Stars
Training library for Megatron-based models with bidirectional Hugging Face conversion capability
Ongoing research training transformer models at scale
verl/HybridFlow: A Flexible and Efficient RL Post-Training Framework
A framework for few-shot evaluation of language models.
A high-throughput and memory-efficient inference and serving engine for LLMs
Efficient Triton Kernels for LLM Training
Train transformer language models with reinforcement learning.
Machine learning metrics for distributed, scalable PyTorch applications.
🤗 PEFT: State-of-the-art Parameter-Efficient Fine-Tuning.
A library for efficient similarity search and clustering of dense vectors.
DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.
Simple implementation of OpenAI CLIP model in PyTorch.
Pretrain, finetune ANY AI model of ANY size on 1 or 10,000+ GPUs with zero code changes.
Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
ONNXMLTools enables conversion of models to ONNX
Open standard for machine learning interoperability
Curated coding interview preparation materials for busy software engineers
🤗 The largest hub of ready-to-use datasets for AI models with fast, easy-to-use and efficient data manipulation tools
ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
💥 Fast State-of-the-Art Tokenizers optimized for Research and Production
🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.
Causal Inference for the Brave and True. A light-hearted yet rigorous approach to learning about impact estimation and causality.
Seamless operability between C++11 and Python
Header-only C++/python library for fast approximate nearest neighbors
Library for fast text representation and classification.
Production-Grade Container Scheduling and Management
FastAPI framework, high performance, easy to learn, fast to code, ready for production
Tensors and Dynamic neural networks in Python with strong GPU acceleration
A game theoretic approach to explain the output of any machine learning model.
Tutorials on implementing a few sequence-to-sequence (seq2seq) models with PyTorch and TorchText.