Showing 110 open source projects for "raspberry-gpio-python"

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  • 1
    whisper.cpp

    whisper.cpp

    Port of OpenAI's Whisper model in C/C++

    High-performance inference of OpenAI's Whisper automatic speech recognition (ASR) model. Supported platforms: Mac OS (Intel and Arm) iOS Android Linux / FreeBSD WebAssembly Windows (MSVC and MinGW] Raspberry Pi
    Downloads: 401 This Week
    Last Update:
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  • 2
    SageMaker Python SDK

    SageMaker Python SDK

    Training and deploying machine learning models on Amazon SageMaker

    SageMaker Python SDK is an open source library for training and deploying machine learning models on Amazon SageMaker. With the SDK, you can train and deploy models using popular deep learning frameworks Apache MXNet and TensorFlow. You can also train and deploy models with Amazon algorithms, which are scalable implementations of core machine learning algorithms that are optimized for SageMaker and GPU training. If you have your own algorithms built into SageMaker-compatible Docker containers...
    Downloads: 1 This Week
    Last Update:
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  • 3
    GPT4All

    GPT4All

    Run Local LLMs on Any Device. Open-source

    .... This project also supports Python integrations for easy automation and customization. GPT4All is ideal for individuals and businesses seeking private, offline access to powerful LLMs.
    Downloads: 84 This Week
    Last Update:
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  • 4
    llama.cpp

    llama.cpp

    Port of Facebook's LLaMA model in C/C++

    The llama.cpp project enables the inference of Meta's LLaMA model (and other models) in pure C/C++ without requiring a Python runtime. It is designed for efficient and fast model execution, offering easy integration for applications needing LLM-based capabilities. The repository focuses on providing a highly optimized and portable implementation for running large language models directly within C/C++ environments.
    Downloads: 63 This Week
    Last Update:
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  • 5
    rwkv.cpp

    rwkv.cpp

    INT4/INT5/INT8 and FP16 inference on CPU for RWKV language model

    Besides the usual FP32, it supports FP16, quantized INT4, INT5 and INT8 inference. This project is focused on CPU, but cuBLAS is also supported. RWKV is a novel large language model architecture, with the largest model in the family having 14B parameters. In contrast to Transformer with O(n^2) attention, RWKV requires only state from the previous step to calculate logits. This makes RWKV very CPU-friendly on large context lengths.
    Downloads: 38 This Week
    Last Update:
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  • 6
    EasyOCR

    EasyOCR

    Ready-to-use OCR with 80+ supported languages

    Ready-to-use OCR with 80+ supported languages and all popular writing scripts including Latin, Chinese, Arabic, Devanagari, Cyrillic and etc. EasyOCR is a python module for extracting text from image. It is a general OCR that can read both natural scene text and dense text in document. We are currently supporting 80+ languages and expanding. Second-generation models: multiple times smaller size, multiple times faster inference, additional characters and comparable accuracy to the first...
    Downloads: 38 This Week
    Last Update:
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  • 7
    ChatGLM.cpp

    ChatGLM.cpp

    C++ implementation of ChatGLM-6B & ChatGLM2-6B & ChatGLM3 & GLM4(V)

    ChatGLM.cpp is a C++ implementation of the ChatGLM-6B model, enabling efficient local inference without requiring a Python environment. It is optimized for running on consumer hardware.
    Downloads: 28 This Week
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  • 8
    AIMET

    AIMET

    AIMET is a library that provides advanced quantization and compression

    Qualcomm Innovation Center (QuIC) is at the forefront of enabling low-power inference at the edge through its pioneering model-efficiency research. QuIC has a mission to help migrate the ecosystem toward fixed-point inference. With this goal, QuIC presents the AI Model Efficiency Toolkit (AIMET) - a library that provides advanced quantization and compression techniques for trained neural network models. AIMET enables neural networks to run more efficiently on fixed-point AI hardware...
    Downloads: 24 This Week
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  • 9
    Phi-3-MLX

    Phi-3-MLX

    Phi-3.5 for Mac: Locally-run Vision and Language Models

    Phi-3-Vision-MLX is an Apple MLX (machine learning on Apple silicon) implementation of Phi-3 Vision, a lightweight multi-modal model designed for vision and language tasks. It focuses on running vision-language AI efficiently on Apple hardware like M1 and M2 chips.
    Downloads: 21 This Week
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  • 10
    LMDeploy

    LMDeploy

    LMDeploy is a toolkit for compressing, deploying, and serving LLMs

    LMDeploy is a toolkit designed for compressing, deploying, and serving large language models (LLMs). It offers tools and workflows to optimize LLMs for production environments, ensuring efficient performance and scalability. LMDeploy supports various model architectures and provides deployment solutions across different platforms.
    Downloads: 18 This Week
    Last Update:
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  • 11
    MegEngine

    MegEngine

    Easy-to-use deep learning framework with 3 key features

    .... Gain the lowest memory usage when inferencing a model by leveraging our unique pushdown memory planner. NOTE: MegEngine now supports Python installation on Linux-64bit/Windows-64bit/MacOS(CPU-Only)-10.14+/Android 7+(CPU-Only) platforms with Python from 3.5 to 3.8. On Windows 10 you can either install the Linux distribution through Windows Subsystem for Linux (WSL) or install the Windows distribution directly. Many other platforms are supported for inference.
    Downloads: 15 This Week
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  • 12
    Seldon Core

    Seldon Core

    An MLOps framework to package, deploy, monitor and manage models

    The de facto standard open-source platform for rapidly deploying machine learning models on Kubernetes. Seldon Core, our open-source framework, makes it easier and faster to deploy your machine learning models and experiments at scale on Kubernetes. Seldon Core serves models built in any open-source or commercial model building framework. You can make use of powerful Kubernetes features like custom resource definitions to manage model graphs. And then connect your continuous integration and...
    Downloads: 14 This Week
    Last Update:
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  • 13
    vLLM

    vLLM

    A high-throughput and memory-efficient inference and serving engine

    vLLM is a fast and easy-to-use library for LLM inference and serving. High-throughput serving with various decoding algorithms, including parallel sampling, beam search, and more.
    Downloads: 13 This Week
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  • 14
    DeepSparse

    DeepSparse

    Sparsity-aware deep learning inference runtime for CPUs

    A sparsity-aware enterprise inferencing system for AI models on CPUs. Maximize your CPU infrastructure with DeepSparse to run performant computer vision (CV), natural language processing (NLP), and large language models (LLMs).
    Downloads: 12 This Week
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  • 15
    OpenMLDB

    OpenMLDB

    OpenMLDB is an open-source machine learning database

    ... and inference. Real-time features are essential for many machine learning applications, such as real-time personalized recommendations and risk analytics. However, a feature engineering script developed by data scientists (Python scripts in most cases) cannot be directly deployed into production for online inference because it usually cannot meet the engineering requirements, such as low latency, high throughput and high availability.
    Downloads: 13 This Week
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  • 16
    optillm

    optillm

    Optimizing inference proxy for LLMs

    OptiLLM is an optimizing inference proxy for Large Language Models (LLMs) that implements state-of-the-art techniques to enhance performance and efficiency. It serves as an OpenAI API-compatible proxy, allowing for seamless integration into existing workflows while optimizing inference processes. OptiLLM aims to reduce latency and resource consumption during LLM inference.
    Downloads: 8 This Week
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  • 17
    Scanpy

    Scanpy

    Single-cell analysis in Python

    Scanpy is a scalable toolkit for analyzing single-cell gene expression data built jointly with anndata. It includes preprocessing, visualization, clustering, trajectory inference and differential expression testing. The Python-based implementation efficiently deals with datasets of more than one million cells.
    Downloads: 7 This Week
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  • 18
    Transformer Engine

    Transformer Engine

    A library for accelerating Transformer models on NVIDIA GPUs

    Transformer Engine (TE) is a library for accelerating Transformer models on NVIDIA GPUs, including using 8-bit floating point (FP8) precision on Hopper GPUs, to provide better performance with lower memory utilization in both training and inference. TE provides a collection of highly optimized building blocks for popular Transformer architectures and an automatic mixed precision-like API that can be used seamlessly with your framework-specific code. TE also includes a framework-agnostic C++...
    Downloads: 8 This Week
    Last Update:
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  • 19
    Curated Transformers

    Curated Transformers

    PyTorch library of curated Transformer models and their components

    State-of-the-art transformers, brick by brick. Curated Transformers is a transformer library for PyTorch. It provides state-of-the-art models that are composed of a set of reusable components. Supports state-of-the-art transformer models, including LLMs such as Falcon, Llama, and Dolly v2. Implementing a feature or bugfix benefits all models. For example, all models support 4/8-bit inference through the bitsandbytes library and each model can use the PyTorch meta device to avoid unnecessary...
    Downloads: 7 This Week
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  • 20
    Arize Phoenix

    Arize Phoenix

    Uncover insights, surface problems, monitor, and fine tune your LLM

    Phoenix provides ML insights at lightning speed with zero-config observability for model drift, performance, and data quality. Phoenix is an Open Source ML Observability library designed for the Notebook. The toolset is designed to ingest model inference data for LLMs, CV, NLP and tabular datasets. It allows Data Scientists to quickly visualize their model data, monitor performance, track down issues & insights, and easily export to improve. Deep Learning Models (CV, LLM, and Generative)...
    Downloads: 7 This Week
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  • 21
    FlashInfer

    FlashInfer

    FlashInfer: Kernel Library for LLM Serving

    FlashInfer is a kernel library designed to enhance the serving of Large Language Models (LLMs) by optimizing inference performance. It provides a high-performance framework that integrates seamlessly with existing systems, aiming to reduce latency and improve efficiency in LLM deployments. FlashInfer supports various hardware architectures and is built to scale with the demands of production environments.
    Downloads: 6 This Week
    Last Update:
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  • 22
    EvaDB

    EvaDB

    Database system for building simpler and faster AI-powered application

    Over the last decade, AI models have radically changed the world of natural language processing and computer vision. They are accurate on various tasks ranging from question answering to object tracking in videos. To use an AI model, the user needs to program against multiple low-level libraries, like PyTorch, Hugging Face, Open AI, etc. This tedious process often leads to a complex AI app that glues together these libraries to accomplish the given task. This programming complexity prevents...
    Downloads: 6 This Week
    Last Update:
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  • 23
    Text Generation Inference

    Text Generation Inference

    Large Language Model Text Generation Inference

    Text Generation Inference is a high-performance inference server for text generation models, optimized for Hugging Face's Transformers. It is designed to serve large language models efficiently with optimizations for performance and scalability.
    Downloads: 6 This Week
    Last Update:
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  • 24
    Superduper

    Superduper

    Superduper: Integrate AI models and machine learning workflows

    Superduper is a Python-based framework for building end-2-end AI-data workflows and applications on your own data, integrating with major databases. It supports the latest technologies and techniques, including LLMs, vector-search, RAG, and multimodality as well as classical AI and ML paradigms. Developers may leverage Superduper by building compositional and declarative objects that out-source the details of deployment, orchestration versioning, and more to the Superduper engine. This allows...
    Downloads: 6 This Week
    Last Update:
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  • 25
    MMDeploy

    MMDeploy

    OpenMMLab Model Deployment Framework

    MMDeploy is an open-source deep learning model deployment toolset. It is a part of the OpenMMLab project. Models can be exported and run in several backends, and more will be compatible. All kinds of modules in the SDK can be extended, such as Transform for image processing, Net for Neural Network inference, Module for postprocessing and so on. Install and build your target backend. ONNX Runtime is a cross-platform inference and training accelerator compatible with many popular ML/DNN...
    Downloads: 6 This Week
    Last Update:
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