Train speculative decoding models effortlessly and port them smoothly to SGLang serving.

Best practices & guides on how to write distributed pytorch training code

Fault tolerance for PyTorch (HSDP, LocalSGD, DiLoCo, Streaming DiLoCo)

Repo for Qwen Image Finetune

A PyTorch native library for training speculative decoding models

META LLAMA3 GENAI Real World UseCases End To End Implementation Guide

Llama-style transformer in PyTorch with multi-node / multi-GPU training. Includes pretraining, fine-tuning, DPO, LoRA, and knowledge distillation. Scripts for dataset mixing and training from scratch.

🦾💻🌐 distributed training & serverless inference at scale on RunPod

A comprehensive hands-on guide to building production-grade distributed applications with Ray - from distributed training and multimodal data processing to inference and reinforcement learning.

Fast and easy distributed model training examples.

A script for training the ConvNextV2 on CIFAR10 dataset using the FSDP technique for a distributed training scheme.

A minimal, hackable pre-training stack for GPT-style language models

Simple and efficient implementation of 671B DeepSeek V3 that trainable with FSDP+EP and minimal requirement of 256x A100/H100, targeted for HuggingFace ecosystem

A minimal, hackable pre-training stack for GPT-style language models

Minimal yet high performant code for pretraining llms. Attempts to implement some SOTA features. Implements training through: Deepspeed, Megatron-LM, and FSDP. WIP

Billus 大模型技能库：面向 LLM、VL、多模态与图像生成模型的训练、调参、剪枝、量化与工程化技能库

Implementations of some popular approaches for efficient deep learning training and inference

Framework, Model & Kernel Optimizations for Distributed Deep Learning - Data Hack Summit

This repository focuses on distributed and parallel computing with PyTorch, covering model parallelism, data parallelism, and advanced optimization techniques. It provides resources for scaling AI training and inference efficiently across multiple devices.

Scalable multimodal AI system combining FSDP, RLHF, and Inferentia optimization for customer insights generation.