Showing 897 open source projects for "source code computer"

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  • 1
    Agent Development Kit (ADK)

    Agent Development Kit (ADK)

    Open-source, code-first Python toolkit for building, evaluating, etc.

    ADK (Android Device Key) Python is a reference implementation by Google for working with Android attestation keys in Python. It facilitates the integration of Android attestation features into backends or systems that require verification of device identity and integrity. This is especially important in high-security applications where verifying that a device is genuine and uncompromised is critical. ADK Python helps developers verify hardware-backed keys, work with JSON Web Tokens (JWT),...
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  • 2
    FastAgency

    FastAgency

    The fastest way to bring multi-agent workflows to production

    FastAgency is a framework that simplifies the creation and deployment of AI-driven automation agents. It provides a structured environment for developing AI assistants capable of handling various business and technical tasks.
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  • 3
    InternGPT

    InternGPT

    Open source demo platform where you can easily showcase your AI models

    InternGPT is an open-source multimodal AI framework designed to extend large language models beyond text interactions into visual reasoning and image manipulation tasks. The system integrates conversational AI with computer vision models so users can interact with images, videos, and visual environments through natural language instructions. Unlike traditional chat systems that rely solely on text prompts, InternGPT allows users to interact with visual content using both language and nonverbal signals such as pointing or highlighting objects within images. ...
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  • 4
    NeMo Automodel

    NeMo Automodel

    Pytorch Distributed native training library for LLMs/VLMs

    NeMo AutoModel is NVIDIA's open-source PyTorch Distributed training library for scaling LLM, VLM, diffusion, and retrieval-model training. Its DTensor-native SPMD approach lets the same training code scale from one GPU to large multi-node clusters by changing configuration. Hugging Face integration provides broad model compatibility without requiring format conversion.
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    MongoDB Atlas runs apps anywhere

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  • 5
    OpenLIT

    OpenLIT

    OpenLIT is an open-source LLM Observability tool

    OpenLIT is an OpenTelemetry-native tool designed to help developers gain insights into the performance of their LLM applications in production. It automatically collects LLM input and output metadata and monitors GPU performance for self-hosted LLMs. OpenLIT makes integrating observability into GenAI projects effortless with just a single line of code. Whether you're working with popular LLM providers such as OpenAI and HuggingFace, or leveraging vector databases like ChromaDB, OpenLIT...
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  • 6
    smolagents

    smolagents

    Agents write python code to call tools and orchestrate other agents

    This library is the simplest framework out there to build powerful agents. We provide our definition in this page, where you’ll also find tips for when to use them or not (spoilers: you’ll often be better off without agents). smolagents is a lightweight framework for building AI agents using large language models (LLMs). It simplifies the development of AI-driven applications by providing tools to create, train, and deploy language model-based agents.
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  • 7
    FastKoko

    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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  • 8
    Google DeepMind GraphCast and GenCast

    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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  • 9
    Weights and Biases

    Weights and Biases

    Tool for visualizing and tracking your machine learning experiments

    Use W&B to build better models faster. Track and visualize all the pieces of your machine learning pipeline, from datasets to production models. Quickly identify model regressions. Use W&B to visualize results in real time, all in a central dashboard. Focus on the interesting ML. Spend less time manually tracking results in spreadsheets and text files. Capture dataset versions with W&B Artifacts to identify how changing data affects your resulting models. Reproduce any model, with saved...
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  • 10
    Swirl

    Swirl

    Swirl queries any number of data sources with APIs

    Swirl queries any number of data sources with APIs and uses spaCy and NLTK to re-rank the unified results without extracting and indexing anything! Includes zero-code configs for Apache Solr, ChatGPT, Elastic Search, OpenSearch, PostgreSQL, Google BigQuery, RequestsGet, Google PSE, NLResearch.com, Miro & more! SWIRL adapts and distributes queries to anything with a search API - search engines, databases, noSQL engines, cloud/SaaS services etc - and uses AI (Large Language Models) to re-rank...
    Downloads: 2 This Week
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  • 11
    LingBot-Video

    LingBot-Video

    Scaling Mixture-of-Experts Video Pretraining for Embodied Intelligence

    LingBot-Video is a large-scale mixture-of-experts video generation model focused on embodied intelligence. It is designed to connect video synthesis with physical-world understanding instead of generating only visually appealing clips. The project includes dense and MoE model variants for text-to-image, text-to-video, and text-image-to-video workflows. Its training combines large-scale web video data with more than 70,000 hours of embodied data. A multi-reward system emphasizes aesthetics,...
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  • 12
    Upscale-A-Video

    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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  • 13
    Claude Ads

    Claude Ads

    Comprehensive paid advertising audit & optimization skill

    Claude Ads is an AI-powered auditing and optimization tool designed to analyze paid advertising campaigns across multiple platforms using Claude Code. It processes user-provided data such as exports or screenshots and evaluates campaigns using hundreds of predefined checks. The system generates structured reports, identifies inefficiencies, and suggests optimization strategies based on industry benchmarks. It supports platforms like Google Ads, Meta Ads, TikTok, LinkedIn, and more, offering...
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  • 14
    Gemma in PyTorch

    Gemma in PyTorch

    The official PyTorch implementation of Google's Gemma models

    gemma_pytorch provides the official PyTorch reference for running and fine-tuning Google’s Gemma family of open models. It includes model definitions, configuration files, and loading utilities for multiple parameter scales, enabling quick evaluation and downstream adaptation. The repository demonstrates text generation pipelines, tokenizer setup, quantization paths, and adapters for low-rank or parameter-efficient fine-tuning. Example notebooks walk through instruction tuning and evaluation...
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  • 15
    TorchDistill

    TorchDistill

    A coding-free framework built on PyTorch

    torchdistill (formerly kdkit) offers various state-of-the-art knowledge distillation methods and enables you to design (new) experiments simply by editing a declarative yaml config file instead of Python code. Even when you need to extract intermediate representations in teacher/student models, you will NOT need to reimplement the models, which often change the interface of the forward, but instead specify the module path(s) in the yaml file. In addition to knowledge distillation, this...
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  • 16
    self-llm

    self-llm

    Tutorial tailored for Chinese babies on rapid fine-tuning

    self-llm is an open source educational project created by the Datawhale community that serves as a practical guide for deploying, fine-tuning, and using open-source large language models on Linux systems. The repository focuses on helping beginners and developers understand how to run and customize modern LLMs locally rather than relying solely on hosted APIs. It provides step-by-step tutorials covering environment setup, model deployment, inference workflows, and efficient fine-tuning...
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  • 17
    A.I.G (AI-Infra-Guard)

    A.I.G (AI-Infra-Guard)

    A full-stack AI Red Teaming platform securing AI ecosystems

    AI-Infra-Guard is an open-source AI red teaming platform for evaluating security risks across modern AI ecosystems. It combines scanning for AI agents, agent skills, MCP servers, AI infrastructure, and OpenClaw deployments. Its infrastructure scanner fingerprints AI services and checks them against a large library of known vulnerabilities. Agent and skill analysis detects risks involving malicious instructions, unsafe code execution, privilege abuse, compromised tools, and insecure dependencies. ...
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  • 18
    MiniMind-O

    MiniMind-O

    A 0.1B Omni model trained from scratch

    MiniMind-O is an educational open-source project for building a small end-to-end Omni model from scratch. It extends the MiniMind family by exploring a model that can handle text, audio, and image inputs while producing text and streaming speech outputs. The project is designed to make multimodal AI training more accessible by keeping the model size small enough for ordinary personal hardware.
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  • 19
    SimpleHTR

    SimpleHTR

    Handwritten Text Recognition (HTR) system implemented with TensorFlow

    SimpleHTR is an open-source implementation of a handwriting text recognition system based on deep learning techniques. The project focuses on converting images of handwritten text into machine-readable digital text using neural networks. The system uses a combination of convolutional neural networks and recurrent neural networks to extract visual features and model sequential character patterns in handwriting.
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  • 20
    LLMs-Zero-to-Hero

    LLMs-Zero-to-Hero

    From nobody to big model (LLM) hero

    LLMs-Zero-to-Hero is an open-source educational project designed to guide learners through the complete process of understanding and building large language models from the ground up. The repository presents a structured learning pathway that begins with fundamental concepts in machine learning and progresses toward advanced topics such as model pre-training, fine-tuning, and deployment. Rather than relying entirely on existing frameworks, the project encourages readers to implement...
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  • 21
    A.I.G (AI-Infra-Guard)

    A.I.G (AI-Infra-Guard)

    Full-stack AI Red Teaming platform

    AI-Infra-Guard is a powerful open-source security platform from Tencent’s Zhuque Lab designed to assess the safety and resilience of AI infrastructures, codebases, and components through automated scanning and evaluation tools. It brings together AI infrastructure vulnerability scanning, MCP server risk analysis, and jailbreak evaluation into a unified workflow so that enterprises and individuals can identify critical security issues without relying on external services. Users can deploy it...
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  • 22
    ZAPI

    ZAPI

    ZAPI by Adopt AI is an open-source Python library

    ZAPI is a developer-centric API framework that streamlines building, testing, and deploying APIs with strong type safety and minimal boilerplate, helping teams deliver backend services faster with fewer errors. It emphasizes a declarative router and schema model that uses types to define request and response formats, providing clear contracts for frontend and backend teams while automatically generating documentation. Zapi abstracts many repetitive tasks such as validation, authentication...
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  • 23
    DFlash

    DFlash

    Block Diffusion for Ultra-Fast Speculative Decoding

    DFlash is an open-source framework for ultra-fast speculative decoding using a lightweight block diffusion model to draft text in parallel with a target large language model, dramatically improving inference speed without sacrificing generation quality. It acts as a “drafter” that proposes likely continuations which the main model then verifies, enabling significant throughput gains compared to traditional autoregressive decoding methods that generate token by token.
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  • 24
    Archon

    Archon

    The knowledge and task management backbone for AI coding assistants

    Archon is an open-source “command center” designed to enhance AI coding assistant workflows by giving developers a centralized environment for knowledge management, context engineering, and task coordination across AI agents. It acts as a backend (including an MCP server) that allows different AI coding tools and assistants to share the same structured context, knowledge base, and task lists, improving consistency, productivity, and collaboration across multi-agent interactions. ...
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  • 25
    Lightweight' GAN

    Lightweight' GAN

    Implementation of 'lightweight' GAN, proposed in ICLR 2021

    Implementation of 'lightweight' GAN proposed in ICLR 2021, in Pytorch. The main contribution of the paper is a skip-layer excitation in the generator, paired with autoencoding self-supervised learning in the discriminator. Quoting the one-line summary "converge on single gpu with few hours' training, on 1024 resolution sub-hundred images". Augmentation is essential for Lightweight GAN to work effectively in a low data setting. You can test and see how your images will be augmented before...
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