HPC-AI Tech 的公共 GitHub 足迹

Making large AI models cheaper, faster and more accessible

Open-Sora: Democratizing Efficient Video Production for All

Large-scale model inference.

Optimizing AlphaFold Training and Inference on GPU Clusters

Efficient AI Inference & Serving

Examples of training models with hybrid parallelism using ColossalAI

Scalable PaLM implementation of PyTorch

A Python library transfers PyTorch tensors between CPU and NVMe

A memory efficient DLRM training solution using ColossalAI

Sky Computing: Accelerating Geo-distributed Computing in Federated Learning

Performance benchmarking with ColossalAI

A collection of models built with ColossalAI

Documentation for Colossal-AI

A collection of dockerfiles for various tasks

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Elixir: Train a Large Language Model on a Small GPU Cluster

Storing publicly available assets such as images, animations and texts

CLI for ColossalAI Platform

🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.

GPT Demo with hybrid distributed training

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HPC-AI TECH 's Fine-tuning SDK

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Train mmdetection models with ColossalAI.

Democratizing AlphaFold3: an PyTorch reimplementation to accelerate protein structure prediction

A unified library of state-of-the-art model optimization techniques like quantization, pruning, distillation, speculative decoding, etc. It compresses deep learning models for downstream deployment frameworks like TensorRT-LLM or TensorRT to optimize inference speed.

TensorRT LLM provides users with an easy-to-use Python API to define Large Language Models (LLMs) and support state-of-the-art optimizations to perform inference efficiently on NVIDIA GPUs. TensorRT LLM also contains components to create Python and C++ runtimes that orchestrate the inference execution in performant way.

Pytorch domain library for recommendation systems

A modular graph-based Retrieval-Augmented Generation (RAG) system

此仓库未提供描述。

This repository contains Huawei Ascend CANN files

Documentation for our cloud platform

Selects tests affected by changed files. Executes the right tests first. Continuous test runner when used with pytest-watch.