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

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

#11

by robert1968 - opened Feb 9

Discussion

robert1968

Feb 9

Qwen3-Coder-Next-IQ4_NL.gguf have tool calling issue.
IQ4_NL loaded exactly the same way as i used Qwen3-Coder-Next-IQ4_XS.gguf:

/adat/ai/llama.cpp/llama-server -m /adat/ai/models/unsloth/Qwen3-Coder-Next-GGUF/Qwen3-Coder-Next-IQ4_NL.gguf --host 0.0.0.0 --port 1234 -ngl 99 -c 65
536 -fa on --ctx-size 96500 --temp 1.0 --top-p 0.95 --min-p 0.01 --top-k 40 --fit on --parallel 1 --chat-template-file /adat/ai/llama.cpp/qwen3.jinja

But Opencode has lots of errors and does not work with IQ4_NL:

invalid [tool=write, error=Invalid input for tool write: JSON parsing failed: Text: {"

IQ4_XS- still works with Opencode with the same Llama.cpp server settings.

alexeiza

Feb 15

I'm gettting this error with all quants of this model:

invalid [tool=write, error=Invalid input for tool write: JSON parsing failed: Text: {"content":"...","filePath":"/home/myfile.py","filePath"/home/myfile.py"}.
Error message: JSON Parse error: Unrecognized token '/']

You can see "filePath" is specified twice, and second time incorrectly. Something is probably broken in the embedded template

Greyworks

Feb 16

I'm still having the same problem, too, wit the current llama.cpp Version and the recommended settings.
Q4_K_M. Same with the GGUF from Qwen themselves though.

In OpenCode it is always the write tool with the double "filePath" and the messed up JSON at the end like in your example.
It doesn't seem to be an unsloth specific problem but I wish there was a solution to this.

Llama.cpp is so much faster than ollama for me but ollama does not cause the problem with the model from their registry.

robert1968

Feb 16

•

edited Feb 17

This solved the tool call (save file tool call) issue and also for random server segfault issues too:
PR for the fix has been tested, but it hasn’t been merged into the main branch yet.
https://github.com/pwilkin/llama.cpp/tree/autoparser

Many thanks for it.

robert1968 changed discussion title from Qwen3-Coder-Next-IQ4_NL.gguf tool calling issue. to Qwen3-Coder-Next-IQ4_NL.gguf tool calling issue. - Update: SOLVED Feb 16

LadislavDanis

Feb 20

Hello, I was very frustrated that calling tools didn't work until I solved it like this for any model
https://github.com/ladislav-danis/systemd-llm-switch

tomstokes

Feb 20

In OpenCode it is always the write tool with the double "filePath" and the messed up JSON at the end like in your example.
It doesn't seem to be an unsloth specific problem but I wish there was a solution to this.

The malformed second filePath occurs when Qwen3-Coder-Next emits two filePath parameters for the same tool call. It doesn't happen on every write call but I can trigger it on longer sessions and/or with long writes. This is an unexpected quirk, so llama.cpp's XML parser and streaming diff logic weren't designed to handle duplicate keys.

I made a minimal PR to correctly translate duplicate XML parameter keys into JSON output which fixes the issue for me: https://github.com/ggml-org/llama.cpp/pull/19753

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