# Low-resource Efficiency

Phi-4 Reasoning Enhanced is a 14-billion parameter open-source reasoning model developed by Microsoft Research, optimized through supervised fine-tuning and reinforcement learning, focusing on advanced reasoning capabilities in mathematics, science, and programming fields.

A text summarization model fine-tuned on the XSum dataset based on T5-small

Korean text summarization model based on T5 architecture, specifically fine-tuned for news reporting

DRAMA-large (0.3B) is a dense retrieval model built upon a pruned large language model architecture, optimized for efficient and generalizable multilingual text retrieval tasks.

IBM Granite Embedding 30M English is a transformer-based English text embedding model developed and released by IBM.

A summarization model fine-tuned using QLoRa technology on the scitldr dataset based on Meta-Llama-3-8B

A bilingual reranking model based on Bloomz-560m for measuring semantic relevance between queries and contexts, supporting both French and English

GGUF-Imatrix quantized version of Prodigy_7B, utilizing importance matrix technology to enhance quantization quality

OpenChat 3.5 7B is a 7B-parameter large language model based on the Mistral architecture, developed by the OpenChat team and released under the Apache 2.0 license.

News summarization model fine-tuned on google/pegasus-xsum, trained on the cnn_dailymail dataset

A text summarization generation model fine-tuned on the cnn_dailymail dataset based on the T5-small model

A text summarization model fine-tuned on the cnn_dailymail dataset based on the T5-small architecture, excelling in news summarization tasks

A QA and NER model fine-tuned on the conll2003 dataset based on distilbert-base-uncased-squad2

This model is a BART variant fine-tuned on the SQuAD dataset, specifically designed to generate corresponding questions based on text passages and answers.

A multi-task model based on DistilBERT for question answering and named entity recognition, fine-tuned on the conll2003 dataset