Search Results for "loading" - Page 4
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Showing 152 open source projects for "loading"
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Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
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$300 Free Credits for Your Google Cloud ProjectsLaunch your next project with $300 in free Google Cloud credits—no strings attached. Test, build, and deploy without risk. Use your credits across the entire Google Cloud platform to find what works best for your needs. After your credits are used, continue with always-free tier services. Only pay when you're ready to scale. Sign up in minutes and start exploring.
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ANYstructure
GUI based steel structure calculation and optimization tool.
GUI based steel optimization tool for naval architects and structural engineers. Calculate and optimize the following: Minimum plate thickness (DNV/DNVGL-OS-C101) Minimum section modulus of stiffener/plate (DNVGL-OS-C101) Minimum shear area (DNVGL-OS-C101) Buckling (DNVGL-RP-C201) Fatigue for plate/stiffener connection (DNVGL-RP-C203) Compartments (tank pressures) are created automatically. Pressures on external hull (or any other generic location) is defined by specifying... -
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PoJamas aims to provide a Python and tools for loading, processing, and producing .cr2, pz3 (crz, pzz) files compatible with the SmithMicro (e-frontier) Poser character animation application. PoJamas is composed of: - Python library - Python Wavefront (.obj) 3D viewer based on GLFW - LibreOffice/Python Application (to ease the library and the viewer usage) As of 2020, the project is ported in Python3 As of 2021 this project proposes a 3D viewer for Wavefront files (pypos3dv) PyPosPosLO v0.15 (and above) provides the means to easily create Poser Mechanical Character. ...
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garysfm
An advanced file manager with qss themes and iso and folder previews
garysfm which stands for Gary's File Manager is a file manager with some advanced features. Those features include bulk renaming and folder image previews. I has rather advanced search functions, tab browsing with persistence between launches. It remembers your folder sorting and view options in icon view. It also remembers your active tabs between sessions. It has progress dialog while doing large operations like copying large files, and folders with many files. python version works on... -
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TensorFlow Hub
A library for transfer learning by reusing parts of TensorFlow models
TensorFlow Hub is a repository that provides a library and platform for publishing, discovering, and reusing pre-trained machine learning models built with TensorFlow. The project enables developers to integrate high-quality models into their applications without needing to train them from scratch. Through TensorFlow Hub, researchers and practitioners can share reusable model components such as image classifiers, text embedding models, and object detection networks. These models can be... -
Train ML Models With SQL You Already KnowBuild and deploy ML models using familiar SQL. Automate data prep with built-in Gemini. Query 1 TB and store 10 GB free monthly.
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RAGxplorer
Open-source tool to visualise your RAG
RAGxplorer is an open-source visualization tool designed to help developers analyze and understand Retrieval-Augmented Generation (RAG) pipelines. Retrieval-augmented generation combines language models with external document retrieval systems in order to produce more accurate and grounded responses. However, RAG systems can be complex because they involve multiple components such as embedding models, vector databases, and retrieval algorithms. RAGxplorer provides visual tools that allow... -
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Mixtral offloading
Run Mixtral-8x7B models in Colab or consumer desktops
...This approach takes advantage of the sparse activation properties of mixture-of-experts architectures, where only a subset of expert networks are used for each token during generation. By selectively loading and caching the required experts, the system avoids keeping the entire model in GPU memory at once. The repository includes notebooks and code examples that demonstrate how to run large language models on consumer hardware such as personal GPUs or cloud notebook environments. -
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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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LLM Applications
A comprehensive guide to building RAG-based LLM applications
LLM Applications is a practical reference repository that demonstrates how to build production-grade applications powered by large language models. The project focuses particularly on Retrieval-Augmented Generation architectures, which combine language models with external knowledge sources to improve accuracy and reliability. It provides step-by-step guidance for constructing systems that ingest documents, split them into chunks, generate embeddings, index them in vector databases, and... -
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Summarize from Feedback
Code for "Learning to summarize from human feedback"
The summarize-from-feedback repository implements the methods from the paper “Learning to Summarize from Human Feedback”. Its purpose is to train a summarization model that better aligns with human preferences by first collecting human feedback (comparisons between summaries) to train a reward model, and then fine-tuning a policy (summarizer) to maximize that learned reward. The code includes different stages: a supervised baseline (i.e. standard summarization training), the reward modeling... -
Go from Code to Production URL in SecondsSkip the Kubernetes configs. Cloud Run handles HTTPS, scaling, and infrastructure automatically. Two million requests free per month.
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Lightning Flash
Flash enables you to easily configure and run complex AI recipes
Your PyTorch AI Factory, Flash enables you to easily configure and run complex AI recipes for over 15 tasks across 7 data domains. In a nutshell, Flash is the production-grade research framework you always dreamed of but didn't have time to build. All data loading in Flash is performed via a from_* classmethod on a DataModule. Which DataModule to use and which from_* methods are available depends on the task you want to perform. For example, for image segmentation where your data is stored in folders, you would use the from_folders method of the SemanticSegmentationData class. Our tasks come loaded with pre-trained backbones and (where applicable) heads. ... -
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sense2vec
Contextually-keyed word vectors
sense2vec (Trask et. al, 2015) is a nice twist on word2vec that lets you learn more interesting and detailed word vectors. This library is a simple Python implementation for loading, querying and training sense2vec models. For more details, check out our blog post. To explore the semantic similarities across all Reddit comments of 2015 and 2019, see the interactive demo. -
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NOW
No-code tool for creating a neural search solution in minutes
...Perhaps your data is stored in an S3 bucket, which is an option NOW also supports. In this case, NOW asks for the URI to the S3 bucket, as well as the credentials and region thereof. A final step in loading your data is to choose the fields of your data that you would like to use for search and filter respectively. -
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FFCV
Fast Forward Computer Vision (and other ML workloads!)
ffcv is a drop-in data loading system that dramatically increases data throughput in model training. From gridding to benchmarking to fast research iteration, there are many reasons to want faster model training. Below we present premade codebases for training on ImageNet and CIFAR, including both (a) extensible codebases and (b) numerous premade training configurations. -
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Merlion
A Machine Learning Framework for Time Series Intelligence
Merlion is a Python library for time series intelligence. It provides an end-to-end machine learning framework that includes loading and transforming data, building and training models, post-processing model outputs, and evaluating model performance. It supports various time series learning tasks, including forecasting, anomaly detection, and change point detection for both univariate and multivariate time series. This library aims to provide engineers and researchers a one-stop solution to rapidly develop models for their specific time series needs, and benchmark them across multiple time series datasets. -
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Bulk Image Optimizer and Converter
Imagine having all your images well compressed and optimized :)
Bulk Image Optimizer and Converter (Portable Executable) It allows users to choose the output format (JPEG, PNG, or WebP), set the desired image quality, and remove EXIF data. The optimized images are saved in a separate folder named "optimized" within the input folder. The tool displays progress information, including the number of images processed, the average compression ratio, and the total space saved. -
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albert_zh
Implementation of A Lite Bert For Self-Supervised Learning Language
...It also provides guidance for fine-tuning downstream tasks such as sentence-pair semantic similarity and Chinese classification benchmarks. The repository includes support paths for TensorFlow, PyTorch conversion, Keras loading, TensorFlow 2.0 loading, and TensorFlow Lite deployment for mobile scenarios. Overall, it is useful for Chinese NLP developers who need compact pretrained language models for classification, similarity, and other language understanding tasks. -
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dein.vim
Dark powered Vim/Neovim plugin manager
dein.vim is a high-performance, feature-rich plugin manager for Vim and Neovim that enables fast startup and efficient plugin loading. It supports managing plugins from GitHub and local directories, with a design that balances speed (like vim-plug) and flexibility (like NeoBundle). Active development has ceased, with only bug fixes expected going forward. Function API and familiar patterns, without commands or dependency hell. Clean asynchronous installation supported. -
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AllenNLP
An open-source NLP research library, built on PyTorch
...AllenNLP includes reference implementations of high quality models for both core NLP problems (e.g. semantic role labeling) and NLP applications (e.g. textual entailment). AllenNLP supports loading "plugins" dynamically. A plugin is just a Python package that provides custom registered classes or additional allennlp subcommands. There is ecosystem of open source plugins, some of which are maintained by the AllenNLP team here at AI2, and some of which are maintained by the broader community. AllenNLP will automatically find any official AI2-maintained plugins that you have installed, but for AllenNLP to find personal or third-party plugins you've installed, you also have to create either a local plugins file named .allennlp_plugins in the directory where you run the allennlp command. -
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Video Pre-Training
Learning to Act by Watching Unlabeled Online Videos
...The repository contains demonstration models of different widths, fine-tuned variants (e.g. for building houses or early-game tasks), and inference scripts that instantiate agents from pretrained weights. Key modules include the behavioral cloning logic, the agent wrapper, and data loading pipelines (with an accessible skeleton for loading Minecraft demonstration data). The repo also includes a run_agent.py script for testing an agent interactively, and an agent.py module encapsulating the control logic. -
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Fairseq
Facebook AI Research Sequence-to-Sequence Toolkit written in Python
...Models define the neural network architecture and encapsulate all of the learnable parameters. Criterions compute the loss function given the model outputs and targets. Tasks store dictionaries and provide helpers for loading/iterating over Datasets, initializing the Model/Criterion and calculating the loss. -
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RoBERTa for Chinese
RoBERTa Chinese pre-training model: RoBERTa for Chinese
...Its training data includes news, community discussion, encyclopedia content, and other broad Chinese text sources. The repository also describes whole word masking for Chinese and provides examples for loading and fine-tuning models on sentence-pair matching tasks. Overall, it is a useful pretrained model resource for developers who want stronger Chinese BERT-style representations for classification, matching, reading comprehension, and related NLP tasks. -
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OpenPrompt
An Open-Source Framework for Prompt-Learning
Prompt-learning is the latest paradigm to adapt pre-trained language models (PLMs) to downstream NLP tasks, which modifies the input text with a textual template and directly uses PLMs to conduct pre-trained tasks. OpenPrompt is a library built upon PyTorch and provides a standard, flexible and extensible framework to deploy the prompt-learning pipeline. OpenPrompt supports loading PLMs directly from huggingface transformers. In the future, we will also support PLMs implemented by other libraries. The template is one of the most important modules in prompt learning, which wraps the original input with textual or soft-encoding sequence. Use the implementations of current prompt-learning approaches.* We have implemented various of prompting methods, including templating, verbalizing and optimization strategies under a unified standard. ... -
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LayoutParser
A Unified Toolkit for Deep Learning Based Document Image Analysis
With the help of state-of-the-art deep learning models, Layout Parser enables extracting complicated document structures using only several lines of code. This method is also more robust and generalizable as no sophisticated rules are involved in this process. A complete instruction for installing the main Layout Parser library and auxiliary components. Learn how to load DL Layout models and use them for layout detection. The full list of layout models currently available in Layout Parser.... -
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MuJoCo-py
mujoco-py allows using MuJoCo from Python 3
...It allows developers and researchers to run detailed rigid body simulations with contacts directly from Python, making MuJoCo easier to integrate into machine learning workflows. The library is compatible with MuJoCo version 2.1 and supports Linux and macOS, while Windows support has been deprecated. It provides utilities for loading models, running simulations, and accessing simulation states in real time, along with visualization tools for rendering environments. The project also includes interactive examples showcasing collision handling, texture randomization, state resetting, and robot control. By bridging MuJoCo with Python, mujoco-py enables rapid prototyping, training, and evaluation of AI agents in physics-rich environments. -
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...Current release solely targets Linux, but has previously run on macOS. Currently released components are: - eda-model: XML schema and schematron that define the storage of a valid design. - eda-model-interface: python3 library for loading, editing, diffing and validating an instance of an EDA design. - edator: The editor/IDE of EDA designs, using Qt/PySide2. - code generator: generate a C++ code base from a CEDAR model. - runtime: C++ and python runtime support files - ruststore: A datastore for the CEDAR runtime
