To get this model running locally in no time, utilize the built-in WSL tools.
Use the instructions provided below to complete the setup.
1-click setup: the app automatically fetches the large weight files.
You don’t need to tweak anything; the installer picks the highest performing setup.
The Gemma-4-31B-it-qat-w4a16-ct is a large language model designed for instruction following and conversational tasks. It leverages 31 billion parameters to achieve a balance between accuracy and computational efficiency. The model employs QAT (quantized aware training) combined with a w4a16 format, enabling reduced memory footprint while preserving performance. Its CT architecture incorporates advanced attention mechanisms that improve context retention and response relevance. The following table summarizes key technical attributes.
| Parameter Count | 31 B |
| Quantization | QAT (w4a16) |
| Precision | 16‑bit float |
| Training Method | Instruction‑following fine‑tuning |
| Architecture | CT with enhanced attention |
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