Qwen3-14B-GGUF

Qwen3-14B-GGUF

Gives you Qwen3 14B, a text generation model that can think through hard questions or answer fast, as GGUF files for your own computer. Packaged by unsloth for people who run models locally.

Made by unsloth 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: Qwen3-14B-GGUF holds GGUF copies of Qwen3 14B, a text generation model from the Qwen family, packaged by unsloth. One model covers two modes, a thinking mode for hard reasoning, maths and coding, and a plain mode for quick everyday chat. The GGUF files are meant for running it on your own computer.

Who it suits: It suits people who want a 14.8B open model they can run locally and who like to choose per question between slow reasoning and fast replies.

Industry | Who uses it | How they use it | Example
Software | Developers | Run a local assistant for code questions | Switch thinking on for a tricky bug, off for a quick syntax check
Education | Students | Work through maths with the reasoning shown | Ask a word problem and read the steps before the answer
Business | Small teams | Draft short routine replies with thinking switched off | Ask for a brief answer to a common customer question

People who only want a hosted chatbot can skip it. So can anyone short on disk space, since the 4-bit weights are about 7.4 GB.

Where to use it: The download runs on any computer with Python, and the files open in runners that read GGUF.

• Windows, macOS and Linux, with Python for the download
• llama.cpp, Ollama and Open WebUI, which the card names for the thinking switch
• Python with the transformers library, the library the record lists
• Google Colab, through unsloth's free fine-tuning notebook

Use case | Who | What they get back
Hard reasoning questions | Students and developers | A visible reasoning section, then the answer
Quick chat | Anyone | A direct answer with an empty reasoning section
Fine-tuning | Practitioners | A fine-tuned copy you can export to Ollama, llama.cpp or Hugging Face

How to set it up: You need three things first.

• Python with pip
• About 7.4 GB of free disk for the 4-bit weights, which is an estimate
• A runner such as llama.cpp, Ollama or Open WebUI

1. Install the Hugging Face hub library with pip.
2. Download the repository's files to your computer with the hf command.
3. Open the downloaded GGUF file in your runner.

pip install -U huggingface_hub
hf download unsloth/Qwen3-14B-GGUF

How to use it: Chat as you normally would, and add /think or /no_think to a message to pick the mode for that turn. In a multi-turn chat the model follows whichever instruction came last.

> Who are you /no_think
> How many 'r's are in 'strawberries'? /think

Tool | What it does
/think | Turns the thinking mode on for a message
/no_think | Turns thinking off for a message
hf download | Fetches the repository files to your disk

Example: Send "How many 'r's are in 'strawberries'? /think" and the model works through the question in a think section first. It then answers that the letter shows up 3 times. Send "Who are you /no_think" instead and the think section stays empty. The model names itself as Qwen, a language model from Alibaba Cloud.

For newcomers and experts: If you are new to this, download the files with the two commands above, load them in Ollama or another runner and try a simple question with /no_think. Then ask a harder one with /think and compare how the replies differ.

If you know the field, one model holds both modes, so you switch per turn instead of loading two models. The card says unsloth's Dynamic 2.0 quantization beats other quants on accuracy, which is unsloth's own claim. A 4-bit file trades some precision for a much smaller download.

You add /think or /no_think to a message
The runner passes the message to the model
Thinking mode writes reasoning, then answers
Plain mode skips the reasoning and answers

Good to know: The record lists English, and the card also mentions multilingual support.

• License: Apache 2.0, as the Hugging Face record states
• Size: 14.8B parameters, with weights of about 7.4 GB at 4-bit (an estimate)
• Other versions: the record counts 2 quantized copies and 0 fine-tuned ones made by other people
• Cost to run: none named
• Data it reaches: the download fetches files from Hugging Face to your disk, and the source names no account

Title: Fine-tuning speed-up claimed for unsloth's notebooks
Qwen3 (14B): 3 times faster
GRPO with Qwen3 (8B): 3 times faster
Qwen2.5 (7B): 2 times faster
Phi-4 (14B): 2 times faster

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: 75,244
• Likes: 148
• License: apache-2.0
• Languages: en
• Based on: Qwen/Qwen3-14B
• Quantized versions: 2
• Tags: transformers, gguf, qwen3, text-generation, qwen, unsloth, conversational

Links

• Model: https://huggingface.co/unsloth/Qwen3-14B-GGUF
• Files: https://huggingface.co/unsloth/Qwen3-14B-GGUF/tree/main

Get the model

Download its files with Hugging Face's command line:

pip install -U huggingface_hub
hf download unsloth/Qwen3-14B-GGUF

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