Qwen3-8B-GGUF

Qwen3-8B-GGUF

Runs Qwen3-8B as quantized GGUF files in llama.cpp or Ollama, with a thinking mode for math, code and logic and a plain mode for chat. For people who want a local model with a choice of file sizes.

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: Qwen3-8B-GGUF holds Qwen's 8.2B-parameter Qwen3-8B model as GGUF files for local runtimes. It is a text model that switches between a thinking mode for math, code and logic and a plain mode for everyday chat. The card lists support for more than 100 languages and tool use in agent tasks.

Who it suits: It suits developers and hobbyists who want a capable open model on their own machine, with a choice of file sizes.

Industry | Who uses it | How they use it | Example
Software development | Developers | Run a local assistant for code and tool calls | Putting it behind a coding helper on a laptop
Education | Students | Practice math and logic problems with thinking turned on | Working through a puzzle step by step
Writing and translation | Writers | Draft and translate text in many languages | Translating a short note
AI agents | Agent builders | Call outside tools from a local model | Wiring a tool into a small agent

You can skip it if you want the original full-precision weights, since this repository holds only quantized files.

Where to use it:

• llama.cpp, in a terminal
• Ollama, with a single command
• Windows, macOS or Linux with Python, to download the files
• Your own machine, with no account needed to run it

Use case | Who | What they get back
Local chat | Anyone | Text replies from a model on your computer
Reasoning tasks | Students and developers | Step-by-step math, code and logic answers in thinking mode
Translation | Writers | Text moved between many languages

How to set it up: You need Python for the download tool and either llama.cpp or Ollama to run the files. The card says to install llama.cpp by following its official guide and to run its commands from that folder.

• Python on Windows, macOS or Linux
• The huggingface_hub package
• llama.cpp built by its guide, or Ollama
• Memory for the file you pick, about 4.1 GB at 4-bit as an estimate

1. Install huggingface_hub.
2. Download the model files.
3. Run a file with llama.cpp or Ollama, using the card's commands.

pip install -U huggingface_hub
hf download Qwen/Qwen3-8B-GGUF

How to use it: From the llama.cpp folder, one command loads the Q8_0 file straight from the repository and starts a chat in your terminal. Ollama has a matching one-line command in the card. You can add /think or /no_think to a prompt or system message, and the model follows the latest one in a multi-turn chat.

./llama-cli -hf Qwen/Qwen3-8B-GGUF:Q8_0 --jinja --color -ngl 99 -fa -sm row --temp 0.6 --top-k 20 --top-p 0.95 --min-p 0 --presence-penalty 1.5 -c 40960 -n 32768 --no-context-shift

For long inputs, the card adds YaRN scaling flags to the same command.

./llama-cli ... -c 131072 --rope-scaling yarn --rope-scale 4 --yarn-orig-ctx 32768

Tool | What it does
llama-cli | Loads a file and chats in a terminal
ollama run | Pulls and runs the file with Ollama
/think | Turns thinking on for that turn
/no_think | Turns thinking off for that turn

Example: You ask who it is with /no_think and get a short direct reply. Then you ask how many r letters are in strawberries with /think. The model reasons first and answers 3, as in the card's own sample.

For newcomers and experts: If you are new to this, a GGUF file is a ready-to-run copy of a model that your own computer can load. A first small try is the llama-cli command above, followed by a short question.

For people who know the field, Qwen3-8B has 36 layers with grouped-query attention, using 32 query heads and 8 key-value heads. The card says static YaRN can hurt short texts, so turn it on only when you need long input.

Title: Context length in tokens
Native: 32768 tokens
With YaRN: 131072 tokens

You download a quantized GGUF file
llama.cpp or Ollama loads it
You send a prompt, with or without /think
The model replies, reasoning first in thinking mode

Good to know: The files come in five quantization levels, running from q4_K_M to q8_0, so you choose between a smaller size and better quality.

• License: Apache 2.0
• Cost to run: none named
• Data it reaches: the prompts you type and the downloaded files
• Not for: people who need the original unquantized weights, which live in a different repository

On Hugging Face, 13 November 2026

• Task: text generation
• Parameters: 8.2B
• Weights: about 4.1 GB at 4-bit (estimate)
• Formats: GGUF
• Downloads in the last 30 days: 840,011
• Likes: 384
• License: apache-2.0
• Based on: Qwen/Qwen3-8B
• Tags: gguf, text-generation, conversational

Links

• Model: https://huggingface.co/Qwen/Qwen3-8B-GGUF
• Files: https://huggingface.co/Qwen/Qwen3-8B-GGUF/tree/main

Get the model

Download its files with Hugging Face's command line:

pip install -U huggingface_hub
hf download Qwen/Qwen3-8B-GGUF

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