# Medical Text Generation

Medra4b is a multilingual (English and Romanian) text generation model fine-tuned on medical datasets, specializing in medical domain applications.

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

MedGemma-27B-Text-IT-4bit is an MLX-format model converted from Google's MedGemma-27B-Text-IT model, specifically optimized for medical and clinical reasoning tasks.

MedGemma is a large language model developed by Google for the medical field, focusing on clinical reasoning and medical text generation tasks.

MedGemma-27B-Text-IT is a large language model developed by Google, focusing on text generation tasks in the medical field.

MedGemma is a medical multimodal model optimized based on Gemma 3, specifically designed for medical text and image understanding, available in 4B and 27B versions.

An 8B-parameter language model based on the Llama 3.1 architecture, created by merging multiple specialized models, excelling in text generation tasks.

medllama3-v20 is a text generation model specialized in the medical field, developed based on the Llama3 architecture, aiming to provide high-quality medical text generation capabilities.

A fine-tuned version based on epfl-llm/meditron-7b using the Alpaca dataset for SFT training, specializing in medical Q&A tasks

This is a fine-tuned version of the Clinical-T5 model for PET report findings summarization, capable of automatically generating personalized clinical impressions based on PET examination findings.

A fine-tuned PEGASUS model specialized for summarizing the findings section of PET reports, capable of generating accurate and personalized whole-body PET report impressions.

A sequence-to-sequence model based on BioBart for summarizing radiological findings into impressions, trained on 70,000 radiology reports.

This model is further pre-trained on GPT2, incorporating specific contexts from COVID-19-related news articles, making it suitable for generating COVID-19-related content.