Qwen2.5-Coder-32B-Instruct-GGUF

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.

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.

pip install -U huggingface_hub

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

# ./llama-gguf-split --merge <first-split-file-path> <merged-file-path>
./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.

./llama-cli -m <gguf-file-path> \
    -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

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:

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.