This is a 5-bit quantized version of Google's Gemma-4 26B model (A4B instruction-tuned variant) optimized for the MLX framework. It is hosted on Hugging Face and designed for local inference on Apple Silicon hardware, allowing developers to run a capable language model with significantly reduced memory footprint compared to the original.
Gemma 4 26B A4B It sits in PulseGate's Quantised & converted weights category. It focuses on running large language models locally with reduced memory requirements. Gemma 4 26B A4B It is an open-source project aimed at developers and AI researchers. Gemma 4 26B A4B It is open source under the MIT license. It ships for the web, the command line, and API.
lmstudio-community builds and maintains Gemma 4 26B A4B It, and it first shipped in 2024. The project is developed in the open on GitHub with 5.2k stars and 418 commits in the last 90 days. It competes in a saturated segment with 24 similar projects in PulseGate's index. Key capabilities include quantized weights, MLX optimization, and instruction tuned.
Summary written by a language model from the project’s public pages.
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