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

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 new

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

Pi new

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

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

danielhanchen commited on Feb 23

Commit

8df0b78

verified ·

1 Parent(s): 881901f

Update README.md

Browse files

Files changed (1) hide show

README.md +8 -6

README.md CHANGED Viewed

@@ -6,18 +6,15 @@ tags:
 - qwen3
 base_model:
 - Qwen/Qwen3-Coder-Next
-library_name: transformers
 license: apache-2.0
 license_link: https://huggingface.co/Qwen/Qwen3-Coder-Next/blob/main/LICENSE
 pipeline_tag: text-generation
 ---
 <div>
   <p style="margin-bottom: 0; margin-top: 0;">
-    <h1 style="margin-top: 0rem;">To Run Qwen3-Coder-Next locally - <a href="https://unsloth.ai/docs/models/qwen3-coder-next">Read our Guide.</a></h1>
-  </p>
-    <p style="margin-bottom: 0; margin-top: 0;">
-    <h2 style="margin-top: 0rem;">Feb 4 update: llama.cpp fixed a bug that caused Qwen to loop and have poor outputs.<br>We updated GGUFs - please re-download and update llama.cpp for improved outputs.</h2>
   </p>
 <p style="margin-top: 0;margin-bottom: 0;">
     <em><a href="https://unsloth.ai/docs/basics/unsloth-dynamic-2.0-ggufs">Unsloth Dynamic 2.0</a> achieves superior accuracy & outperforms other leading quants.</em>
   </p>
@@ -32,11 +29,16 @@ pipeline_tag: text-generation
       <img src="https://raw.githubusercontent.com/unslothai/unsloth/refs/heads/main/images/documentation%20green%20button.png" width="143">
     </a>
   </div>
-<h2 style="margin-top: 0rem;">Qwen3-Coder-Next Usage Guidelines</h2>
 </div>
 - It is recommended to have >45GB unified memory or RAM/VRAM to run 4-bit quants.
 - For best results, use any 2-bit XL quant or above (requires >30GB unified memory /combined RAM + VRAM).
 - For complete detailed instructions (sampling parameters etc.), see our guide: [docs.unsloth.ai/models/qwen3-coder-next](https://unsloth.ai/docs/models/qwen3-coder-next)
 ---

 - qwen3
 base_model:
 - Qwen/Qwen3-Coder-Next
 license: apache-2.0
 license_link: https://huggingface.co/Qwen/Qwen3-Coder-Next/blob/main/LICENSE
 pipeline_tag: text-generation
 ---
 <div>
   <p style="margin-bottom: 0; margin-top: 0;">
+    <h1 style="margin-top: 0rem;">To Run Qwen3-Coder-Next locally - <a href="https://unsloth.ai/docs/models/qwen3-coder-next">Read our Guide!</a></h1>
   </p>
 <p style="margin-top: 0;margin-bottom: 0;">
     <em><a href="https://unsloth.ai/docs/basics/unsloth-dynamic-2.0-ggufs">Unsloth Dynamic 2.0</a> achieves superior accuracy & outperforms other leading quants.</em>
   </p>
       <img src="https://raw.githubusercontent.com/unslothai/unsloth/refs/heads/main/images/documentation%20green%20button.png" width="143">
     </a>
   </div>
 </div>
+- **Feb 19 update**: Tool-calling should now be even better after llama.cpp fixes parsing.
+- **Quantization benchmarks**: See third-party Aider, LiveCodeBench v6, MMLU Pro, GPQA [benchmarks for GGUFs here](https://unsloth.ai/docs/models/qwen3-coder-next#gguf-quantization-benchmarks).</h2>
+- **Feb 4 update**: llama.cpp fixed a bug that caused Qwen to loop and have poor outputs.<br>We updated GGUFs - please re-download and update llama.cpp for improved outputs.</h2>
+## Qwen3-Coder-Next Usage Guidelines
 - It is recommended to have >45GB unified memory or RAM/VRAM to run 4-bit quants.
 - For best results, use any 2-bit XL quant or above (requires >30GB unified memory /combined RAM + VRAM).
+- See how to run the model via [Claude Code & OpenAI Codex](https://unsloth.ai/docs/models/qwen3-coder-next#claude-codex).
 - For complete detailed instructions (sampling parameters etc.), see our guide: [docs.unsloth.ai/models/qwen3-coder-next](https://unsloth.ai/docs/models/qwen3-coder-next)
 ---