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ArcticInference: vLLM plugin for high-throughput, low-latency inference
🤗 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.
ArcticTraining is a framework designed to simplify and accelerate the post-training process for large language models (LLMs)
Minimal example scripts of the Hugging Face Trainer, focused on staying under 150 lines
Machine Learning Engineering Open Book
🤗 PEFT: State-of-the-art Parameter-Efficient Fine-Tuning.
MII makes low-latency and high-throughput inference possible, powered by DeepSpeed.
Pretrained language model with 100B parameters
Trainable, memory-efficient, and GPU-friendly PyTorch reproduction of AlphaFold 2
Code release for SLIP Self-supervision meets Language-Image Pre-training
Library for 8-bit optimizers and quantization routines.
Ongoing research training transformer language models at scale, including: BERT & GPT-2
Distribution transparent Machine Learning experiments on Apache Spark
An implementation of model parallel autoregressive transformers on GPUs, based on the Megatron and DeepSpeed libraries
Guide: Finetune GPT2-XL (1.5 Billion Parameters) and finetune GPT-NEO (2.7 B) on a single GPU with Huggingface Transformers using DeepSpeed
Accelerate your Neural Architecture Search (NAS) through fast, reproducible and modular research.
RDMA and SHARP plugins for nccl library
Example models using DeepSpeed
DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.
A minimal & modern LaTeX template for your (bachelor's | master's | doctoral) thesis
Find the smallest number of switches necessary to build topologies of a given number of hosts and bisection bandwidth for the EGFT, HyperX, and Jellyfish topologies.