# Knowledge distillation optimization

This is a fine-tuned sentence transformer model based on xlm-roberta-base, supporting English and Ukrainian, suitable for tasks like semantic textual similarity and semantic search.

A Verilog RTL code generation model distilled from DeepSeek-R1, demonstrating outstanding performance in Verilog benchmarks

AIM-v2 is an efficient image encoder implemented based on the timm library, suitable for various computer vision tasks.

Ultravox is a multimodal voice large language model based on Llama3.1-8B-Instruct and Whisper-medium, capable of processing both voice and text inputs simultaneously.

A fine-tuned model based on DistilBERT for detecting and reducing political bias in text, utilizing knowledge distillation and diffusion techniques to achieve unbiased text representation.

An improved multilingual automatic speech recognition model based on whisper-small, enhancing target language performance through CLSR module and knowledge distillation

LaMini-T5-738M is an instruction-tuned model based on t5-large, fine-tuned on the LaMini-instruction dataset, with 738M parameters, and is one of the LaMini-LM series models.

A multilingual sentence embedding model optimized for Bengali, mapping text to a 768-dimensional vector space

QuaLA-MiniLM is a compact language model developed by Intel, integrating knowledge distillation, length-adaptive transformers, and 8-bit quantization technology. It achieves up to 8.8x acceleration on the SQuAD1.1 dataset with less than 1% accuracy loss.

MiniLMv2 is a lightweight language representation model developed by Microsoft, achieving efficient performance through knowledge distillation techniques.

MiniLMv2 is a lightweight language representation model developed by Microsoft, achieving efficient inference through knowledge distillation techniques, suitable for various natural language processing tasks.