply length. ```sh ./llama-cli -m \ -co -cnv -p "You are Qwen, created by Alibaba Cloud. You are a helpful assistant." \ -fa -ngl 80 -n 512 ``` ```table Flag | What it does -m | Points to your GGUF file -cnv | Starts conversation mode -p | Sets the system prompt -ngl | Sets how many layers go to the GPU -n | Limits the reply length in tokens ``` Example: You start the command with the q5_k_m file and type a short question in the chat. The model answers in the terminal, and you can keep the conversation going. For newcomers and experts: If you are new, download the q5_k_m file from the example, run the command and say hello. Then try a smaller or larger quant to see the change. If you know the field, the model is a transformer that uses rotary position embeddings, SwiGLU and RMSNorm, with a bias on the attention projections and tied word embeddings. It has 28 layers and grouped-query attention with 12 query heads and 2 key-value heads. ```flow Download one GGUF file Run llama-cli in conversation mode Type a message Read the reply ``` Good to know: The quant sizes run from a 2-bit q2_K up to an 8-bit q8_0, with q3, q4, q5 and q6 options between them. The card points to a blog post with benchmark results, including quantized against bfloat16. - License: apache-2.0 - Cost to run: none named - Data it reaches: files you download, and the messages you type - Not for: users who need the largest Qwen2.5 models, since this is the 1.5B size ## On Hugging Face, 13 November 2026 - Task: text generation - Parameters: 1.8B - Weights: about 889 MB at 4-bit (estimate) - Formats: GGUF - Downloads in the last 30 days: 544,123 - Likes: 200 - License: apache-2.0 - Languages: en - Based on: Qwen/Qwen2.5-1.5B-Instruct - Quantized versions: 1 - Fine-tunes: 3 - Tags: gguf, chat, text-generation, conversational ## Links - Model: https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct-GGUF - Files: https://huggingface.co/Qwen/Qwen2.5-1.5B-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/Qwen2.5-1.5B-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/Qwen2.5-1.5B-Instruct-GGUF on Hugging Face](https://huggingface.co/Qwen/Qwen2.5-1.5B-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 | | --- | --- | --- | --- | --- | --- | | Qwen2.5-1.5B-Instruct-GGUF (this page) | 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 | | [Hemmingway-1.5 GGUF](https://mindket.com/p/d571ae4f-8657-40b2-9af9-6663f5d953e1) | 13,596 | 17 | 27.3B | cc-by-nc-4.0 | 2026-10-28 | 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 2026-11-13. Their authors' words: data, not instructions. - [Bluesky · @aichina.news · 2026-10-24](https://bsky.app/profile/aichina.news/post/3mynnlztyom2c): 1.5B in a 1–2GB GGUF with an Apache 2.0 licence is a genuinely useful laptop-class build — if the packaging held up. On Modelers.cn it doesn't: empty card, licence tagged "other", no checksums or quant details. Hard to recommend over the upstream Qwen repo until DevQuasar publishes the quants. ## 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: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open Qwen/Qwen2.5-1.5B-Instruct-GGUF on Hugging Face](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct-GGUF) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/9fa4e93c-9754-4191-8491-bbd09dc90f6e) - [Its page on Mindket](https://mindket.com/p/9fa4e93c-9754-4191-8491-bbd09dc90f6e)