Open-source alternatives to Alphafold

This project is a collection of pretrained transformer protein language models designed for sequence embedding, structure prediction, variant effect estimation, and generative inverse folding. It provides a framework for transforming amino acid sequences into high-dimensional numerical vectors and predicting biological properties through these embeddings. The system includes a generative model for inverse folding that designs amino acid sequences to fit a specific target protein backbone structure. It also features tools for protein variant analysis that use zero-shot prediction to estimate t

This project provides a comprehensive educational curriculum and research resource for deep learning, focusing on the theoretical and technical foundations of neural network implementation. It serves as a structured academic guide for building and training complex models from scratch, covering the essential mathematical primitives, computational graph construction, and automatic differentiation mechanisms required for modern machine learning. The repository distinguishes itself through its extensive coverage of generative modeling and specialized neural architectures. It includes practical im

Qwen3-Omni is an omni-modal large language model designed to process and generate text, audio, images, and video within a single unified neural architecture. It functions as a real-time voice assistant and multimodal AI agent capable of reasoning across different media types and executing external tool-calling functions via APIs. The system supports low-latency conversational AI through autoregressive token streaming and natural turn-taking. It enables multilingual speech translation and generation across dozens of languages, featuring customizable speaker profiles and tones. The model's cap

GOT-OCR2.0 is an end-to-end optical character recognition system and document text extractor. It utilizes a unified transformer architecture to recognize and extract plain and formatted text from diverse images and documents. The system features a multi-crop processing method that divides high-resolution or dense documents into smaller sections to maintain recognition detail. It also includes a renderer that transforms recognized text into HTML to preserve the original structure and layout of the document. The project provides a framework for fine-tuning pre-trained models on custom datasets

This project is a scientific agent framework and workflow orchestrator designed to extend large language models with specialized tools for genomic, chemical, and biological research. It provides a system for planning research hypotheses and executing automated workflows by integrating scientific databases with dynamic code execution. The framework includes a cheminformatics modeling suite for predicting molecular bioactivity and performing virtual screening, alongside a bioinformatics analysis toolkit for processing genomic sequences and single-cell data. It also features an academic document

AlphaFold3 is a biomolecular structure prediction model and bioinformatics structural analysis tool. It uses a deep learning system to predict the three-dimensional shapes of proteins, DNA, RNA, and ligands. The system functions as a diffusion-based protein folding model that predicts the spatial coordinates of biomolecular atoms and interactions. It utilizes a GPU-accelerated inference pipeline to process genetic sequences and structural templates for molecular modeling. The project covers structural bioinformatics analysis and protein interaction modeling to determine the physical arrangem

TripoSR is a single-image 3D reconstruction system that generates a high-quality textured mesh from one photograph in under half a second. It uses a feedforward neural network to process a single image through a transformer architecture, compressing the input into a compact latent vector that conditions the entire reconstruction pipeline. The system outputs a separate UV texture map with configurable resolution, replacing vertex colors for higher-quality surface detail. The project is built around an end-to-end differentiable pipeline that trains the entire reconstruction system from image in

UniAD is a unified deep learning framework for autonomous driving that integrates perception, prediction, and planning into a single end-to-end model. It functions as a neural network architecture that maps raw sensor data directly to driving trajectories and motion plans. This project serves as a research implementation of a planning-oriented approach that jointly trains occupancy, mapping, and object tracking modules. It employs a multi-task perception framework to optimize overall driving performance. The system covers a broad capability surface including end-to-end driving pipelines, veh

TinyRecursiveModels is a recursive training framework for small neural networks designed to solve complex logical tasks. It functions as a parameter-efficient model trainer and a reasoning dataset generator, enabling the optimization of models that refine their answers through iterative reasoning steps. The framework differentiates itself by utilizing latent-state recursive refinement, where the model maintains and updates an internal hidden representation to improve prediction accuracy over multiple sequential steps. It also includes tools for generating structured training and evaluation da

This project is a PyTorch object detection framework that implements the Faster R-CNN architecture. It serves as a vision model for predicting precise bounding boxes around multiple objects within images and live video feeds. The system is optimized for multi-GPU training to reduce the time required for model convergence. It utilizes a GPU-accelerated design to handle the training and inference of complex detection networks. The framework covers the full object detection lifecycle, including custom network training and inference for static images and real-time video streams. It includes capa

This project is a plugin framework and agentic workflow library designed to connect large language models to professional toolstacks. It provides a system for integrating language models with external data warehouses, CRMs, and other enterprise software to retrieve and manipulate real-time business data. The framework enables the automation of specialized professional tasks through a file-based plugin definition system. It allows for the customization of domain expertise and plugin behavior to align with internal company processes, supported by an enterprise data connector that links models t

graph_nets is a graph-structured deep learning framework and library for building message-passing neural networks. It provides tools for designing architectures that operate on nodes and edges to process and reason about data structured as graphs using TensorFlow. The framework implements a message-passing paradigm for iterative information exchange between nodes. This approach enables the development of models that can reason about complex graph-structured inputs for tasks such as path-finding and sorting, or serve as a predictor for the future states and trajectories of physical systems.

This project is a deep learning framework designed for facial landmark detection. It functions as a computer vision library that provides the necessary routines to locate precise points on human faces within images by generating spatial probability maps. The architecture distinguishes itself through high-resolution parallel branching, which maintains detailed visual representations throughout the entire network. By utilizing multi-scale feature fusion, the model repeatedly exchanges information across these parallel streams to integrate fine-grained spatial details with broader semantic conte

Xan is a command-line tool and data transformation engine for processing CSV, TSV, and JSONL datasets. It functions as a processor for compressed files, enabling random access and seeking within gzipped and Zstd files, and serves as a converter for specialized bioinformatics data formats. The tool handles large datasets without requiring full memory loads by utilizing stream-based processing. It provides capabilities for merging, sorting, and deduplicating massive files, as well as converting data between various tabular formats. The project covers a broad range of data wrangling and analysi

DiffSinger is an AI vocal synthesizer and neural audio generator designed to produce high-fidelity singing and speech. It functions as a text-to-speech system and a diffusion-based singing voice synthesis tool that transforms text and pitch into audible audio. The system utilizes a shallow diffusion mechanism and iterative noise refinement to generate realistic vocal performances. It incorporates specialized sampling plugins and numerical solvers to accelerate inference and reduce the time required to generate synthetic voices. The project covers acoustic modeling, mel-spectrogram synthesis,

This project is a deep learning framework built for detecting and tracking human body keypoints in images and video streams. It functions as both a real-time motion tracking system and a machine learning environment for training and evaluating pose estimation models. The system utilizes a two-branch convolutional neural network to predict body part locations and their directional connections simultaneously. It employs multi-stage feature refinement to improve keypoint localization accuracy and uses greedy parsing and bipartite matching algorithms to associate detected parts into individual sk

This project is a machine learning toolkit designed for the development, training, and deployment of automatic speech recognition engines. It provides a comprehensive framework for converting spoken audio into written text, specifically supporting models trained on Mandarin and English datasets. The library utilizes an end-to-end neural architecture that processes raw audio input directly into character sequences, bypassing the need for intermediate linguistic alignment. It incorporates signal processing techniques to transform sound waves into numerical spectrograms and feature vectors, whic

This repository is a comprehensive collection of instructional guides and practical examples for Python development, focusing on machine learning, data science, and web scraping. It provides implementations for neural networks, reinforcement learning algorithms, and deep learning architectures using PyTorch, alongside detailed manuals for scientific computing and data visualization. The project distinguishes itself by offering specialized tutorials on concurrent programming to optimize CPU performance and guides for setting up Linux development environments. It covers the implementation of ad

Trax is a deep learning framework and hardware-agnostic tensor engine designed for designing and training neural networks. It serves as a research tool providing high-level combinators for composing complex architectures, alongside a dedicated library for building transformer models and a toolkit for reinforcement learning. The framework is distinguished by its support for reversible and sparse transformer architectures, which reduce memory and computational overhead. It enables a single set of model instructions to execute across different hardware backends without changing the underlying co

This repository serves as a comprehensive research platform and toolkit for advancing machine learning, quantum computing, and large-scale scientific data analysis. It provides foundational frameworks for developing complex algorithmic systems, offering the necessary infrastructure for distributed training, computational graph execution, and high-performance model development. The project distinguishes itself by integrating specialized research domains with robust, privacy-preserving methodologies. It supports diverse scientific discovery through tools for quantum simulation, physics-informed

Inverse optimal control for continuous psychophysics

DISCLAIMER: This code is a work in progress and has passed preliminary checks, but might still contain bugs. Thorough testing is yet to come, feedback and questions are very welcome.

NuX is a library for building normalizing flows using JAX.

Transformers is a comprehensive library for machine learning that provides a unified interface for training, fine-tuning, and deploying transformer-based models. It supports a wide range of tasks, including text classification, language modeling, question answering, and sequence-to-sequence translation, while offering specialized architectures for both text and vision processing. The framework includes tools for managing the entire model lifecycle, from data preprocessing and tokenization to distributed training and inference. The library features extensive support for model optimization and

This project is a collection of structured study notes and notebooks serving as an educational resource for deep learning and neural network fundamentals. It provides a technical reference for implementing machine learning theory, covering everything from basic network design to the construction of advanced architectures. The material specifically focuses on the implementation of convolutional neural networks for computer vision and sequence models for natural language processing. It includes detailed guidance on building object detection systems, face recognition, and speech transcription mo

DeepLabCut is a deep learning toolkit for markerless 2D and 3D animal pose estimation. It functions as a motion tracking system that identifies anatomical keypoints on animals in video sequences without the need for physical markers. The framework utilizes transfer learning and a library of pre-trained weights to accelerate the training of networks for different species. It supports multi-individual identity tracking to maintain unique identities across video sequences and offers real-time pose detection for live video feeds. The system covers a broad range of computer vision capabilities, i