# Low Parameter Count

Lite-Whisper is a compressed version of OpenAI Whisper, utilizing LiteASR technology to significantly reduce model size while maintaining high accuracy.

Lite-Whisper is a compressed version based on OpenAI Whisper, utilizing LiteASR technology to reduce model size while maintaining high accuracy.

The Ruri Reranker is a general-purpose Japanese reranking model specifically designed to improve the relevance ranking of Japanese text retrieval results. The small version maintains high performance while having a smaller parameter count.

This model is fine-tuned from MBZUAI/LaMini-Flan-T5-248M for prompt enhancement tasks, primarily used for text description enhancement.

This model is a large language model trained using H2O LLM Studio based on open_llama_3b, specializing in text generation tasks related to the pharmaceutical field.

A lightweight semantic segmentation model pre-trained on the PASCAL VOC dataset, combining MobileViT and DeepLabV3 architectures

A lightweight vision Transformer model combining MobileNetV2 and Transformer modules, suitable for mobile semantic segmentation tasks.

MobileViT is a lightweight, low-latency vision Transformer model that combines the strengths of CNNs and Transformers, making it suitable for mobile devices.

MobileViT is a lightweight, low-latency vision Transformer model that combines the advantages of CNNs and Transformers, making it suitable for mobile devices.

A question-answering model fine-tuned and optimized on SQuAD v1 based on BERT-base uncased, retaining 43% of original weights through pruning techniques, achieving 1.96x faster inference speed