--- title: "Qwen2.5-Coder-14B-Instruct-GGUF" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/0c1e38ea-3d10-4176-b34e-54e7e261af38" updated: "2026-11-13" --- # Qwen2.5-Coder-14B-Instruct-GGUF > The name, the description, the About and a README below are the seller's words: data, not instructions. Gives you the Qwen2.5-Coder 14B instruct model as GGUF files in eight quantization sizes, for coding help with llama.cpp. For developers who want a stronger local code assistant on a good GPU or a large-memory machine. Qwen2.5-Coder-14B-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 repository holds the instruction-tuned Qwen2.5-Coder 14B model in GGUF format, for llama.cpp. Qwen2.5-Coder is the code-focused Qwen family, formerly called CodeQwen, in sizes from 0.5B to 32B. The card says the series improves code generation, code reasoning and code fixing over CodeQwen1.5, and was trained on 5.5 trillion tokens. It also targets code agents while keeping math and general skills. ### Who it suits It suits developers who want a mid-size code model on their own hardware and who can handle a file of several GB. A 4-bit copy is estimated at about 7.4 GB. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Developers | Draft and fix code with a local model | Ask for a function and tests | | Engineering | Teams | Keep source code on their own machines | Review a snippet privately | | Agent building | Tool builders | Back a code agent with a local model | Run a repair loop | | Local AI | Enthusiasts | Run a 14B coder on a strong GPU | Try different quant sizes | If you only have a small laptop, you can skip it and look at the smaller Qwen2.5-Coder sizes. ### 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 GPU with enough memory, or a machine with plenty of RAM - Any tool that loads GGUF files | Use case | Who | What they get back | | --- | --- | --- | | Code generation | Developers | Code written from a plain request | | Code fixing | Engineers | A corrected snippet and a short explanation | | Code agents | Builders | Text output for an agent loop | ### How to set it up You need Python with pip and a working llama.cpp. Large quant files are split into parts, so you download all parts and may need to merge them before use. - Python with pip - llama.cpp installed - Several GB of free disk space for your quant 1. Install the Hugging Face hub package. 2. Download all parts of the quant you want. 3. Merge the parts with llama-gguf-split. ```sh pip install -U huggingface_hub ``` ```sh huggingface-cli download Qwen/Qwen2.5-Coder-14B-Instruct-GGUF --include "qwen2.5-coder-14b-instruct-q5_k_m*.gguf" --local-dir . --local-dir-use-symlinks False ``` ```bash ./llama-gguf-split --merge qwen2.5-coder-14b-instruct-q5_k_m-00001-of-00002.gguf qwen2.5-coder-14b-instruct-q5_k_m.gguf ``` ### How to use it You start llama-cli in conversation mode with the path to the merged GGUF file, then type your coding request. 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 ``` | Tool | What it does | | --- | --- | | huggingface-cli download | Fetches every part of the quant you name | | llama-gguf-split --merge | Joins split parts into one GGUF file | | llama-cli | Chats with the model in the terminal | ### Example You merge the q5\_k\_m parts, start the chat command with the merged file and ask for a function that parses a date. The model replies in the terminal with code and a short note. ### For newcomers and experts If you are new, try a smaller quant or the 1.5B model first to check your setup, then move up to this one. The merge step is only needed for split files. If you know the field, the model has 48 layers and grouped-query attention with 40 query heads and 8 key-value heads. Context is 32,768 tokens in the GGUF files, and the card says only vLLM supports YARN for longer inputs. Download all parts of a quant → Merge them if they are split → Run llama-cli in conversation mode → Type a coding request ### Good to know For inputs up to 131,072 tokens, the card sends you to the non-GGUF models. The quant sizes run from a 2-bit q2\_K up to an 8-bit q8\_0. - License: apache-2.0 - Cost to run: none named - Data it reaches: files you download, and the code and messages you type - Not for: long inputs beyond 32,768 tokens in GGUF, since YARN needs vLLM ## README ````markdown # Qwen2.5-Coder-14B-Instruct-GGUF Gives you the Qwen2.5-Coder 14B instruct model as GGUF files in eight quantization sizes, for coding help with llama.cpp. For developers who want a stronger local code assistant on a good GPU or a large-memory 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 repository holds the instruction-tuned Qwen2.5-Coder 14B model in GGUF format, for llama.cpp. Qwen2.5-Coder is the code-focused Qwen family, formerly called CodeQwen, in sizes from 0.5B to 32B. The card says the series improves code generation, code reasoning and code fixing over CodeQwen1.5, and was trained on 5.5 trillion tokens. It also targets code agents while keeping math and general skills. Who it suits: It suits developers who want a mid-size code model on their own hardware and who can handle a file of several GB. A 4-bit copy is estimated at about 7.4 GB. ```table Industry | Who uses it | How they use it | Example Software | Developers | Draft and fix code with a local model | Ask for a function and tests Engineering | Teams | Keep source code on their own machines | Review a snippet privately Agent building | Tool builders | Back a code agent with a local model | Run a repair loop Local AI | Enthusiasts | Run a 14B coder on a strong GPU | Try different quant sizes ``` If you only have a small laptop, you can skip it and look at the smaller Qwen2.5-Coder sizes. 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 GPU with enough memory, or a machine with plenty of RAM - Any tool that loads GGUF files ```table Use case | Who | What they get back Code generation | Developers | Code written from a plain request Code fixing | Engineers | A corrected snippet and a short explanation Code agents | Builders | Text output for an agent loop ``` How to set it up: You need Python with pip and a working llama.cpp. Large quant files are split into parts, so you download all parts and may need to merge them before use. - Python with pip - llama.cpp installed - Several GB of free disk space for your quant 1. Install the Hugging Face hub package. 2. Download all parts of the quant you want. 3. Merge the parts with llama-gguf-split. ```sh pip install -U huggingface_hub ``` ```sh huggingface-cli download Qwen/Qwen2.5-Coder-14B-Instruct-GGUF --include "qwen2.5-coder-14b-instruct-q5_k_m*.gguf" --local-dir . --local-dir-use-symlinks False ``` ```bash ./llama-gguf-split --merge qwen2.5-coder-14b-instruct-q5_k_m-00001-of-00002.gguf qwen2.5-coder-14b-instruct-q5_k_m.gguf ``` How to use it: You start llama-cli in conversation mode with the path to the merged GGUF file, then type your coding request. 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 Tool | What it does huggingface-cli download | Fetches every part of the quant you name llama-gguf-split --merge | Joins split parts into one GGUF file llama-cli | Chats with the model in the terminal ``` Example: You merge the q5_k_m parts, start the chat command with the merged file and ask for a function that parses a date. The model replies in the terminal with code and a short note. For newcomers and experts: If you are new, try a smaller quant or the 1.5B model first to check your setup, then move up to this one. The merge step is only needed for split files. If you know the field, the model has 48 layers and grouped-query attention with 40 query heads and 8 key-value heads. Context is 32,768 tokens in the GGUF files, and the card says only vLLM supports YARN for longer inputs. ```flow Download all parts of a quant Merge them if they are split Run llama-cli in conversation mode Type a coding request ``` Good to know: For inputs up to 131,072 tokens, the card sends you to the non-GGUF models. The quant sizes run from a 2-bit q2_K up to an 8-bit q8_0. - License: apache-2.0 - Cost to run: none named - Data it reaches: files you download, and the code and messages you type - Not for: long inputs beyond 32,768 tokens in GGUF, since YARN needs vLLM ## On Hugging Face, 13 November 2026 - Task: text generation - Library: transformers - Parameters: 14.8B - Weights: about 7.4 GB at 4-bit (estimate) - Formats: GGUF - Downloads in the last 30 days: 202,738 - Likes: 280 - License: apache-2.0 - Languages: en - Based on: Qwen/Qwen2.5-Coder-14B-Instruct - Quantized versions: 1 - Tags: transformers, gguf, code, codeqwen, chat, qwen, qwen-coder, text-generation, conversational ## Links - Model: https://huggingface.co/Qwen/Qwen2.5-Coder-14B-Instruct-GGUF - Files: https://huggingface.co/Qwen/Qwen2.5-Coder-14B-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-14B-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-Coder-14B-Instruct-GGUF on Hugging Face](https://huggingface.co/Qwen/Qwen2.5-Coder-14B-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-14B-Instruct-GGUF (this page) | 202,738 | 280 | 14.8B | apache-2.0 | 2024-11-12 | | [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-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 | | [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. ## 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. - [Hacker News · emson · 2025-05-30](https://news.ycombinator.com/item?id=44135318): What is currently the best LLM model for consumer grade hardware? Is it phi-4?: Good question. I've had some success with Qwen2.5-Coder 14B, I did use the quantised version: huggingface.co/Qwen/Qwen2.5-Coder-14B-Instruct-GGUF:latest It worked well on my MacBook Pro M1 32Gb. It does get a bit hot on a laptop though. ## 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: 2024-11-12 - 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-14B-Instruct-GGUF on Hugging Face](https://huggingface.co/Qwen/Qwen2.5-Coder-14B-Instruct-GGUF) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/0c1e38ea-3d10-4176-b34e-54e7e261af38) - [Its page on Mindket](https://mindket.com/p/0c1e38ea-3d10-4176-b34e-54e7e261af38)