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

Libraries

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

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="unsloth/Qwen3-Coder-Next-GGUF",
	filename="BF16/Qwen3-Coder-Next-BF16-00001-of-00004.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 unsloth/Qwen3-Coder-Next-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf unsloth/Qwen3-Coder-Next-GGUF:UD-Q4_K_M
# Run inference directly in the terminal:
llama cli -hf unsloth/Qwen3-Coder-Next-GGUF:UD-Q4_K_M

Install from WinGet (Windows)

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

Use Docker

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

LM Studio
Jan

vLLM

How to use unsloth/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 "unsloth/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": "unsloth/Qwen3-Coder-Next-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

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

Unsloth Studio

How to use unsloth/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 unsloth/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 unsloth/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 unsloth/Qwen3-Coder-Next-GGUF to start chatting

How to use unsloth/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 serve -hf unsloth/Qwen3-Coder-Next-GGUF:UD-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": "unsloth/Qwen3-Coder-Next-GGUF:UD-Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use unsloth/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 serve -hf unsloth/Qwen3-Coder-Next-GGUF:UD-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 unsloth/Qwen3-Coder-Next-GGUF:UD-Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use unsloth/Qwen3-Coder-Next-GGUF with OpenClaw:

Start the llama.cpp server

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

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "unsloth/Qwen3-Coder-Next-GGUF:UD-Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

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

Lemonade

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

Pull the model

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

Run and chat with the model

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

List all available models

lemonade list

New discussion

Resources

View closed (3)

Qwen3-Coder-Next GGUF Benchmarks!

pinned

👍 4

#20 opened 5 months ago by

danielhanchen

Feb 19: Qwen3-Coder-Next GGUFs update - much better outputs!

pinned

🤗👍 16

#5 opened 5 months ago by

danielhanchen

Listed on OpenModelMap

#33 opened 25 days ago by

duola15

TemporalMesh Transformer: 29.4 PPL at 48% compute — beats Mamba, new open-source architecture

#32 opened about 1 month ago by

vigneshwar234

MTP version

🔥👍 11

#31 opened about 2 months ago by

DzmitryTheOtherOne

Huge thanks for the Qwen3-Coder-Next GGUFs 🙏

#30 opened 2 months ago by

urbanswelt

Android Studio issue with Qwen3-Coder-Next-GGUF

#28 opened 3 months ago by

DocWolle

Model load in gpu & ram but only CPU compute is used

#27 opened 4 months ago by

klaasb123

Agent Response missing at the end in prompt/chat template? Looping end.

#26 opened 4 months ago by

chrisoutwright

Issue with OpenCode

#24 opened 4 months ago by

Gogich77

Request for update

🤝➕ 4

#23 opened 4 months ago by

engrtipusultan

Error on Ollama | 3 bit quantized | 500 Internal Server Error

#22 opened 5 months ago by

Sidmttl

MXFP4 slower than Q4_K_M

#21 opened 5 months ago by

ykarout

TQ1_0 quants

🔥 2

#19 opened 5 months ago by

ilintar

Built a platform to deploy AI models instantly. Looking for feedback from ML engineers

#18 opened 5 months ago by

jensenjesper

GGUF Update (17 Feb)

👀 5

#17 opened 5 months ago by

allkhor

Ablation studies on effects of quantization on SSM weights?

#15 opened 5 months ago by

dinerburger

Feb 13 update? I have Feb 4, should I update?

👀 9

#14 opened 5 months ago by

tnuvkeg

UD-Q4_K_XL lower precision than Q4_K_M?

#12 opened 5 months ago by

zhuangzhoumengdie

Qwen3-Coder-Next-IQ4_NL.gguf tool calling issue. - Update: SOLVED

#11 opened 5 months ago by

robert1968

Publish KL-Divergence for Every Quant Level, for all your models

➕ 15

#10 opened 5 months ago by

BitBuilder

UD-Q5_K_XL not recognized

#9 opened 5 months ago by

Metalazzo

does not work with ollama 0.15.5 - 0.15.6

#8 opened 5 months ago by

asg345asdf

stepfun 3.5 flash pls

#7 opened 5 months ago by

Simon716

Inquiry About Unsloth Dynamic 1.8-bit Qwen3-Coder-Next-UD-TQ1_0.gguf for vLLM Deployment

#6 opened 5 months ago by

BuiDoan

just curious, why the lack of <thinking> response?

#4 opened 5 months ago by

YellowjacketGames

What are the differences between MFxP4, Q4, and UD-Q4 quantization? Which one is better to use on an AMD 395?

👍 8

#3 opened 5 months ago by

wszgrcy

Error jinja template LM Studio + Open Code or Qwen code or Kilo Code

#2 opened 5 months ago by

RGMC98

Check in here for tok/s and benchmarks for local gguf models

👍 1

#1 opened 5 months ago by

ykarout