Search Results for "source code claude code" - Page 33
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Showing 2474 open source projects for "source code claude code"
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Semantix
Non-Pydantic, Non-JSON Schema, efficient AutoPrompting
Semantix empowers developers to infuse meaning into their code through enhanced variable typing (semantic typing). By leveraging the power of large language models (LLMs) behind the scenes, Semantix transforms ordinary functions into intelligent, context-aware operations without explicit LLM calls. -
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Xorbits Inference
Replace OpenAI GPT with another LLM in your app
Replace OpenAI GPT with another LLM in your app by changing a single line of code. Xinference gives you the freedom to use any LLM you need. With Xinference, you're empowered to run inference with any open-source language models, speech recognition models, and multimodal models, whether in the cloud, on-premises, or even on your laptop. Xorbits Inference(Xinference) is a powerful and versatile library designed to serve language, speech recognition, and multimodal models. ... -
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SageMaker Hugging Face Inference Toolkit
Library for serving Transformers models on Amazon SageMaker
SageMaker Hugging Face Inference Toolkit is an open-source library for serving Transformers models on Amazon SageMaker. This library provides default pre-processing, predict and postprocessing for certain Transformers models and tasks. It utilizes the SageMaker Inference Toolkit for starting up the model server, which is responsible for handling inference requests. For the Dockerfiles used for building SageMaker Hugging Face Containers, see AWS Deep Learning Containers. The SageMaker Hugging... -
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PythonPark
Python open source project "The Road to Self-Study Programming"
PythonPark is a large, curated “learning playground” for Python — essentially a comprehensive self-study meta-repository aimed at helping learners progress in Python programming, data science, machine learning, web scraping, and software engineering practices. It aggregates tutorials, learning guides, project examples, and resources across topics: from Python basics and data structures to machine learning, web scraping, and even interview preparation and “programmer life” guidance. Because... -
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Upscale-A-Video
Temporal-Consistent Diffusion Model for Real-World Video
Upscale-A-Video is a diffusion-based video super-resolution project from the CVPR 2024 Highlight paper “Temporal-Consistent Diffusion Model for Real-World Video Super-Resolution.” It upscales low-resolution videos while using text prompts to guide the enhancement process. The model is designed for real-world videos where compression artifacts, blur, aging, or generated-video defects can make ordinary upscaling less reliable. The repository includes inference code, example inputs,... -
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authentik
The authentication glue you need
authentik is an open-source Identity Provider that emphasizes flexibility and versatility. It can be seamlessly integrated into existing environments to support new protocols. authentik is also a great solution for implementing sign-up, recovery, and other similar features in your application, saving you the hassle of dealing with them. authentik is an open-source Identity Provider focused on flexibility and versatility. You can use authentik in an existing environment to add support for new... -
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BackAnt CLI
Let your AI agent create clean, easy to maintain flask backends
BackAnt CLI is an AI-native backend scaffolding tool designed to generate production-ready Flask APIs from a JSON specification or a small set of CLI commands. Its main goal is to reduce the repetitive work involved in setting up layered backend projects by generating key application components automatically rather than forcing developers to assemble them by hand. The tool creates a structured backend architecture that includes routes, services, repositories, models, startup files, Docker... -
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CycleGAN and pix2pix in PyTorch
Image-to-Image Translation in PyTorch
CycleGAN and pix2pix in PyTorch repository is a PyTorch implementation of two influential image-to-image translation frameworks: CycleGAN (for unpaired translation) and pix2pix (for paired translation). This repo gives developers and researchers a convenient, modern (PyTorch-based) platform to train and test these methods — supporting both paired datasets (input to output) and unpaired datasets (domain-to-domain) with minimal changes. The code supports standard training and inference... -
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Top Deep Learning Projects
A list of popular github projects related to deep learning
TopDeepLearning is a curated index of the most popular GitHub projects related to deep learning, ranked by their star count. Rather than being a library itself, it serves as a curated roadmap and reference guide for anyone exploring the deep learning ecosystem — from beginners to experienced practitioners. By aggregating high-star projects across frameworks (TensorFlow, PyTorch), tools (computer vision, NLP, reinforcement learning), tutorials, and research code, it helps users quickly... -
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FastKoko
Dockerized FastAPI wrapper for Kokoro-82M text-to-speech model
FastKoko is a self-hosted text-to-speech server built around the Kokoro-82M model and exposed through a FastAPI backend. It is designed to be easy to deploy via Docker, with separate CPU and GPU images so that users can choose between pure CPU inference and NVIDIA GPU acceleration. The project exposes an OpenAI-compatible speech endpoint, which means existing code that talks to the OpenAI audio API can often be pointed at a Kokoro-FastAPI instance with minimal changes. It supports multiple... -
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AWS SDK for pandas
Easy integration with Athena, Glue, Redshift, Timestream, Neptune
aws-sdk-pandas (formerly AWS Data Wrangler) bridges pandas with the AWS analytics stack so DataFrames flow seamlessly to and from cloud services. With a few lines of code, you can read from and write to Amazon S3 in Parquet/CSV/JSON/ORC, register tables in the AWS Glue Data Catalog, and query with Amazon Athena directly into pandas. The library abstracts efficient patterns like partitioning, compression, and vectorized I/O so you get performant data lake operations without hand-rolling... -
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verl
Volcano Engine Reinforcement Learning for LLMs
VERL is a reinforcement-learning–oriented toolkit designed to train and align modern AI systems, from language models to decision-making agents. It brings together supervised fine-tuning, preference modeling, and online RL into one coherent training stack so teams can move from raw data to aligned policies with minimal glue code. The library focuses on scalability and efficiency, offering distributed training loops, mixed precision, and replay/buffering utilities that keep accelerators busy.... -
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TextFSM
Python module for parsing semi-structured text into python tables
TextFSM is a Python library created by Google that provides a template-based state machine engine for parsing semi-structured text. It is particularly useful for extracting structured data from command-line interface (CLI) outputs, such as those from network devices, routers, and switches. By defining parsing logic through reusable template files, TextFSM transforms unstructured text into structured data like lists or tables without requiring complex regular expression code. Each template... -
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Google DeepMind GraphCast and GenCast
Global weather forecasting model using graph neural networks and JAX
GraphCast, developed by Google DeepMind, is a research-grade weather forecasting framework that employs graph neural networks (GNNs) to generate medium-range global weather predictions. The repository provides complete example code for running and training both GraphCast and GenCast, two models introduced in DeepMind’s research papers. GraphCast is designed to perform high-resolution atmospheric simulations using the ERA5 dataset from ECMWF, while GenCast extends the approach with... -
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Mesh R-CNN
code for Mesh R-CNN, ICCV 2019
Mesh R-CNN is a 3D reconstruction and object understanding framework developed by Facebook Research that extends Mask R-CNN into the 3D domain. Built on top of Detectron2 and PyTorch3D, Mesh R-CNN enables end-to-end 3D mesh prediction directly from single RGB images. The model learns to detect, segment, and reconstruct detailed 3D mesh representations of objects in natural images, bridging the gap between 2D perception and 3D understanding. Unlike voxel-based or point-based approaches, Mesh... -
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vJEPA-2
PyTorch code and models for VJEPA2 self-supervised learning from video
VJEPA2 is a next-generation self-supervised learning framework for video that extends the “predict in representation space” idea from i-JEPA to the temporal domain. Instead of reconstructing pixels, it predicts the missing high-level embeddings of masked space-time regions using a context encoder and a slowly updated target encoder. This objective encourages the model to learn semantics, motion, and long-range structure without the shortcuts that pixel-level losses can invite. The... -
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cheat.sh
The only cheat sheet you need
cheat.sh is a compact, network-accessible cheat-sheet service that serves concise examples and usage notes for hundreds of shell commands, programming languages, and tools via a simple HTTP interface. You can query it from the terminal (for example curl cht.sh/rsync or curl cheat.sh/ls) or browse the web front page; it also supports a shorthand hostname (cht.sh) and provides both online and standalone/local installation modes. The repository contains the server and client code, instructions... -
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Flama
Fire up your models with the flame
Flama is a python library which establishes a standard framework for development and deployment of APIs with special focus on machine learning (ML). The main aim of the framework is to make ridiculously simple the deployment of ML APIs, simplifying (when possible) the entire process to a single line of code. The library builds on Starlette, and provides an easy-to-learn philosophy to speed up the building of highly performant GraphQL, REST and ML APIs. Besides, it comprises an ideal solution... -
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TurboGears
Python web framework with full-stack layer
TurboGears is a hybrid web framework able to act both as a Full Stack framework or as a Microframework. TurboGears helps you get going fast and gets out of your way when you want it! TurboGears can be used both as a full stack framework or as a microframework in single-file mode. TurboGears 2 is built on top of the experience of several next-generation web frameworks including TurboGears 1 (of course), Django, and Rails. All of these frameworks had limitations that frustrated us, and TG2 was... -
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Qiling
Qiling Advanced Binary Emulation Framework
Cross-platform and multi-arch ultra lightweight emulator. Supported OS: Linux, MacOS, Windows, FreeBSD, DOS and UEFI. Support Arch: x86(16/32/64), ARM(64) MIPS, EVM and WASM. It also support Linux Kernel Module(.ko) , Windows Driver(.sys) and MacOS Kernel(.kext) via Demigod. Binary instrumentation and API are Qiling Framework's main focus and priority. It is designed for reverse engineers - thus there is no need to rebuild another sand boxing tool. Using Qiling Framework saves you time. The... -
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Thinc
A refreshing functional take on deep learning
Thinc is a lightweight deep learning library that offers an elegant, type-checked, functional-programming API for composing models, with support for layers defined in other frameworks such as PyTorch, TensorFlow and MXNet. You can use Thinc as an interface layer, a standalone toolkit or a flexible way to develop new models. Previous versions of Thinc have been running quietly in production in thousands of companies, via both spaCy and Prodigy. We wrote the new version to let users compose,... -
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AutoGluon
AutoGluon: AutoML for Image, Text, and Tabular Data
AutoGluon enables easy-to-use and easy-to-extend AutoML with a focus on automated stack ensembling, deep learning, and real-world applications spanning image, text, and tabular data. Intended for both ML beginners and experts, AutoGluon enables you to quickly prototype deep learning and classical ML solutions for your raw data with a few lines of code. Automatically utilize state-of-the-art techniques (where appropriate) without expert knowledge. Leverage automatic hyperparameter tuning,... -
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SageMaker Training Toolkit
Train machine learning models within Docker containers
Train machine learning models within a Docker container using Amazon SageMaker. Amazon SageMaker is a fully managed service for data science and machine learning (ML) workflows. You can use Amazon SageMaker to simplify the process of building, training, and deploying ML models. To train a model, you can include your training script and dependencies in a Docker container that runs your training code. A container provides an effectively isolated environment, ensuring a consistent runtime and... -
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Vedo
A python module for scientific analysis of 3D data
A lightweight and powerful python module for scientific analysis and visualization of 3d objects. Inspired by the vpython manifesto "3D programming for ordinary mortals", vedo makes it easy to work with 3D pointclouds, meshes and volumes, in just a few lines of code, even for less experienced programmers. vedo is based on VTK and numpy, with no other dependencies. Import meshes from VTK format, STL, Wavefront OBJ, 3DS, Dolfin-XML, Neutral, GMSH, OFF, PCD (PointCloud). Export meshes as ASCII... -
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Recommenders
Best practices on recommendation systems
The Recommenders repository provides examples and best practices for building recommendation systems, provided as Jupyter notebooks. The module reco_utils contains functions to simplify common tasks used when developing and evaluating recommender systems. Several utilities are provided in reco_utils to support common tasks such as loading datasets in the format expected by different algorithms, evaluating model outputs, and splitting training/test data. Implementations of several...
