If you need a near-instant local setup, just fetch files via a basic curl request.
Simply follow the directions outlined below.
The framework seamlessly downloads the massive neural network binaries.
During setup, the script automatically determines and applies the best settings.
Gemma-4-E4B-it is a state‑of‑the‑art language model engineered for high‑efficiency inference on edge devices. It incorporates 2 B parameters and a 4 K context window, allowing nuanced comprehension while preserving low latency. The architecture leverages advanced quantization techniques to achieve sub‑2 ms token generation on consumer hardware. Its design includes multi‑head attention and grouped‑query attention, delivering strong performance across benchmarks such as MMLU and GSM‑8K. The model also supports seamless integration with developer tools through its open‑source API.
| Parameters | 2 B |
| Context Length | 4 K tokens |
| Quantization | INT4 |
| Throughput | >2000 tokens/s on GPU |
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