This is a GGUF quantized version of the Gemma 4 4B instruction-tuned (it) model, optimized using Unsloth's QAT (Quantization Aware Training) techniques. It enables efficient local inference of a capable open LLM on consumer GPUs and CPUs with significantly reduced memory footprint. The model is hosted on Hugging Face and can be used with popular inference libraries supporting the GGUF format.
Gemma 4 E4B It Qat is a Text generation project. It focuses on running large language models efficiently on consumer hardware with reduced memory and compute requirements. It is built as an open-source project for developers. Gemma 4 E4B It Qat is open source under the Apache-2.0 license. It ships for the web, the command line, and API.
It is developed by Unsloth, and it first shipped in 2023. The project is developed in the open on GitHub with 68.7k stars and 1.2k commits in the last 90 days. The category is crowded — PulseGate's index counts 25 comparable projects. Among its 3 catalogued features are Quantized GGUF, instruction tuned, and efficient inference.
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