Search Results for "research" - Page 65
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Showing 15059 open source projects for "research"
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DeepSeek MoE
Towards Ultimate Expert Specialization in Mixture-of-Experts Language
DeepSeek-MoE (“DeepSeek MoE”) is the DeepSeek open implementation of a Mixture-of-Experts (MoE) model architecture meant to increase parameter efficiency by activating only a subset of “expert” submodules per input. The repository introduces fine-grained expert segmentation and shared expert isolation to improve specialization while controlling compute cost. For example, their MoE variant with 16.4B parameters claims comparable or better performance to standard dense models like DeepSeek 7B... -
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BIG-bench
Beyond the Imitation Game collaborative benchmark for measuring
...Tasks are intentionally heterogeneous: some are multiple-choice with exact scoring, others are free-form generation judged by model-based or human evaluation. The suite provides a common JSON task format and an evaluation harness so research groups can contribute new tasks and reproduce results consistently. It emphasizes robustness analysis—looking at scale trends, calibration, and areas where models systematically fail—to guide model development beyond raw accuracy. BIG-bench is as much a community process as a dataset, encouraging open sharing of tasks and findings to keep evaluations fresh and comprehensive. -
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Wikipedia2Vec
A tool for learning vector representations of words and entities
Wikipedia2Vec is an embedding learning tool that creates word and entity vector representations from Wikipedia, enabling NLP models to leverage structured and contextual knowledge. -
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A Virtual Machine based on a Simplified Instructional Computer (SIC). The Machine will emulate all the hardware of a SIC and will be able to run any program that is written for the SIC as described by Leland L.Beck in his book "System Software"
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ADAMS
ADAMS is a workflow engine for building complex knowledge workflows.
ADAMS is a flexible workflow engine aimed at quickly building and maintaining data-driven, reactive workflows, easily integrated into business processes. Instead of placing operators on a canvas and manually connecting them, a tree structure and flow control operators determine how data is processed (sequentially/parallel). This allows rapid development and easy maintenance of large workflows, with hundreds or thousands of operators. Operators include machine learning (WEKA, MOA, MEKA)... -
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TRACK code
Code for finding the transport properties using Kubo formalism.
...It is possible to use the relaxation time approximation in this code with the eigenvalues of DFT method. In present version, the code is interfaced with WIEN2k and eDMFTF codes. Please cite the paper mentioned below while using the TRACK code for your research. A. Sihi and S. K. Pandey, Comput. Phys. Commun. 285, 108640 (2023). Link of the paper: https://doi.org/10.1016/j.cpc.2022.108640 -
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SSD in PyTorch 1.0
High quality, fast, modular reference implementation of SSD in PyTorch
This repository implements SSD (Single Shot MultiBox Detector). The implementation is heavily influenced by the projects ssd.pytorch, pytorch-ssd and maskrcnn-benchmark. This repository aims to be the code base for research based on SSD. Multi-GPU training and inference: We use DistributedDataParallel, you can train or test with arbitrary GPU(s), the training schema will change accordingly. Add your own modules without pain. We abstract backbone, Detector, BoxHead, BoxPredictor, etc. You can replace every component with your own code without changing the code base. ... -
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Extended Dreambooth How-To Guides
Implementation of Dreambooth
Extended Dreambooth How-To Guides is an implementation and extended toolkit for fine-tuning Stable Diffusion models using the DreamBooth technique, enabling users to train AI image generators to reproduce specific subjects, styles, or identities from a small set of reference images. The project adapts and expands upon earlier DreamBooth research by providing practical scripts, notebooks, and workflows that allow users to train personalized models on local machines, cloud environments, or platforms such as Google Colab. It focuses heavily on usability, offering detailed guides for different setups while still exposing advanced configuration options for experienced users. ... -
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Sonnet
TensorFlow-based neural network library
Sonnet is a neural network library built on top of TensorFlow designed to provide simple, composable abstractions for machine learning research. Sonnet can be used to build neural networks for various purposes, including different types of learning. Sonnet’s programming model revolves around a single concept: modules. These modules can hold references to parameters, other modules and methods that apply some function on the user input. There are a number of predefined modules that already ship with Sonnet, making it quite powerful and yet simple at the same time. ... -
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OnionSearch
Search multiple Tor .onion engines at once and collect hidden links.
...This approach helps researchers and investigators locate hidden services more efficiently without manually querying each individual search engine. It is primarily intended for educational use and open-source intelligence (OSINT) research involving the Tor network. OnionSearch supports multiple engines and can combine results into a single output, making it easier to analyze discovered onion links. It also offers flexible command-line options that allow users to limit results, choose which engines to query, and export collected data. By automating searches across several dark web search engines, OnionSearch simplifies the process of discovering information on hidden services. -
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RWKV
RNN with great LLM performance
RWKV-LM is the main research and training repository for the RWKV language model architecture. It presents RWKV as an attention-free RNN-style model that aims to reach transformer-level language model performance. The project is built around the idea that a model can be trained in a parallelizable way like a GPT-style transformer while running inference with recurrent efficiency. -
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Privacy and Anonymity in the Internet
System for anonymous Web browsing
Our goal is to develop, implement, evaluate and provide a secure and scaleable technical infrastructure for anonymous communication. This project is supported by Deutsche Forschungsgemeinschaft (DFG). -
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LLaMA-MoE
Building Mixture-of-Experts from LLaMA with Continual Pre-training
...The project is not just a model release, but also a research framework that includes multiple expert construction methods, several gating strategies, and tooling for continual pre-training on filtered SlimPajama-based datasets. It also emphasizes training efficiency through features such as FlashAttention-v2 integration and fast streaming dataset loading, which are important for large-scale experimentation. -
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Habfuzz
A command-line tool for data-driven fuzzy modelling
Input 1 - A training dataset (multiple observations) of up to four variables (predictors) against one (response variable) Input 2 - A test dataset (multiple observations) of the same four variables with unknown response variable Output - Calculation of the response variable for each test observation using fuzzy logic or fuzzy rule-based Bayesian algorithms HABFUZZ is a habitat model, which can be used in ecohydraulic modelling applications for the calculation of the instream habitat... -
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WoPeD is a Java-based graphical workflow process editor, simulator and analysor using Petri Nets and supporting the PNML format.
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InternLM
Official release of InternLM series
InternLM is an open-source family of multilingual foundation and chat models, accompanied by an ecosystem that supports training, inference, and application development. The repository highlights multiple model sizes intended to serve different needs, from efficient research and prototyping to more capable deployments for complex scenarios. Beyond model weights, the project emphasizes an ecosystem view, pointing developers to compatible tools and projects across training and inference so teams can build end-to-end workflows. InternLM’s direction includes strong general-purpose capabilities and ongoing iterations that target improved reasoning, coding, and tool-use behaviors. ... -
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ALAMODE
Ab initio simulator for thermal transport and lattice anharmonicity
ALAMODE is designed for analyzing lattice anharmonicity and lattice thermal conductivity of solids. By using an external DFT package such as VASP and Quantum ESPRESSO, you can extract harmonic and anharmonic force constants straightforwardly with ALAMODE. Using the anharmonic force constants, you can also calculate lattice thermal conductivity from first principles. For more information about ALAMODE, please visit the following webpages: Documentation :... -
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Despite the substantial progress facilitated by bioinformatics tool development, some of them are tailored for specific analytical objectives and code-based, and challenges arise when non-bioinformaticians users need to integrate the outputs of these specific tools into a personalized and appropriate pipeline for their research demands. To address this, we developed BioPipeline Creator, a user-friendly Java-based GUI for managing and customizing biological data pipelines according to end-user final demand.
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GPT-2 Output Dataset
Dataset of GPT-2 outputs for research in detection, biases, and more
...The repository provides scripts and metadata for working with the dataset, with the goal of supporting research in areas like detection, evaluation of text coherence, and analysis of generative models. While no active development is expected, the dataset remains a useful benchmark for tasks involving text classification, style analysis, and generative model evaluation. -
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DeepSee
Visualize deep ocean biogeochemical sediment samples in 2D and 3D!
DeepSee is an interactive workspace for deep ocean scientists to upload sediment core data and map images and see their sampling history displayed across multiple connected views at once! 🦑🦀🐚 🚀 For a live demo, visit: https://www.its.caltech.edu/~datavis/deepsee/ 🌱 To get started, visit our Wiki: https://sourceforge.net/p/deepsee/wiki/Home/ 🧑💻 To modify DeepSee for your own project, visit our GitHub repository: https://github.com/orphanlab/DeepSee/ --- Created by Adam Coscia,... -
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Alpaca-CoT
We unified the interfaces of instruction-tuning data
Alpaca-CoT is an open research project focused on improving reasoning capabilities in language models through chain-of-thought training data. The project builds upon the Alpaca instruction-tuning approach by introducing datasets and methods that encourage models to produce intermediate reasoning steps when solving problems. Instead of generating answers directly, the model learns to produce logical reasoning sequences that lead to the final solution. -
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Basic features: 1. Generate KPOINTS, POTCAR and INCAR for a given POSCAR file; 2. Elastic-constants using stress-strain or energy-strain methods; 3. Equation-of-state fitting; 4. Suggested k-paths for a given crystal structure; 5. Optical adsorption coefficient; 6. Band structure unfolding; 7. Fermi surface; 8. Density-of-states and band-structure; 9. Charge/spin density, Charge density difference; 10. Vacuum level and work function; 11. Wave-function...
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myColex is a complete museum management and collection documentation system with longterm archiving capabilities based on Apache/PHP and mySQL. Manage your object inventories, events, pictures, addresses e.g. with your browser.
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Grasscutter
A server software reimplementation for a certain anime game
...Grasscutter is most relevant to developers interested in reverse engineering, server emulation, game protocol research, and modding communities. Because it interacts with proprietary game clients and services, it should be used only in lawful, authorized, and non-commercial contexts. -
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GrowthRates
Calculate growth rates from microplate reader output
The Bellingham Research Institute (BRI) develops and distributes software that is free of charge to academic and other non-profit organizations. GrowthRates is our software tool that simplifies the analysis of microplate reader output from microbial growth experiments without having to program in Python, R, or MATLAB. It automates the calculation of growth rate parameters, including rates, lag times, maximum population density.
