Qwen2.5-Coder-7B-Instruct-GGUF

Qwen2.5-Coder-7B-Instruct-GGUF

Runs a 7B instruction-tuned coding model from Qwen on your own machine, packed as GGUF files in eight quantization levels. Made for developers who want code help through llama.cpp.

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: A GGUF packaging of Qwen's Qwen2.5-Coder 7B instruct model, which llama.cpp can run on your own computer. Qwen2.5-Coder is Qwen's coding model family, the successor to CodeQwen1.5, with gains in writing, reasoning about and fixing code. Eight quantization levels are on offer.

Who it suits: Developers who want a coding model that runs on their own hardware instead of a hosted service. It also fits people building code agents on top of a local model.

Industry | Who uses it | How they use it | Example
Software | Developers | Draft, explain and repair code with a local model | Ask it to fix a failing function
Education | Students learning to code | Get code explained in plain terms | Paste a snippet and ask what it does
Tooling | Agent builders | Use it as the coding model inside an agent | Point an agent at the local model for code steps

If you want the largest context window, you can skip this GGUF copy.

Where to use it:

• llama.cpp on Windows, macOS or Linux
• A terminal chat session started with llama-cli
• Code agent setups that need a local coding model

Use case | Who | What they get back
Code generation | Developers | Code drafted from a plain request
Code fixing | Developers | A corrected version of code you supply
Code reasoning | Students and reviewers | An explanation of what a piece of code does

How to set it up:

• Python with pip, to fetch the files
• llama.cpp, cloned and installed with its official guide
• Disk space for the GGUF file you pick, about 3.8 GB at 4-bit by Hugging Face's estimate

1. Install the Hugging Face hub package.
2. Download one quantization of the model. The example below takes q5_k_m.
3. If the file comes in several parts, merge them with llama-gguf-split.

pip install -U huggingface_hub

huggingface-cli download Qwen/Qwen2.5-Coder-7B-Instruct-GGUF --include "qwen2.5-coder-7b-instruct-q5_k_m*.gguf" --local-dir . --local-dir-use-symlinks False

Large files are split into parts that share a prefix and end with an index. Download all of them, then merge:

./llama-gguf-split --merge qwen2.5-coder-7b-instruct-q5_k_m-00001-of-00002.gguf qwen2.5-coder-7b-instruct-q5_k_m.gguf

How to use it: Run the commands from inside the llama.cpp folder. Start llama-cli in conversation mode, point -m at your GGUF file, and chat 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 file or files you name
llama-gguf-split --merge | Joins split GGUF parts into one file
llama-cli | Runs the model as a chat in your terminal

Example: You start llama-cli in conversation mode and ask it to write a function that parses a date string. The model answers in the terminal with code, and you can keep asking follow-ups in the same session.

For newcomers and experts: If you are new, GGUF is a file format that lets llama.cpp run a model on a normal computer. Download one file, run the chat command with that file, and ask a coding question.

This is a 7.61B parameter causal model with 28 layers and grouped query attention, 28 heads for queries and 4 for keys and values. It uses RoPE, SwiGLU and RMSNorm. The GGUF copy has a 32,768 token context. YARN extrapolation to 131,072 tokens works only in vLLM, so the card points to the non-GGUF models for that. The eight quantization levels run from q2_K at the small end to q8_0 at the large end.

Pick a quantization level
Download its GGUF file or files
Merge split parts if there are any
Run llama-cli with -m on the file

Good to know:

• Context: 32,768 tokens in this GGUF copy
• Quantization: lower levels save memory and higher ones keep more detail
• License: Apache 2.0
• Cost to run: none named, it runs locally with llama.cpp
• Data it reaches: the prompts you type and the files downloaded from Hugging Face
• Not for: sequences up to 131,072 tokens, where the card sends you to non-GGUF models

On Hugging Face, 13 November 2026

• Task: text generation
• Library: transformers
• Parameters: 7.6B
• Weights: about 3.8 GB at 4-bit (estimate)
• Formats: GGUF
• Downloads in the last 30 days: 375,860
• Likes: 653
• License: apache-2.0
• Languages: en
• Based on: Qwen/Qwen2.5-Coder-7B-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-7B-Instruct-GGUF
• Files: https://huggingface.co/Qwen/Qwen2.5-Coder-7B-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-7B-Instruct-GGUF

Then follow the usage in its model card. Nothing in this kit runs by itself.