Safetensors
How to Run gemma-4-12B-it-QAT-GGUF via WebGPU (Browser) Offline Setup
Using a native PowerShell script is the absolute quickest way to install this model.
Please adhere to the deployment steps listed below.
The process automatically pulls down gigabytes of critical model assets.
An automated hardware sweep ensures the system will select the best tuning parameters.
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???? Hash code: 9c95258e0a5399690e0d7e333559e686 — Last modification: 2026-07-05
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The **gemma-4-12B-it-QAT-GGUF** model is a 12‑billion parameter instruction‑tuned language model designed for high performance and efficiency. It leverages *QAT* (quantized aware training) and the GGUF format to achieve a *balanced trade‑off* between accuracy and inference speed on consumer hardware. The model supports a context window of up to **8192** tokens, enabling it to understand and generate longer passages with coherent reasoning. Benchmarks show it outperforms comparable open models in reasoning and coding tasks while maintaining a modest memory footprint. Below is a quick comparison of its core specifications to illustrate how it stands against other popular open models:
| Spec | Value |
|---|---|
| Parameters | **12 B** |
| Context Length | **8192** tokens |
| Quantization | QAT‑GGUF |
| Benchmark (MMLU) | 68% |
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