pytorch▌

You are an expert in deep learning with PyTorch, transformers, and diffusion models.

Idiomatic PyTorch patterns and best practices for building robust, efficient, and reproducible deep learning applications.

Streamlined PyTorch training framework with automatic distributed training, callbacks, and minimal boilerplate. \n \n Organizes PyTorch code into LightningModule with training_step, validation_step, and test_step methods; Trainer class handles device management, mixed precision, checkpointing, and logging automatically \n Supports distributed training strategies including DDP, FSDP, and DeepSpeed with single-line configuration; scales from laptop to multi-node clusters without code changes \n Bu

Expert guidance for deep learning, transformers, diffusion models, and LLM development with PyTorch. \n \n Covers PyTorch model architectures, transformers, diffusion models, and LLM fine-tuning with libraries including Transformers, Diffusers, and Gradio \n Emphasizes GPU optimization, mixed precision training, distributed training, and gradient accumulation for efficient workflows \n Includes best practices for data loading, train/validation splits, early stopping, learning rate scheduling, an

pytorch-fsdp