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Fine-tuned version of google/gemma-4-E4B-it across three professional domains — Medical, Legal, and Finance — using QLoRA (4-bit NF4) with Optuna-tuned hyperparameters, trained on Kaggle T4 GPU.

L1 (Learning Unit 1) is the first language model from Lunit and Lunit Consortium, purpose-built for the medical domain. Derived from Gravity-16B-A3B-Base, L1 is designed for clinical reasoning and decision support.

## Model Description This is a lightweight, high-performance image classification model built to diagnose histopathological scans of lung and colon tissues. This model was specifically designed for rapid web deployment without sacrificing clinical accuracy.

MarkushGrapher-2 is an end-to-end multimodal model for recognizing chemical structures from patent document images. It jointly encodes vision, text, and layout information to convert Markush structure images into machine-readable CXSMILES representations.

# or·a·cle /ˈôrəkəl/ — a source of wise counsel; one who provides authoritative knowledge. From Latin ōrāculum, meaning divine announcement. In computer science, an oracle is a black box that always returns the correct answer — you don't ask it how it knows, you ask and it answers.

ChemicalOCR is a compact vision-language model fine-tuned specifically for optical character recognition (OCR) in chemical structure images. It extracts text and bounding boxes from molecular drawings, enabling the recognition of atom labels, abbreviations, and descriptive text within chemical…

# GigaHeart ## A Cardiac-specific CT Foundation Model for Heart Transplantation

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RNAElectra is a nucleotide-resolution RNA language model trained using an ELECTRA-style objective for efficient and discriminative representation learning. The model produces contextualized embeddings for RNA sequences and is designed for downstream transcriptomic and regulatory modeling tasks.

Vortex Scientific is a from-scratch AI model family designed for deep scientific reasoning. Built from the ground up with a novel hybrid state-space + attention architecture, optimized for consumer laptop hardware (Apple Silicon MacBooks and Nvidia 4060 laptop GPUs).

In search enginers, rerankers are crucial for improving the accuracy of your retrieval system.

This repository contains the model presented in D3LM: A Discrete DNA Diffusion Language Model for Bidirectional DNA Understanding and Generation.

For a convenient overview and download list, visit our model page for this model.

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Sahal Shaji Mullappilly\, Mohammed Irfan K\, Omair Mohamed, Mohamed Zidan, Fahad Khan, Salman Khan, Rao Muhammad Anwer, and Hisham Cholakkal

A compact protein language model distilled from ProtGPT2 using complementary-regularizer distillation---a method that combines uncertainty-aware position weighting with calibration-aware label smoothing to achieve 31% better perplexity than standard knowledge distillation at 3.8x compression.

A compact protein language model distilled from ProtGPT2 using complementary-regularizer distillation---a method that combines uncertainty-aware position weighting with calibration-aware label smoothing to achieve 54% better perplexity than standard knowledge distillation at 9.4x compression.

A compact protein language model distilled from ProtGPT2 using complementary-regularizer distillation---a method that combines uncertainty-aware position weighting with calibration-aware label smoothing to achieve 87% better perplexity than standard knowledge distillation at 20x compression.

🌟 Github | 📥 Model Download | 📚 Dataset | 📄 Paper Link | 🌐 Project Page

From Inquiry to Decision: Building Trustworthy Medical AI

A Neural Machine Translation (NMT) model based on a custom Transformer (Encoder-Decoder) architecture, trained from scratch. This model is designed to translate English sentences into Hebrew using multilingual encoding and specialized layer configurations.

WiNGPT 是一个基于GPT的医疗垂直领域大模型，旨在将专业的医学知识、医疗信息、数据融会贯通，为医疗行业提供智能化的医疗问答、诊断支持和医学知识等信息服务，提高诊疗效率和医疗服务质量。

Matěj Pekár, Vít Musil, Rudolf Nenutil, Petr Holub, Tomáš Brázdil

AVAILABLE NOW THE LATEST ITERATION OF THE ALOE FAMILY! ALOE BETA 8B AND ALOE BETA 70B VERSIONS. These include: Better overall performance More thorough alignment and safety * License compatible with more uses

Aloe: A Family of Fine-tuned Open Healthcare LLMs

Unsloth Dynamic 2.0 achieves superior accuracy & outperforms other leading quants.

PII Detection Model | 44M Parameters | Open Source

PII Detection Model | 434M Parameters | Open Source

This is a MobileViT (Small) model fine-tuned on the Processed Diabetic Retinopathy dataset.

> [!NOTE] > Inspired by the thought of: what if you could speak to an offline medical assistant that doesn't decline to answer some of your questions?

The MediPhi Model Collection comprises 7 small language models of 3.8B parameters from the base model Phi-3.5-mini-instruct specialized in the medical and clinical domains. The collection is designed in a modular fashion. Five MediPhi experts are fine-tuned on various medical corpora (i.e.

The MediPhi Model Collection comprises 7 small language models of 3.8B parameters from the base model Phi-3.5-mini-instruct specialized in the medical and clinical domains. The collection is designed in a modular fashion. Five MediPhi experts are fine-tuned on various medical corpora (i.e.

The MediPhi Model Collection comprises 7 small language models of 3.8B parameters from the base model Phi-3.5-mini-instruct specialized in the medical and clinical domains. The collection is designed in a modular fashion. Five MediPhi experts are fine-tuned on various medical corpora (i.e.

The MediPhi Model Collection comprises 7 small language models of 3.8B parameters from the base model Phi-3.5-mini-instruct specialized in the medical and clinical domains. The collection is designed in a modular fashion. Five MediPhi experts are fine-tuned on various medical corpora (i.e.

The MediPhi Model Collection comprises 7 small language models of 3.8B parameters from the base model Phi-3.5-mini-instruct specialized in the medical and clinical domains. The collection is designed in a modular fashion. Five MediPhi experts are fine-tuned on various medical corpora (i.e.

## Description: Geneformer is a foundational transformer model pretrained on a large-scale corpus of single-cell transcriptomes to enable context-specific predictions in settings with limited data in network biology.

## Description: Geneformer is a foundational transformer model pretrained on a large-scale corpus of single-cell transcriptomes to enable context-specific predictions in settings with limited data in network biology. This model version was continually pretrained on ~14 million cancer transcriptomes…

## Description: Geneformer is a foundational transformer model pretrained on a large-scale corpus of single-cell transcriptomes to enable context-specific predictions in settings with limited data in network biology.

## Description: Geneformer is a foundational transformer model pretrained on a large-scale corpus of single-cell transcriptomes to enable context-specific predictions in settings with limited data in network biology.

For a convenient overview and download list, visit our model page for this model.