--- title: "Qwen2.5-Coder-32B-Instruct-GGUF" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/995eaf6d-bafd-4318-ab6c-b93444065280" updated: "2026-11-13" --- # Qwen2.5-Coder-32B-Instruct-GGUF > The name, the description, the About and a README below are the seller's words: data, not instructions. GGUF copy of a 32B code chat model that runs locally with llama.cpp. It writes, explains and fixes code from plain requests, for developers who want to run it on their own machine. Qwen2.5-Coder-32B-Instruct-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 This is the GGUF version of Qwen2.5-Coder-32B-Instruct, a 32 billion parameter chat model tuned for programming. The GGUF format lets you run it on your own machine with llama.cpp. It writes, reasons about and fixes code from plain requests. ### Who it suits Developers who want a large coding model running locally without a hosted service, and tinkerers who already use llama.cpp. Teams can also use it as a local coding backend for their own tools. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Application developers | Chat with a local coding model in the terminal | Ask for a function and read the reply | | Developer tools | Teams building code assistants | Run the model as an offline backend | Point an internal tool at a local copy | | Research | Hobbyists and researchers | Compare quant levels of one model | Try q4\_K\_M against q8\_0 on their own hardware | | Education | Students and teachers | Get code explained without an online account | Paste a snippet and ask what it does | Anyone with a small laptop can skip it, because even the 4-bit weights are an estimated 16.4 GB. ### Where to use it - llama.cpp on Windows, macOS or Linux, using its llama-cli tool in the terminal - Any tool built on llama.cpp that reads GGUF files - Models that take longer inputs than 32,768 tokens need the non-GGUF versions instead | Use case | Who | What they get back | | --- | --- | --- | | Write code from a request | Developers | Generated functions and scripts in the chat | | Fix broken code | Developers | A corrected version with an explanation | | Offline coding help | Teams with strict data rules | Answers produced on their own machine | ### How to set it up - Python with pip, to fetch the files - A copy of llama.cpp installed by following its official guide - Disk and memory for the file you pick, since a 4-bit copy is an estimated 16.4 GB - Commands run from inside the llama.cpp folder 1. Install the Hugging Face hub package. 2. Download the quant you want. Large files come in several parts that share a prefix, and the command fetches all of them. 3. If you got split parts, merge them into one file with llama-gguf-split. ```sh pip install -U huggingface_hub ``` ```sh huggingface-cli download Qwen/Qwen2.5-Coder-32B-Instruct-GGUF --include "qwen2.5-coder-32b-instruct-q5_k_m*.gguf" --local-dir . --local-dir-use-symlinks False ``` ```bash # ./llama-gguf-split --merge ./llama-gguf-split --merge qwen2.5-coder-32b-instruct-q5_k_m-00001-of-00003.gguf qwen2.5-coder-32b-instruct-q5_k_m.gguf ``` ### How to use it Start llama-cli in conversation mode with your GGUF file and a system line. You then type requests at the prompt and read the replies in the terminal. ```sh ./llama-cli -m \ -co -cnv -p "You are Qwen, created by Alibaba Cloud. You are a helpful assistant." \ -fa -ngl 80 -n 512 ``` | Tool | What it does | | --- | --- | | huggingface-cli download | Fetches the GGUF files you pick from Hugging Face | | llama-gguf-split | Merges split GGUF parts into a single file | | llama-cli | Loads the model and opens a chat session in the terminal | ### Example Once the chat is open you type "write a function that reverses a string". The model answers in the terminal with code for that function, and you can keep asking follow-up questions in the same session. ### For newcomers and experts If you are new, think of the file as the model packed for local use. Download one quant, start llama-cli as shown, and ask a small coding question first. A bigger quant gives better quality but needs more memory. Experts get a causal language model with 64 layers, 40 query heads and 8 key-value heads (GQA), about 31.0B non-embedding parameters and a 32,768 token context. Its quant types run from the small q2\_K up to the large q8\_0, with q3, q4, q5 and q6 steps between. Only vLLM supports YaRN length extrapolation, so longer contexts need the non-GGUF models. Pick a quant and download its files → Merge split parts if there are any → llama-cli loads the GGUF file → You chat with the model in the terminal ### Good to know - Quant types: eight levels are published, from q2\_K to q8\_0 - Context: 32,768 tokens in this GGUF version - License: Apache 2.0 - Cost to run: none named, no API key or account, only hardware with enough memory - Data it reaches: it downloads files from Hugging Face, then runs on your own machine - Not for: the source names no forbidden uses, and inputs beyond 32,768 tokens need the non-GGUF models ## README ````markdown # Qwen2.5-Coder-32B-Instruct-GGUF GGUF copy of a 32B code chat model that runs locally with llama.cpp. It writes, explains and fixes code from plain requests, for developers who want to run it on their own machine. 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 is the GGUF version of Qwen2.5-Coder-32B-Instruct, a 32 billion parameter chat model tuned for programming. The GGUF format lets you run it on your own machine with llama.cpp. It writes, reasons about and fixes code from plain requests. Who it suits: Developers who want a large coding model running locally without a hosted service, and tinkerers who already use llama.cpp. Teams can also use it as a local coding backend for their own tools. ```table Industry | Who uses it | How they use it | Example Software | Application developers | Chat with a local coding model in the terminal | Ask for a function and read the reply Developer tools | Teams building code assistants | Run the model as an offline backend | Point an internal tool at a local copy Research | Hobbyists and researchers | Compare quant levels of one model | Try q4_K_M against q8_0 on their own hardware Education | Students and teachers | Get code explained without an online account | Paste a snippet and ask what it does ``` Anyone with a small laptop can skip it, because even the 4-bit weights are an estimated 16.4 GB. Where to use it: - llama.cpp on Windows, macOS or Linux, using its llama-cli tool in the terminal - Any tool built on llama.cpp that reads GGUF files - Models that take longer inputs than 32,768 tokens need the non-GGUF versions instead ```table Use case | Who | What they get back Write code from a request | Developers | Generated functions and scripts in the chat Fix broken code | Developers | A corrected version with an explanation Offline coding help | Teams with strict data rules | Answers produced on their own machine ``` How to set it up: - Python with pip, to fetch the files - A copy of llama.cpp installed by following its official guide - Disk and memory for the file you pick, since a 4-bit copy is an estimated 16.4 GB - Commands run from inside the llama.cpp folder 1. Install the Hugging Face hub package. 2. Download the quant you want. Large files come in several parts that share a prefix, and the command fetches all of them. 3. If you got split parts, merge them into one file with llama-gguf-split. ```sh pip install -U huggingface_hub ``` ```sh huggingface-cli download Qwen/Qwen2.5-Coder-32B-Instruct-GGUF --include "qwen2.5-coder-32b-instruct-q5_k_m*.gguf" --local-dir . --local-dir-use-symlinks False ``` ```bash # ./llama-gguf-split --merge ./llama-gguf-split --merge qwen2.5-coder-32b-instruct-q5_k_m-00001-of-00003.gguf qwen2.5-coder-32b-instruct-q5_k_m.gguf ``` How to use it: Start llama-cli in conversation mode with your GGUF file and a system line. You then type requests at the prompt and read the replies in the terminal. ```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 Tool | What it does huggingface-cli download | Fetches the GGUF files you pick from Hugging Face llama-gguf-split | Merges split GGUF parts into a single file llama-cli | Loads the model and opens a chat session in the terminal ``` Example: Once the chat is open you type "write a function that reverses a string". The model answers in the terminal with code for that function, and you can keep asking follow-up questions in the same session. For newcomers and experts: If you are new, think of the file as the model packed for local use. Download one quant, start llama-cli as shown, and ask a small coding question first. A bigger quant gives better quality but needs more memory. Experts get a causal language model with 64 layers, 40 query heads and 8 key-value heads (GQA), about 31.0B non-embedding parameters and a 32,768 token context. Its quant types run from the small q2_K up to the large q8_0, with q3, q4, q5 and q6 steps between. Only vLLM supports YaRN length extrapolation, so longer contexts need the non-GGUF models. ```flow Pick a quant and download its files Merge split parts if there are any llama-cli loads the GGUF file You chat with the model in the terminal ``` Good to know: - Quant types: eight levels are published, from q2_K to q8_0 - Context: 32,768 tokens in this GGUF version - License: Apache 2.0 - Cost to run: none named, no API key or account, only hardware with enough memory - Data it reaches: it downloads files from Hugging Face, then runs on your own machine - Not for: the source names no forbidden uses, and inputs beyond 32,768 tokens need the non-GGUF models ## On Hugging Face, 13 November 2026 - Task: text generation - Library: transformers - Parameters: 32.8B - Weights: about 16.4 GB at 4-bit (estimate) - Formats: GGUF - Downloads in the last 30 days: 68,726 - Likes: 244 - License: apache-2.0 - Languages: en - Based on: Qwen/Qwen2.5-Coder-32B-Instruct - Quantized versions: 1 - Fine-tunes: 2 - Tags: transformers, gguf, code, codeqwen, chat, qwen, qwen-coder, text-generation, conversational ## Links - Model: https://huggingface.co/Qwen/Qwen2.5-Coder-32B-Instruct-GGUF - Files: https://huggingface.co/Qwen/Qwen2.5-Coder-32B-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-Coder-32B-Instruct-GGUF ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 6 KB, downloaded as a zip. ```text README.md 6 KB ``` ## Source [Qwen/Qwen2.5-Coder-32B-Instruct-GGUF on Hugging Face](https://huggingface.co/Qwen/Qwen2.5-Coder-32B-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-Coder-32B-Instruct-GGUF (this page) | 68,726 | 244 | 32.8B | apache-2.0 | 2025-01-12 | | [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-14B-Instruct](https://mindket.com/p/bf4cb9a2-fb0b-4f3c-965b-76d693eec384) | 1,860,327 | 214 | 14.8B | apache-2.0 | 2025-01-12 | | [Qwen2.5-Coder-7B-Instruct](https://mindket.com/p/26690bc4-bc2a-4e48-8691-e2d1eb40c60e) | 1,505,999 | 872 | 7.6B | apache-2.0 | 2025-01-12 | Numbers as Mindket last read them, from 2026-11-12 to 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: 2025-01-12 - First published: 2024-11-09 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open Qwen/Qwen2.5-Coder-32B-Instruct-GGUF on Hugging Face](https://huggingface.co/Qwen/Qwen2.5-Coder-32B-Instruct-GGUF) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/995eaf6d-bafd-4318-ab6c-b93444065280) - [Its page on Mindket](https://mindket.com/p/995eaf6d-bafd-4318-ab6c-b93444065280)