Instructions to use MaziyarPanahi/Qwen3-Coder-Next-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MaziyarPanahi/Qwen3-Coder-Next-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="MaziyarPanahi/Qwen3-Coder-Next-GGUF",
	filename="Qwen3-Coder-Next.Q2_K.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use MaziyarPanahi/Qwen3-Coder-Next-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf MaziyarPanahi/Qwen3-Coder-Next-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf MaziyarPanahi/Qwen3-Coder-Next-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf MaziyarPanahi/Qwen3-Coder-Next-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf MaziyarPanahi/Qwen3-Coder-Next-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf MaziyarPanahi/Qwen3-Coder-Next-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf MaziyarPanahi/Qwen3-Coder-Next-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf MaziyarPanahi/Qwen3-Coder-Next-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf MaziyarPanahi/Qwen3-Coder-Next-GGUF:Q4_K_M

Use Docker

docker model run hf.co/MaziyarPanahi/Qwen3-Coder-Next-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use MaziyarPanahi/Qwen3-Coder-Next-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MaziyarPanahi/Qwen3-Coder-Next-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MaziyarPanahi/Qwen3-Coder-Next-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/MaziyarPanahi/Qwen3-Coder-Next-GGUF:Q4_K_M

Ollama
How to use MaziyarPanahi/Qwen3-Coder-Next-GGUF with Ollama:
```
ollama run hf.co/MaziyarPanahi/Qwen3-Coder-Next-GGUF:Q4_K_M
```

Unsloth Studio

How to use MaziyarPanahi/Qwen3-Coder-Next-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for MaziyarPanahi/Qwen3-Coder-Next-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for MaziyarPanahi/Qwen3-Coder-Next-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for MaziyarPanahi/Qwen3-Coder-Next-GGUF to start chatting

How to use MaziyarPanahi/Qwen3-Coder-Next-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf MaziyarPanahi/Qwen3-Coder-Next-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "MaziyarPanahi/Qwen3-Coder-Next-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use MaziyarPanahi/Qwen3-Coder-Next-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf MaziyarPanahi/Qwen3-Coder-Next-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default MaziyarPanahi/Qwen3-Coder-Next-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use MaziyarPanahi/Qwen3-Coder-Next-GGUF with Docker Model Runner:
```
docker model run hf.co/MaziyarPanahi/Qwen3-Coder-Next-GGUF:Q4_K_M
```

Lemonade

How to use MaziyarPanahi/Qwen3-Coder-Next-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull MaziyarPanahi/Qwen3-Coder-Next-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3-Coder-Next-GGUF-Q4_K_M

List all available models

lemonade list

Qwen3-Coder-Next-GGUF / README.md

MaziyarPanahi

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	---
	base_model: Qwen/Qwen3-Coder-Next
	inference: false
	model_creator: Qwen
	model_name: Qwen3-Coder-Next-GGUF
	pipeline_tag: text-generation
	quantized_by: MaziyarPanahi
	tags:
	- quantized
	- 2-bit
	- 3-bit
	- 4-bit
	- 5-bit
	- 6-bit
	- 8-bit
	- GGUF
	- text-generation
	---
	# [MaziyarPanahi/Qwen3-Coder-Next-GGUF](https://huggingface.co/MaziyarPanahi/Qwen3-Coder-Next-GGUF)
	- Model creator: [Qwen](https://huggingface.co/Qwen)
	- Original model: [Qwen/Qwen3-Coder-Next](https://huggingface.co/Qwen/Qwen3-Coder-Next)

	## Description
	[MaziyarPanahi/Qwen3-Coder-Next-GGUF](https://huggingface.co/MaziyarPanahi/Qwen3-Coder-Next-GGUF) contains GGUF format model files for [Qwen/Qwen3-Coder-Next](https://huggingface.co/Qwen/Qwen3-Coder-Next).

	### About GGUF

	GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.

	Here is an incomplete list of clients and libraries that are known to support GGUF:

	* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
	* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
	* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.
	* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
	* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
	* [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
	* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
	* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
	* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
	* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.

	## Special thanks

	🙏 Special thanks to [Georgi Gerganov](https://github.com/ggerganov) and the whole team working on [llama.cpp](https://github.com/ggerganov/llama.cpp/) for making all of this possible.