Search Results for "accelerator"
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Showing 23 open source projects for "accelerator"
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ChatGLM3
ChatGLM3 series: Open Bilingual Chat LLMs | Open Source Bilingual Chat
...The repo ships Python APIs, CLI and web demos (Gradio/Streamlit), an OpenAI-format API server, and a compact fine-tuning kit. Quantization (4/8-bit), CPU/MPS support, and accelerator backends (TensorRT-LLM, OpenVINO, chatglm.cpp) enable lightweight local or edge deployment. -
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TileLang
Domain-specific language designed to streamline the development
...The project includes autotuning, debugging tools, layout visualization, compiler diagnostics, and an LSP for editor assistance. Kernels can be integrated with PyTorch and compiled for several modern accelerator families. TileLang aims to combine research-friendly productivity with performance close to specialized hand-tuned kernels. -
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Frigate
NVR with realtime local object detection for IP cameras
Frigate - NVR With Realtime Object Detection for IP Cameras A complete and local NVR designed for Home Assistant with AI object detection. Uses OpenCV and Tensorflow to perform realtime object detection locally for IP cameras. Use of a Google Coral Accelerator is optional, but highly recommended. The Coral will outperform even the best CPUs and can process 100+ FPS with very little overhead. -
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DeepInfer
High-performance, large-scale inference framework for DeepSeek models
...It integrates optimized computational kernels and distributed parallelism techniques for attention and mixture-of-experts operations. An asynchronous scheduler overlaps CPU scheduling with accelerator execution to improve throughput. Speculative decoding and confidence-based verification help accelerate token generation. Additional capabilities include deterministic sampling, batch-invariant inference, and structured output generation using JSON schemas, regular expressions, or grammars. Complete production serving requires additional infrastructure components. -
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MaxText
A simple, performant and scalable Jax LLM
...The project acts as both a reference implementation and a practical training library that demonstrates best practices for building and scaling transformer-based language models on modern accelerator hardware. It is optimized to run efficiently on Google Cloud TPUs and GPUs, enabling researchers and engineers to train models ranging from small experiments to extremely large distributed workloads. The framework focuses on simplicity while still supporting advanced techniques such as model sharding, distributed computation, and high-throughput training pipelines. ... -
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Denoising Diffusion Probabilistic Model
Implementation of Denoising Diffusion Probabilistic Model in Pytorch
Implementation of Denoising Diffusion Probabilistic Model in Pytorch. It is a new approach to generative modeling that may have the potential to rival GANs. It uses denoising score matching to estimate the gradient of the data distribution, followed by Langevin sampling to sample from the true distribution. If you simply want to pass in a folder name and the desired image dimensions, you can use the Trainer class to easily train a model. -
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Pathway AI Pipelines
Ready-to-run cloud templates for RAG
...The templates include built-in indexing, vector search, hybrid search, and caching capabilities that remove the need to assemble separate infrastructure components. Developers can run the applications locally or deploy them to cloud platforms using Docker with minimal setup. Overall, llm-app functions as a practical accelerator for teams building real-time, production-ready AI knowledge systems. -
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Colossal-AI
Making large AI models cheaper, faster and more accessible
The Transformer architecture has improved the performance of deep learning models in domains such as Computer Vision and Natural Language Processing. Together with better performance come larger model sizes. This imposes challenges to the memory wall of the current accelerator hardware such as GPU. It is never ideal to train large models such as Vision Transformer, BERT, and GPT on a single GPU or a single machine. There is an urgent demand to train models in a distributed environment. However, distributed training, especially model parallelism, often requires domain expertise in computer systems and architecture. ... -
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MSA: Memory Sparse Attention
Trainable latent-memory framework for 100M-token contexts
...A tiered KV-cache design stores routing keys on GPU while larger content states can remain on CPU. Its Memory Parallel engine distributes scoring and transfers only selected memory back to the accelerator. Memory Interleave alternates retrieval, context expansion, and generation to improve multi-hop reasoning across distant segments. The project reports experiments extending from 16K to 100M tokens, including inference on two A800 GPUs. -
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Diffrax
Numerical differential equation solvers in JAX
...The project provides tools for solving ordinary differential equations, stochastic differential equations, controlled differential equations, and related systems in a way that fits naturally into modern machine learning and differentiable programming workflows. Because it is written to work closely with JAX, it supports just-in-time compilation, automatic differentiation, vectorization, and accelerator-backed execution on hardware such as GPUs and TPUs. This makes it especially appealing for researchers who need equation solvers that can be embedded inside trainable models or simulation-heavy learning systems. -
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Best-of Machine Learning with Python
A ranked list of awesome machine learning Python libraries
This curated list contains 900 awesome open-source projects with a total of 3.3M stars grouped into 34 categories. All projects are ranked by a project-quality score, which is calculated based on various metrics automatically collected from GitHub and different package managers. If you like to add or update projects, feel free to open an issue, submit a pull request, or directly edit the projects.yaml. Contributions are very welcome! General-purpose machine learning and deep learning... -
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Hyper Download Manager
High-performance download manager with modern UI and browser support.
Hyper Download Manager (HDM) is a high-performance, open-source download accelerator engineered for speed, privacy, and simplicity. Built with Python and PySide6, it features a sleek, modern interface inspired by Windows 11 aesthetics that blends seamlessly with your desktop environment. HDM replaces your browser's default download manager, offering deep integration with Chrome, Edge, Brave, and Firefox through native messaging. -
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MMDeploy
OpenMMLab Model Deployment Framework
...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 frameworks. Please read getting_started for the basic usage of MMDeploy. -
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Apple Neural Engine (ANE) Transformers
Reference implementation of the Transformer architecture optimized
...The project sits alongside related Apple ML repos that focus on deploying attention-based models efficiently to ANE-equipped hardware. In short, it’s a practical blueprint for adapting Transformers to Apple’s dedicated ML accelerator without rewriting entire model stacks. -
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Pythopad
A free Python source code editor and Notepad replacement for Windows
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TF Quant Finance
High-performance TensorFlow library for quantitative finance
TF Quant Finance is a high-performance library of quantitative finance components built on TensorFlow, aimed at research and production workloads. It implements pricing engines, risk measures, stochastic models, optimizers, and random number generators that are differentiable and vectorized for accelerators. Users can value options and fixed-income instruments, simulate paths, fit curves, and calibrate models while leveraging TensorFlow’s jit compilation and automatic differentiation. The... -
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Codepad
A free source code editor and Notepad replacement for Windows
A free source code editor and Notepad replacement for Windows. Source code on GitHub: https://github.com/altarawy/codepad -
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This project is for particle accelerator start-to-end simulation. The project contains software infrastructure for setting up model run as service/client mode.
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Osmosys is a GUI enabled download manager and accelerator for written in Python that supports simultaneous downloads, pausing and resuming downloads, and backup state files during download to prevent network errors. Supports all major browsers.
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3D fast-paced dodge/shoot-'em-up, created using friendly, portable technologies such as Python and OpenGL. Originally conceived and developed in one week for first ever PyWeek challenge.
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FTP/HTTP;Resume Broken Downloads;Split file into multiple parts;download from mirrors; maximize simultaneous connections;HTTP Proxy Authentication;
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This project will provide a suite of applications which will function as a remote file download accelerator . The roadmap for the project is 1. proof of concept (Command Line Interface) cli client. 2. resume / failure recovery added to
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