Instructions to use edwardyoon79/Qwen3-Coder-Next-TQ3_0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
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
- llama.cpp
How to use edwardyoon79/Qwen3-Coder-Next-TQ3_0 with llama.cpp:
Install from brew
brew install llama.cpp # 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
Install from WinGet (Windows)
winget install llama.cpp # 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
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 new
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
- Pi new
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-server -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-server -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
- 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.
๐ Model Highlights
- Quantization Method: TurboQuant (TQ3_0) โ Fine-tuned for superior intelligence retention.
- Target Bitrate: 3.25 bpw (Bits Per Weight) โ 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.
๐ ๏ธ 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.
๐ป 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
- 98
We're not able to determine the quantization variants.
Model tree for edwardyoon79/Qwen3-Coder-Next-TQ3_0
Base model
Qwen/Qwen3-Coder-Next