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Making large AI models cheaper, faster and more accessible
DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.
Large-scale Self-supervised Pre-training Across Tasks, Languages, and Modalities
A framework for managing and maintaining multi-language pre-commit hooks.
PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
Open source annotation tool for machine learning practitioners.
Hackable and optimized Transformers building blocks, supporting a composable construction.
The Triton Inference Server provides an optimized cloud and edge inferencing solution.
Python client for Baidu Yun (Personal Cloud Storage) 百度云/百度网盘Python客户端
Efficiently computes derivatives of NumPy code.
An implementation of model parallel autoregressive transformers on GPUs, based on the Megatron and DeepSpeed libraries
ClearML - Auto-Magical CI/CD to streamline your AI workload. Experiment Management, Data Management, Pipeline, Orchestration, Scheduling & Serving in one MLOps/LLMOps solution
A repo for distributed training of language models with Reinforcement Learning via Human Feedback (RLHF)
A fast inference library for running LLMs locally on modern consumer-class GPUs
FlagAI (Fast LArge-scale General AI models) is a fast, easy-to-use and extensible toolkit for large-scale model.
PyTorch extensions for high performance and large scale training.
Jittor is a high-performance deep learning framework based on JIT compiling and meta-operators.
🚀 Accelerate inference and training of 🤗 Transformers, Diffusers, TIMM and Sentence Transformers with easy to use hardware optimization tools
A library for accelerating Transformer models on NVIDIA GPUs, including using 8-bit and 4-bit floating point (FP8 and FP4) precision on Hopper, Ada and Blackwell GPUs, to provide better performance…
Easy benchmarking of all publicly accessible implementations of convnets
Pytorch domain library for recommendation systems
slime is an LLM post-training framework for RL Scaling.