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 from brew

brew install llama.cpp
# 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

Install from WinGet (Windows)

winget install llama.cpp
# 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

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-server -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-server -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
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

Qwen3-Coder-Next GGUF Benchmarks!

#20

pinned

by danielhanchen - opened Feb 27

Discussion

danielhanchen

Unsloth AI org Feb 27

Benjamin Marie benchmarked Qwen3-Coder-Next using Unsloth and standard Qwen GGUFs on a 750-prompt mixed suite (LiveCodeBench v6, MMLU Pro, GPQA, Math500), reporting both overall accuracy and relative error increase (how much more often the quantized model makes mistakes vs. the original).

The graphs show the Unsloth's Q4_K_M quants perform better than standard Q4_K_M. Q3_K_M expectedly performs worse on Live Code Bench v6, but surprisingly much better on HumanEval than standard Q4_K_M.

Now we have Aider Polyglot benchmarks, comparing Unsloth GGUF quantizations (score vs. VRAM). Notably, the 3-bit UD-IQ3_XXS quant comes close to BF16 performance, making 3-bit a sensible minimum for most use cases.

NVFP4 slightly outperforms the BF16 reference, which may be sampling noise due to limited runs; however, the overall pattern for: 1-bit → 2-bit → 3-bit → 6-bit steadily improving, suggests the benchmark is capturing meaningful quality differences across Unsloth GGUFs. The non-Unsloth FP8 seems to perform worse than both UD-IQ3_XXS and UD-Q6_K_XL, which could reflect differences in the quantization pipeline or, again, insufficient sampling.

danielhanchen pinned discussion Feb 27

Simon716

Feb 27

hi, is there MXFP4 perform better or badder than Q4_K_M?

noelb24

Mar 4

i dont understand, why not benchmarking Q4_K_XL ? unsloth's Q4_K_M also special?

engrtipusultan

about 1 month ago

@danielhanchen will you be doing MTP update for coder next as well?

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