This is an efficient text summarization model specifically designed to compress text content in large language model prompts, achieving a compression ratio of over 7 times and significantly reducing API call costs.

Natural Language ProcessingTransformersEnglish

This is a multimodal model that supports multilingual processing, covering multiple fields such as natural language processing, code processing, and audio processing. It can perform various tasks such as automatic speech recognition, speech summarization, speech translation, and visual question answering.

MultimodalSafetensorsMultiple Languages

NVIDIA Nemotron Nano v2 12B VL is a powerful multimodal vision-language model that supports multi-image reasoning and video understanding, and has document intelligence, visual question answering, and summarization capabilities. It can be used for commercial purposes.

MultimodalTransformers

This is a lightweight text summary generation model based on the Google T5 Efficient Tiny architecture. It is trained using the shorecode/summary-collection-200k-rows dataset and is specifically designed for automatic text summarization tasks. It features efficient inference and low resource consumption.

Natural Language ProcessingTransformersEnglish

This is a quantized model for the YouTube comment summarization task. It is based on the Sivakkanth/youtube_comments_summarizer base model and undergoes static quantization processing. This model is specifically designed for the automatic summarization of English YouTube comments and provides multiple quantized versions to meet different hardware requirements.

Natural Language ProcessingTransformersEnglish

PiloT5 is a Serbian text summarization model based on the T5 architecture, with 248 million parameters. It is specifically designed for the automatic summarization task of Serbian texts. This model was developed by the TESLA project and is supported by the Science Fund of the Republic of Serbia.

Natural Language ProcessingTransformers

Gemma 3 270M is a lightweight multimodal model launched by Google. Based on the same research technology as the Gemini series, it supports text and image input, has a 32K context window, and provides high-quality output in tasks such as question answering, summarization, image understanding, and code generation.

MultimodalTransformersMultiple Languages

This is a LoRA adapter model fine-tuned for the medical field based on openai/gpt-oss-20b, optimized specifically for tasks such as medical Q&A, summary generation, and knowledge retrieval. The model retains the general inference ability of the 20 billion parameter base model through efficient parameter fine-tuning techniques while enhancing its performance in the medical field.

Natural Language ProcessingPeft

CodeModernBERT-Owl-3.0 is the final pre-trained version of the multilingual long context encoder model in the CodeModernBERT series, optimized for downstream code-related tasks such as code search, code summarization, error repair, and representation learning.

Natural Language ProcessingTransformersMultiple Languages

T5Gemma is a lightweight encoder-decoder research model launched by Google. By adapting a decoder-only pre-trained model to an encoder-decoder architecture, it achieves a better quality-efficiency trade-off. This model has a 9 billion parameter encoder and a 2 billion parameter decoder, and is suitable for various generation tasks such as question answering, summarization, and reasoning.

Natural Language ProcessingTransformers

This model is a text generation model fine-tuned based on Qwen2.5-0.5B-Instruct, focusing on the task of multilingual clinical report summary generation.

Natural Language ProcessingTransformersMultiple Languages

The Medra27B Quantized Model is the result of static quantization based on the nicoboss/Medra27B base model. It is suitable for tasks such as text generation, summary generation, and diagnostic reasoning in the medical field.

Natural Language ProcessingTransformersMultiple Languages

Llama-3.1-8B-tldr is a model fine-tuned on the trl-lib/tldr dataset based on meta-llama/Llama-3.1-8B, specifically designed for efficient summarization of Reddit posts.

Natural Language ProcessingTransformers

Medra4b is a quantized language model specialized in the medical field, supporting English and Romanian, with a focus on text generation and summarization tasks.

Natural Language ProcessingTransformersMultiple Languages

QwenMedic-v1 is a medical-specific language model developed based on Qwen3-1.7B, fine-tuned for clinical reasoning and instruction-following tasks to enhance diagnostic Q&A and clinical summarization capabilities.

Natural Language ProcessingGgufEnglish

Medra is a medical domain-specific QA and summarization model supporting English and Romanian, designed for medical AI applications.

Natural Language ProcessingGgufMultiple Languages

A meeting transcript summarization model optimized based on the BigBird-Pegasus architecture, capable of processing meeting records up to 4096 tokens and generating concise summaries

Natural Language ProcessingTransformersEnglish

A text summarization model fine-tuned on the reddit_tifu dataset based on facebook/bart-base

Natural Language ProcessingTransformers

Pleias-RAG-1B is a 1.2B-parameter compact reasoning model specifically designed for retrieval-augmented generation (RAG), search, and document summarization tasks. It excels in multilingual RAG tasks and supports structured citation generation.

Natural Language ProcessingTransformersMultiple Languages

Pleias-RAG-350M is a 350-million-parameter compact reasoning model specifically trained for retrieval-augmented generation (RAG), search, and source summarization tasks.

Natural Language ProcessingTransformersMultiple Languages