Search Results for "framework" - Page 23
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Showing 744 open source projects for "framework"
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towhee
Framework that is dedicated to making neural data processing
Towhee is an open-source machine-learning pipeline that helps you encode your unstructured data into embeddings. You can use our Python API to build a prototype of your pipeline and use Towhee to automatically optimize it for production-ready environments. From images to text to 3D molecular structures, Towhee supports data transformation for nearly 20 different unstructured data modalities. We provide end-to-end pipeline optimizations, covering everything from data decoding/encoding, to... -
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YiVal
Your Automatic Prompt Engineering Assistant for GenAI Applications
YiVal is an open-source framework designed to automate prompt engineering and evaluation workflows for generative AI applications, enabling developers to systematically improve the performance of large language models. It focuses on experimentation and optimization by allowing users to test multiple prompt variations, configurations, and model parameters in parallel, then evaluate their outputs using structured metrics and scoring systems. -
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AI System & AI Infra
Tutorial repository focused on the full-stack design of AI systems
AI System is an educational and technical tutorial repository focused on the full-stack design of artificial intelligence systems, covering the foundational infrastructure that powers modern deep learning workloads. The project explores the AI software and hardware stack end to end, including AI chips, AI compilers, inference engines, and training frameworks, helping learners understand how these components interact in real-world deployments. Rather than being a single library, it functions... -
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XAgent
An Autonomous LLM Agent for Complex Task Solving
XAgent is an AI-driven autonomous agent framework capable of handling multi-step tasks across different domains. It enables AI agents to perform decision-making, task planning, and self-learning based on user-defined objectives, making it ideal for automation and research applications. -
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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Langcorn
Serving LangChain LLM apps automagically with FastApi
LangCorn is an API server that enables you to serve LangChain models and pipelines with ease, leveraging the power of FastAPI for a robust and efficient experience. -
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MMAction2
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 tasks, including action recognition, action localization, Spatio-temporal action detection, and skeleton-based action detection. ... -
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Detectron
FAIR's research platform for object detection research
Detectron is an object detection and instance segmentation research framework that popularized many modern detection models in a single, reproducible codebase. Built on Caffe2 with custom CUDA/C++ operators, it provided reference implementations for models like Faster R-CNN, Mask R-CNN, RetinaNet, and Feature Pyramid Networks. The framework emphasized a clean configuration system, strong baselines, and a “model zoo” so researchers could compare results under consistent settings. ... -
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LLMFlows
LLMFlows - Simple, Explicit and Transparent LLM Apps
LLMFlows is a framework for building simple, explicit, and transparent applications utilizing Large Language Models (LLMs). It emphasizes clarity and control in the development process, allowing developers to create LLM-powered applications with well-defined workflows and interactions. LLMFlows supports various LLMs and provides tools to manage prompts, responses, and application logic effectively. -
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Graph of Thoughts
Official Implementation of "Graph of Thoughts
...In this framework, problems are modeled as a graph of operations where nodes represent reasoning steps and edges represent dependencies between them. The framework executes these operations using a large language model as the reasoning engine while evaluating intermediate results to guide the search process. This approach enables models to explore multiple reasoning strategies in parallel and choose the most promising solutions during problem solving. -
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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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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. ... -
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LLM Training Puzzles
What would you do with 1000 H100s
...Learners work through simplified scenarios that expose the tradeoffs hidden inside large training systems. The hands-on format emphasizes deriving behavior rather than memorizing framework commands. Google Colab is recommended so readers can copy the notebook and begin experimenting quickly. The project belongs to a broader puzzle series covering tensors, automatic differentiation, Transformers, GPUs, and related topics. -
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fastquant
Backtest and optimize your ML trading strategies with only 3 lines
...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 strategies such as moving average crossovers, momentum trading, and custom indicators on historical stock data. By automating data retrieval, strategy evaluation, and result visualization, the library reduces the barrier to entry for individuals interested in quantitative finance. The project also supports optimization workflows that allow users to search for parameter combinations that improve trading strategy performance. -
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Lightning-Hydra-Template
PyTorch Lightning + Hydra. A very user-friendly template
...Not fitted for data engineering - the template configuration setup is not designed for building data processing pipelines that depend on each other. PyTorch Lightning, a lightweight PyTorch wrapper for high-performance AI research. Think of it as a framework for organizing your PyTorch code. Hydra, a framework for elegantly configuring complex applications. The key feature is the ability to dynamically create a hierarchical configuration by composition and override it through config files and the command line. -
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D2L.ai
Interactive deep learning book with multi-framework code
Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 300 universities from 55 countries including Stanford, MIT, Harvard, and Cambridge. This open-source book represents our attempt to make deep learning approachable, teaching you the concepts, the context, and the code. The entire book is drafted in Jupyter notebooks, seamlessly integrating exposition figures, math, and interactive examples with self-contained code. -
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Nougat
Implementation of Nougat Neural Optical Understanding
Nougat is a multi-modal generative modeling framework that bridges vision and text modalities with structured generation control (e.g. layout, scene composition) rather than treating images as flat contexts. It combines object-centric modules with transformer-based reasoning to propose, refine, and render scenes in a generative pipeline. The architecture allows you to specify or prompt a layout (which objects should be where) and then the model fills in appearance, context, lighting, and relations coherently. ... -
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LangChain Apps on Production with Jina
Langchain Apps on Production with Jina & FastAPI
Jina is an open-source framework for building scalable multi-modal AI apps on Production. LangChain is another open-source framework for building applications powered by LLMs. long-chain-serve helps you deploy your LangChain apps on Jina AI Cloud in a matter of seconds. You can benefit from the scalability and serverless architecture of the cloud without sacrificing the ease and convenience of local development. -
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OpenFlamingo
An open-source framework for training large multimodal models
Welcome to our open source version of DeepMind's Flamingo model! In this repository, we provide a PyTorch implementation for training and evaluating OpenFlamingo models. We also provide an initial OpenFlamingo 9B model trained on a new Multimodal C4 dataset (coming soon). Please refer to our blog post for more details. This repo is still under development, and we hope to release better-performing and larger OpenFlamingo models soon. If you have any questions, please feel free to open an... -
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EasyRL
Reinforcement learning (RL) tutorial series
easy-rl is a beginner-friendly reinforcement learning (RL) tutorial series and framework developed by Datawhale China. It provides educational resources and implementations of various RL algorithms to help new researchers and practitioners learn RL concepts. -
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MetaTransformer
Meta-Transformer for Unified Multimodal Learning
We're thrilled to present OneLLM, an ensembling Meta-Transformer framework with Multimodal Large Language Models, which performs multimodal joint training, supports more modalities including fMRI, Depth, and Normal Maps, and demonstrates very impressive performances on 25 benchmarks. -
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ReAct Prompting
Synergizing Reasoning and Acting in Language Models
...This alternating sequence of reasoning, acting, and observing results allows the model to gather additional information and refine its decision-making process during task execution. The framework has been tested on several benchmarks including question answering, fact verification, and interactive decision-making tasks, demonstrating improved performance compared to methods that rely only on reasoning. -
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Promptify
se GPT or other prompt based models to get structured output
...Instead of manually crafting prompts for each task, Promptify introduces a unified architecture that combines prompt templates, language model interfaces, and processing pipelines into a single framework. This approach allows developers to perform tasks such as text classification, named entity recognition, question answering, and information extraction using consistent prompt templates. The library supports integration with multiple large language model providers, enabling users to experiment with various models without changing their overall workflow. -
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GLM-130B
GLM-130B: An Open Bilingual Pre-Trained Model (ICLR 2023)
...The model supports efficient inference via INT8 and INT4 quantization, reducing hardware requirements from 8× A100 GPUs to as little as a single server with 4× RTX 3090s. Built on the SwissArmyTransformer (SAT) framework and compatible with DeepSpeed and FasterTransformer, it supports high-speed inference (up to 2.5× faster) and reproducible evaluation across 30+ benchmark tasks. -
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wukong-robot
Chinese voice dialogue robot/smart speaker project
wukong-robot is a Chinese voice assistant / smart speaker project built to let makers and hackers design highly customizable voice-controlled devices. It combines wake-word detection, automatic speech recognition, natural language understanding, and text-to-speech into a single framework aimed at the Chinese-speaking ecosystem. The project is positioned as a simple, flexible, and elegant platform that can run on devices like Raspberry Pi and other Linux-based boards, making it suitable for DIY smart speakers and home-automation hubs. It supports multi-turn conversational capabilities powered by ChatGPT or other large language models, letting users have continuous dialogues rather than one-shot commands. ... -
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TaskMatrix
Enable sending and receiving images during chatting
...The project expands beyond traditional chatbot behavior by enabling AI systems to process, generate, edit, and reason about images while coordinating multiple specialized models simultaneously. Originally introduced alongside the Visual ChatGPT concept, TaskMatrix acts as an orchestration framework where a central language model delegates subtasks to domain-specific AI systems such as image generators, segmentation tools, or recognition models. The architecture focuses on modularity, allowing new APIs and foundation models to be integrated as interchangeable task-solving components. The project also explores low-code human-AI interaction workflows that improve controllability and transparency during complex task execution.