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)

bash

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

edwardyoon79/Qwen3-Coder-Next-TQ3_0

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

🚀 Model Highlights

🛠️ Quantization Details

💻 How to Use

Using llama-server (Example)

edwardyoon79/
Qwen3-Coder-Next-TQ3_0