nanogpt-training

You can train GPT-2 scale models on a single GPU using a compact training loop that supports mixed precision and configurable optimizers like AdamW or Muon, enabling rapid prototyping without requiring heavy hardware.

Yes, you can load tokenized data shards for single-GPU pretraining directly from HuggingFace Hub or local storage. The workflow includes memory-friendly data handling to efficiently stream tokenized shards during the GPT-2 scale training process.

Mixed-precision training is an optimization technique used in the training loop to accelerate GPT-2 scale pretraining on a single GPU. It allows researchers to prototype quickly by reducing memory overhead and increasing computational throughput during experimentation.

Yes, the GPT-2 training workflow supports configurable optimizers including AdamW and Muon. This flexibility within the training loop allows researchers and engineers to easily experiment with different optimization strategies during single-GPU pretraining.

Yes, you need Python, PyTorch, and access to tokenized data shards to run this GPT-2 scale training workflow. It is specifically designed for researchers or engineers prototyping GPT-scale pretraining on a single GPU environment.

Training transformers on a single GPU limits you to rapid prototyping of smaller architectures like ~124M parameter GPT-2 scale models. It is not suited for training massive models requiring heavy hardware, but rather for quick experimentation and workflow validation.