Search Results for "python windows" - Page 77
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Showing 2809 open source projects for "python windows"
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DragGAN
Official Code for DragGAN (SIGGRAPH 2023)
DragGAN is a research-driven image editing system that enables precise manipulation of GAN-generated images through interactive point dragging. The project introduces a novel workflow where users move specific points in an image and the model intelligently deforms the content while preserving realism. Built on top of StyleGAN architectures, the tool operates directly on the learned generative manifold to maintain photorealistic consistency. It combines feature-based motion supervision with a... -
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MMAction2
OpenMMLab's Next Generation Video Understanding Toolbox and Benchmark
OpenMMLab's next generation video understanding toolbox and benchmark. MMAction2 is an open-source toolbox for video understanding based on PyTorch. It is a part of the OpenMMLab project. Modular design: We decompose a video understanding framework into different components. One can easily construct a customized video understanding framework by combining different modules. Support four major video understanding tasks: MMAction2 implements various algorithms for multiple video understanding... -
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llama2-webui
Run any Llama 2 locally with gradio UI on GPU or CPU from anywhere
Running Llama 2 with gradio web UI on GPU or CPU from anywhere (Linux/Windows/Mac). -
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SoftVC VITS Singing Voice Conversion
SoftVC VITS Singing Voice Conversion
SoftVC VITS Singing Voice Conversion is a deep learning project focused on singing voice conversion, allowing users to transform one voice into another while preserving melody and timing. Unlike traditional text-to-speech systems, it specializes specifically in singing scenarios and does not provide general TTS functionality. The project leverages neural network architectures derived from VITS and SoftVC research to achieve high-quality voice transformation. It is commonly used in creative... -
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ChatFred
Alfred workflow using ChatGPT, DALL·E 2 and other models for chatting
Alfred workflow using ChatGPT, DALL·E 2 and other models for chatting, image generation and more. Access ChatGPT, DALL·E 2, and other OpenAI models. Language models often give wrong information. Verify answers if they are important. Talk with ChatGPT via the cf keyword. Answers will show as Large Type. Alternatively, use the Universal Action, Fallback Search, or Hotkey. To generate text with InstructGPT models and see results in-line, use the cft keyword. ⤓ Install on the Alfred Gallery or... -
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ReplitLM
Inference code and configs for the ReplitLM model family
ReplitLM is a family of open-source language models developed by Replit for assisting with programming tasks such as code generation and completion. The project includes model checkpoints, configuration files, and example code that enable developers to run and experiment with the models locally or within machine learning frameworks. These models are designed specifically for coding workflows and are trained on large datasets of source code covering many programming languages and development... -
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Lyrebird
Simple and powerful voice changer for Linux, written with Python & GTK
Simple and powerful voice changer for Linux, written with Python & GTK. -
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LLM Training Puzzles
What would you do with 1000 H100s
LLM Training Puzzles is a notebook-based collection of eight exercises about training large neural networks across many GPUs. It introduces the core primitives behind distributed language model training without requiring access to a thousand-machine cluster. The puzzles focus on memory efficiency, communication, partitioning, and compute pipelining. Learners work through simplified scenarios that expose the tradeoffs hidden inside large training systems. The hands-on format emphasizes... -
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PyTorch Implementation of SDE Solvers
Differentiable SDE solvers with GPU support and efficient sensitivity
This library provides stochastic differential equation (SDE) solvers with GPU support and efficient backpropagation. examples/demo.ipynb gives a short guide on how to solve SDEs, including subtle points such as fixing the randomness in the solver and the choice of noise types. examples/latent_sde.py learns a latent stochastic differential equation, as in Section 5 of [1]. The example fits an SDE to data, whilst regularizing it to be like an Ornstein-Uhlenbeck prior process. The model can be... -
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Llama 2 Everywhere (L2E)
Llama 2 Everywhere (L2E)
Llama 2 Everywhere (L2E) is an open-source implementation of the LLaMA-2 large language model architecture designed to demonstrate how transformer-based language models can be executed with extremely minimal code. The project focuses on simplicity and educational clarity by implementing inference for LLaMA-style models in a compact C program rather than relying on large machine learning frameworks. Developers can train models using a Python training pipeline and then run inference using a... -
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Graph of Thoughts
Official Implementation of "Graph of Thoughts
Graph of Thoughts is an open-source framework that implements a novel reasoning paradigm for large language models by organizing reasoning steps as a structured graph instead of a simple linear chain. Traditional reasoning methods such as chain-of-thought generate sequential reasoning steps, but Graph of Thoughts introduces a more flexible structure where multiple reasoning paths can be explored and evaluated simultaneously. In this framework, problems are modeled as a graph of operations... -
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Demucs
Code for the paper Hybrid Spectrogram and Waveform Source Separation
Demucs (Deep Extractor for Music Sources) is a deep-learning framework for music source separation—extracting individual instrument or vocal tracks from a mixed audio file. The system is based on a U-Net-like convolutional architecture combined with recurrent and transformer elements to capture both short-term and long-term temporal structure. It processes raw waveforms directly rather than spectrograms, allowing for higher-quality reconstruction and fewer artifacts in separated tracks. The... -
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Super Easy AI Installer Tool
Application that simplifies the installation of AI-related projects
"Super Easy AI Installer Tool" is a user-friendly application that simplifies the installation process of AI-related repositories for users. The tool is designed to provide an easy-to-use solution for accessing and installing AI repositories with minimal technical hassle to none the tool will automatically handle the installation process, making it easier for users to access and use AI tools. "Super Easy AI Installer Tool" is currently in early development phase and may have a few bugs. But... -
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YoloV3 Implemented in TensorFlow 2.0
YoloV3 Implemented in Tensorflow 2.0
YoloV3 Implemented in TensorFlow 2.0 is built using TensorFlow 2.0. The project provides a modern deep learning implementation of the popular YOLOv3 algorithm, which is widely used for real-time object detection in images and video streams. YOLOv3 works by dividing an image into grid regions and predicting bounding boxes and class probabilities simultaneously, allowing objects to be detected quickly and efficiently. The repository includes training scripts, inference tools, and configuration... -
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Complete Machine Learning Package
A comprehensive machine learning repository containing 30+ notebooks
Complete Machine Learning Package repository is a comprehensive educational collection of machine learning notebooks designed to teach core data science and AI concepts through practical coding examples. The project includes more than thirty notebooks that cover a wide range of topics including data analysis, statistical modeling, neural networks, and deep learning. Each notebook introduces theoretical ideas and then demonstrates how to implement them using Python libraries commonly used in... -
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MusicLM - Pytorch
Implementation of MusicLM music generation model in Pytorch
Implementation of MusicLM, Google's new SOTA model for music generation using attention networks, in Pytorch. They are basically using text-conditioned AudioLM, but surprisingly with the embeddings from a text-audio contrastive learned model named MuLan. MuLan is what will be built out in this repository, with AudioLM modified from the other repository to support the music generation needs here. -
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fastquant
Backtest and optimize your ML trading strategies with only 3 lines
fastquant is a Python library designed to simplify quantitative financial analysis and algorithmic trading strategy development. The project focuses on making backtesting accessible by providing a high-level interface that allows users to test investment strategies with only a few lines of code. It integrates historical market data sources and trading frameworks so that users can quickly build experiments without constructing complex data pipelines. The framework enables users to test common... -
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CTransformers
Python bindings for the Transformer models implemented in C/C++
Python bindings for the Transformer models implemented in C/C++ using GGML library. -
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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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HttpRunner
Testing framework that began with API and performance testing
HttpRunner is an open-source testing framework that began with API and performance testing and has evolved into a general, extensible test platform. The current major version is implemented in Go, with the legacy Python edition split to a separate repository; this shift emphasizes a single, fast, cross-platform runtime for modern pipelines. It provides declarative test cases, data-driven parametrization, and plugin mechanisms so teams can compose reusable steps and validations at scale.... -
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Neural Network Intelligence
AutoML toolkit for automate machine learning lifecycle
Neural Network Intelligence is an open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning. NNI (Neural Network Intelligence) is a lightweight but powerful toolkit to help users automate feature engineering, neural architecture search, hyperparameter tuning and model compression. The tool manages automated machine learning (AutoML) experiments, dispatches and runs experiments'... -
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Medusa
Framework for Accelerating LLM Generation with Multiple Decoding Heads
Medusa is a framework aimed at accelerating the generation capabilities of Large Language Models (LLMs) by employing multiple decoding heads. This approach allows for parallel processing during text generation, significantly enhancing throughput and reducing response times. Medusa is designed to be simple to implement and integrates with existing LLM infrastructures, making it a practical solution for scaling LLM applications. -
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Sweep AI
Sweep: AI-powered Junior Developer for small features and bug fixes
Let Sweep handle your tech debt so you can focus on the exciting problems. Sweep is an AI junior developer that transforms bug reports & feature requests into code changes. Describe bugs, small features, and refactors like you would to a junior developer and Sweep. -
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Petals
Run 100B+ language models at home, BitTorrent-style
Run 100B+ language models at home, BitTorrent‑style. Run large language models like BLOOM-176B collaboratively — you load a small part of the model, then team up with people serving the other parts to run inference or fine-tuning. Single-batch inference runs at ≈ 1 sec per step (token) — up to 10x faster than offloading, enough for chatbots and other interactive apps. Parallel inference reaches hundreds of tokens/sec. Beyond classic language model APIs — you can employ any fine-tuning and... -
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ControlNet
Let us control diffusion models
ControlNet is a neural network architecture designed to add conditional control to text-to-image diffusion models. Rather than training from scratch, ControlNet “locks” the weights of a pre-trained diffusion model and introduces a parallel trainable branch that learns additional conditions—like edges, depth maps, segmentation, human pose, scribbles, or other guidance signals. This allows the system to control where and how the model should focus during generation, enabling users to steer...
