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 sits in PulseGate's Foundation models & chat category. It focuses on running large language models efficiently on consumer hardware with reduced memory and compute requirements. Gemma 4 E4B It Qat is an open-source project aimed at developers. The project is open source (Apache-2.0). It runs on the web, the command line, and API.
Behind Gemma 4 E4B It Qat is Unsloth, and the product 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. It competes in a saturated segment with 25 similar apps in PulseGate's index. Among its 3 catalogued features are Quantized GGUF, instruction tuned, and efficient inference.
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