Instructions to use edwardyoon79/Qwen3-Coder-Next-TQ3_0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use edwardyoon79/Qwen3-Coder-Next-TQ3_0 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="edwardyoon79/Qwen3-Coder-Next-TQ3_0",
	filename="Qwen3-Coder-Next-UD-TQ3_25bpw.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use edwardyoon79/Qwen3-Coder-Next-TQ3_0 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 edwardyoon79/Qwen3-Coder-Next-TQ3_0
# Run inference directly in the terminal:
llama cli -hf edwardyoon79/Qwen3-Coder-Next-TQ3_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf edwardyoon79/Qwen3-Coder-Next-TQ3_0
# Run inference directly in the terminal:
llama cli -hf edwardyoon79/Qwen3-Coder-Next-TQ3_0

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 edwardyoon79/Qwen3-Coder-Next-TQ3_0
# Run inference directly in the terminal:
./llama-cli -hf edwardyoon79/Qwen3-Coder-Next-TQ3_0

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 edwardyoon79/Qwen3-Coder-Next-TQ3_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf edwardyoon79/Qwen3-Coder-Next-TQ3_0

Use Docker

docker model run hf.co/edwardyoon79/Qwen3-Coder-Next-TQ3_0

LM Studio
Jan
Ollama
How to use edwardyoon79/Qwen3-Coder-Next-TQ3_0 with Ollama:
```
ollama run hf.co/edwardyoon79/Qwen3-Coder-Next-TQ3_0
```

Unsloth Studio

How to use edwardyoon79/Qwen3-Coder-Next-TQ3_0 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 edwardyoon79/Qwen3-Coder-Next-TQ3_0 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 edwardyoon79/Qwen3-Coder-Next-TQ3_0 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for edwardyoon79/Qwen3-Coder-Next-TQ3_0 to start chatting

How to use edwardyoon79/Qwen3-Coder-Next-TQ3_0 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf edwardyoon79/Qwen3-Coder-Next-TQ3_0

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": "edwardyoon79/Qwen3-Coder-Next-TQ3_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use edwardyoon79/Qwen3-Coder-Next-TQ3_0 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf edwardyoon79/Qwen3-Coder-Next-TQ3_0

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 edwardyoon79/Qwen3-Coder-Next-TQ3_0

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use edwardyoon79/Qwen3-Coder-Next-TQ3_0 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf edwardyoon79/Qwen3-Coder-Next-TQ3_0

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 "edwardyoon79/Qwen3-Coder-Next-TQ3_0" \
  --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 edwardyoon79/Qwen3-Coder-Next-TQ3_0 with Docker Model Runner:
```
docker model run hf.co/edwardyoon79/Qwen3-Coder-Next-TQ3_0
```

Lemonade

How to use edwardyoon79/Qwen3-Coder-Next-TQ3_0 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull edwardyoon79/Qwen3-Coder-Next-TQ3_0

Run and chat with the model

lemonade run user.Qwen3-Coder-Next-TQ3_0-{{QUANT_TAG}}

List all available models

lemonade list

Qwen3-Coder-Next-UD-TQ3_0 (GGUF)

This repository contains the TQ3_0 quantized version of the Qwen3-Coder-Next model, specifically optimized for the latest NVIDIA hardware.

🚀 Model Highlights

Quantization Method: TurboQuant (TQ3_0) — Fine-tuned for superior intelligence retention.
Target Bitrate: 3.25 bpw (Bits Per Weight) — Strategic sweet spot between 3-bit and 4-bit quantization.
Hardware Used: Quantized on a dedicated NVIDIA GeForce RTX 5090.
Optimization: Built using a custom-patched llama.cpp (llama-turbo) to support the high-efficiency TQ3_0 algorithm.

🛠️ Quantization Details

The TQ3_0 format utilizes advanced Lloyd-Max quantization and Walsh-Hadamard Transform (WHT) to minimize information loss. This specific version has been calibrated to 3.25 bpw, offering a balanced sweet spot between 3-bit and 4-bit quantization.

BPW (Bits Per Weight): 3.25
Size: Approximately 30.4 GB (ideally suited for 32GB VRAM GPUs like the RTX 5090)
Efficiency: Balanced for ultra-fast throughput while maintaining high-level coding logic.

💻 How to Use

To run this model, you need a compatible inference engine that supports TurboQuant.

Using llama-server (Example)

./llama-server \
    -m Qwen3-Coder-Next-UD-TQ3_0.gguf \
    -ngl 99 \
    -c 32768 \
    --port 8080

Downloads last month: 26

GGUF

Model size

80B params

Architecture

qwen3next

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for edwardyoon79/Qwen3-Coder-Next-TQ3_0

Base model

Qwen/Qwen3-Coder-Next

Quantized

(105)

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