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FitCareer_AI Introduction

A native MLX port of OpenMed/privacy-filter-nemotron, affine-quantized to 8-bit for fast on-device PII detection on Apple Silicon. For the unquantized BF16 reference, see OpenMed/privacy-filter-nemotron-mlx.

# ACE-V1.1: Brain Tumor Detection !Python!Format > [!CAUTION] > MEDICAL RESEARCH USE ONLY. ACE-V1.1 is NOT a cleared medical device. It must not be used for primary diagnosis or clinical decision-making. All outputs must be verified by a qualified professional.

## Introduction (简介) This model is a domain-specific expert fine-tuned from Qwen/Qwen2.5-7B-Instruct using LoRA (Low-Rank Adaptation). It is specifically designed for Fine-grained Information Extraction (IE) of technical indicator quintuples from highly complex lithium-ion battery patents.

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

In pursuit of the universal functional for density functional theory (DFT), the OneDFT team from Microsoft Research AI for Science has developed the Skala-1.0 exchange-correlation functional, as introduced in Accurate and scalable exchange-correlation with deep learning (arXiv v5), Luise et al.

OGR is a foundational model for AI-driven precision breeding and functional genomics in rice. It is a generative genomic foundation model trained to process DNA sequences up to 1 million base pairs in length, with 1.25B total parameters and a Mixture-of-Experts (MoE) architecture.

This repository contains the model used for the paper Bridging Quantum Mechanics to Organic Liquid Properties via a Universal Force Field。

A domain-optimized reasoning model built on DeepSeek-R1-Distill-Qwen-32B, refined through a multi-stage pipeline of GPTQ quantization-aware training and QLoRA fine-tuning. Achieves 84% on MedQA — within 4 points of GPT-4o — in a ~20GB package that fits on a single L40/L40s GPU.

# ModernGENA base ModernGENA is a DNA foundation model based on ModernBERT (a modernized BERT-style encoder architecture) adapted for genomic sequence modeling. ModernGENA base is the 377M-parameter version introduced in the paper Back to BERT in 2026: ModernGENA as a Strong, Efficient Baseline for…

An extended version of SCimilarity, a metric-learning model for single-cell RNA-seq that maps cells to a unified 128-dimensional embedding space. The original model and method are described in:

A 50% magnitude-pruned version of facebook/esm2t1235MUR50D optimized for efficient drug discovery inference on Apple Silicon.

📝 arXiv • 📖 IEEE TNNLS • 🤗 Model • 🧩 Codes

GeneLinguaLM is a multimodal model that generates natural language descriptions of protein functions from amino acid sequences.

Duchifat-2.3-Instruct is a state-of-the-art, instruction-tuned Large Language Model developed by TopAI. As the flagship of the Duchifat series, this model represents a fundamental breakthrough in how Hebrew is processed, reasoned, and generated in the LLM era.

This repository contains the PyTorch model weights for MarS-FM (Markov Space Flow Matching) trained on the MD-CATH dataset. This model was introduced in the ICLR 2026 paper: MarS-FM: Generative Modeling of Molecular Dynamics via Markov State Models.

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

A generalist foundation model for healthcare capable of handling diverse medical data modalities.

A frontier protein-language generative model — because proteins deserve better small talk.

DrugCLIP is a dual-encoder multimodal model (SchNet 3D Graph Neural Network + DistilBERT Text Encoder) mapped to a shared 128-dimensional latent space. It is designed to evaluate and retrieve novel 3D molecular structures by aligning them with natural language therapeutic intents and clinical…

UBio-MolFM is a foundation model suite for molecular modeling, specifically designed for bio-systems. This model, UBio-MolFM-V1 (Stage 3), is built on the E2Former-V2 linear-scaling equivariant transformer architecture. Refer to the technique report for more details: UBio-MolFM (arXiv:2602.17709).

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.

> Note: This checkpoint was donated to Huggingface-science to support open medical AI research

CodonTranslator is a protein-conditioned codon sequence generation model trained on the representative-only data_v3 release.

A frontier protein-language generative model — because proteins deserve better small talk.

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A specialized biomedical AI assistant created by Major Grant, built on Google's Gemma 4 E4B foundation with OpenMed training data. GGUF format for efficient local inference.

## 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.

### Model Overview TabPFN-2.6 is a transformer-based foundation model that uses in-context-learning to solve tabular prediction problems in a forward pass. Inference code can be found at https://github.com/PriorLabs/tabPFN.

This repository contains DNA-sequence modeling resources associated with the basal ganglia (BG) cell atlas package. It serves as a centralized entry point for sequence-based regulatory analyses across multiple companion studies.

🧬 BioReason-ProAdvancing Protein Function Prediction withMultimodal Biological Reasoning

🧬 BioReason-ProAdvancing Protein Function Prediction withMultimodal Biological Reasoning

### Model Overview TabPFN-2.5 is a transformer-based foundation model that uses in-context-learning to solve tabular prediction problems in a forward pass. Inference code can be found at https://github.com/PriorLabs/tabPFN.

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.

EVA is a generative foundation model for universal RNA modeling and design, trained on OpenRNA v1 — a curated atlas of 114 million full-length RNA sequences spanning all domains of life.

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…

A biologically & cosmologically inspired causal language model based on the "Cosmology of the Living Cell" (Mother Theory)

Github | Cite

Github | Cite

This is a finetuned EVO2 model for chromosome classification, trained for 20 epochs.

Fine-tuned BGE-M3 on Chinese medical question-answer retrieval using hard negative mining and triple-path InfoNCE loss (dense + sparse + ColBERT).

This repository provides the YOLO26-based version of MolDetv2 model.

A diffusion language model for genome-scale perturbation prediction across diverse cellular contexts.

This repository comprises a collection of TrajCast models, a framework for forecasting molecular dynamics (MD) trajectories using autoregressive equivariant message-passing networks. Provided with a starting configuration comprising information about atom types, atomic positions, and velocities,…