A quantized version of the Huihui-gemma-3n-E4B-it model has been released

Developers presented Huihui-gemma-3n-E4B-it-abliterated-GGUF — an optimized version of the language model, prepared for local launch on consumer equipment. The release includes a set of quantized files in the GGUF format, which allows the model to be used on devices with limited video memory. The given version of the model has undergone the "abliteration" procedure, aimed at removing restrictions in responses, making it more flexible for specific tasks. Files are available in various compression levels, which gives the opportunity to choose the optimal balance between generation accuracy and RAM or VRAM resource consumption. The toolkit for working with the model includes popular environments for local inference, such as llama.cpp and LM Studio. The use of the GGUF format ensures cross-platform compatibility, allowing the model to be launched on both CPU and GPU with CUDA or Metal support. Quantization allows significantly reducing the requirements for computational power without significant loss of response quality. This solution is oriented towards users who prefer to run neural networks locally, ensuring data confidentiality and absence of dependence on cloud APIs.