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🚀 Meerkat-7B-v1.0 is an instruction-tuned medical AI system that surpasses the passing threshold of 60% for the United States Medical Licensing Examination (USMLE) for the first time among all 7B-parameter models. The model was trained using our new synthetic dataset consisting of high-quality…

This is https://huggingface.co/kingabzpro/Qwen-3-32B-Medical-Reasoning applied to https://huggingface.co/Qwen/Qwen3-32B Original model card created by @kingabzpro

GP-MoLFormer is a class of models pretrained on SMILES string representations of 0.65-1.1B molecules from ZINC and PubChem. This repository is for the model pretrained on all the unique molecules from both datasets.

## Model Description EYE-Llama_gqa is a large language model specifically designed for ophthalmic question-answering (QA). It is built upon the Llama 2 architecture and fine-tuned on a the EYE-lit and EYE-QA+ dataset.

This model card describes the ClinicalBERT model, which was trained on a large multicenter dataset with a large corpus of 1.2B words of diverse diseases we constructed. We then utilized a large-scale corpus of EHRs from over 3 million patient records to fine tune the base language model.

This project fine-tunes the meta-llama/Llama-4-Scout-17B-16E-Instruct model using a medical reasoning dataset (FreedomIntelligence/medical-o1-reasoning-SFT) with 4-bit quantization for memory-efficient training.

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This deep learning model is designed for ECG image classification, fine-tuned using ResNet-50. It can classify ECG images into different categories to assist in heart disease detection.

If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files.

This model summarizes radiology findings into accurate, informative impressions to improve radiologist-clinician communication.

Diabetica: Adapting Large Language Model to Enhance Multiple Medical Tasks in Diabetes Care and Management

View label scheme (20 labels for 1 components)

PPTStab: Prediction and Designing of thermostable proteins with a desired melting temperature

INDUS-Retriever-small (previously nasa-smd-ibm-st.38m) is a Bi-encoder sentence transformer model, that is fine-tuned from distilled version of nasa-smd-ibm-v0.1 encoder model. it is a smaller version of nasa-smd-ibm-st with better performance, using fewer parameters (shown below).

If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files.

## Example Usage ```python from transformers import AutoTokenizer, T5ForConditionalGeneration

The Gradio Web UI allows you to use our examples or upload your images for inference.

This model is a high-performance Named Entity Recognition (NER) model designed specifically for medical text. It identifies entities such as diseases, symptoms, procedures, medications, and healthcare providers with high precision and recall, making it ideal for clinical and healthcare applications.

# GPN trained on Arabidopsis thaliana and 7 other Brassicales See https://github.com/songlab-cal/gpn for more details.

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The Clinical Assertion and Negation Classification BERT is introduced in the paper Assertion Detection in Clinical Notes: Medical Language Models to the Rescue? . The model helps structure information in clinical patient letters by classifying medical conditions mentioned in the letter into…

Model from this repo. Model used to be in Dropbox/GDrive, leading to issues with download

# FremyCompany/BioLORD-2023 This model was trained using BioLORD, a new pre-training strategy for producing meaningful representations for clinical sentences and biomedical concepts.

# FremyCompany/BioLORD-2023-M This model was trained using BioLORD, a new pre-training strategy for producing meaningful representations for clinical sentences and biomedical concepts.

# MMedS-Llama3 💻Github Repo 🖨️arXiv Paper

### Welcome to Nidum! At Nidum, we believe in pushing the boundaries of innovation by providing advanced and unrestricted AI models for every application. Dive into our world of possibilities and experience the freedom of Nidum-Llama-3.2-3B-Uncensored, tailored to meet diverse needs with…

If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files.

If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files.

We identified and fixed an issue related to a wrong permutation of some projections, which affects generation quality. To use the new model revision, please load as follows:

If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files.

This model aims to be a base template for new models. It has been generated using this raw template.

The plant DNA large language models (LLMs) contain a series of foundation models based on different model architectures, which are pre-trained on various plant reference genomes. All the models have a comparable model size between 90 MB and 150 MB, BPE tokenizer is used for tokenization and 8000…

Indus (previously known as nasa-smd-ibm-v0.1) is a RoBERTa-based, Encoder-only transformer model, domain-adapted for NASA Science Mission Directorate (SMD) applications. It's fine-tuned on scientific journals and articles relevant to NASA SMD, aiming to enhance natural language technologies like…

Indus-Retriever (nasa-smd-ibm-st-v2) is a Bi-encoder sentence transformer model, that is fine-tuned from nasa-smd-ibm-v0.1 encoder model. it is an updated version of nasa-smd-ibm-st with better performance (shown below). It's trained with 271 million examples along with a domain-specific dataset of…

MediFlow se trata de un modelo inicializado con xlnet-large-cased y adaptado con preguntas y especialidades para poder realizar Derivaciones Automatizadas en Servicios Hospitalarios. El dataset se puede encontrar de manera pública y se trata de MedDialog EN.

If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files.

> [!IMPORTANT] > Better using New version of ChemLLM! > AI4Chem/ChemLLM-7B-Chat-1.5-DPO or AI4Chem/ChemLLM-7B-Chat-1.5-SFT

Chemma-2B is a continually pretrained gemma-2b model for organic molecules. It is pretrained on 40B tokens covering 110M+ molecules from PubChem as well as their chemical properties (molecular weight, synthetic accessibility score, drug-likeness etc.) and similarities (Tanimoto distance between…

## Quick Start ```Python from transformers import AutoTokenizer, AutoModel

ChemFIE-SA is a BERT-like sequence classifier for predicting synthesis accessibility given a SELFIES string of a compound, fine-tuned from gbyuvd/chemselfies-base-bertmlm on DeepSA's expanded dataset from Wang et al. 2023.

This model is a BERT-like sequence classifier for 221 human protein drug targets, fine-tuned from gbyuvd/chemselfies-base-bertmlm on a dataset derived ChemBL34 (Zdrazil et al. 2023). It predicts potential drug targets using chemical structures represented as SELFIES (Self-Referencing Embedded…

The Mistral-DNA-v1-138M-bacteria Large Language Model (LLM) is a pretrained generative DNA text model with 17.31M parameters x 8 experts = 138.5M parameters. It is derived from Mistral-7B-v0.1 model, which was simplified for DNA: the number of layers and the hidden size were reduced.

Model Card for "medllama" ---------------------------

This is a ReactionT5 pre-trained to predict the products of reactions.

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## Model Overview AgroNT is a DNA language model trained on primarily edible plant genomes. More specifically, AgroNT uses the transformer architecture with self-attention and a masked language modeling objective to leverage highly available genotype data from 48 different plant speices to learn…

If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files.