GPT-3 Sized Models with Minimal Code

Last updated on December 15, 2023

For developers seeking simplicity in complexity, Cerebras introduces GigaGPT, a beautifully minimalistic approach to training GPT models that hits above the heavyweight mark. Reflecting the genius of Andrei Karpathy's nanoGPT, GigaGPT stands as the epitome of codebase compactness. Imagine training models with over 100 billion parameters with a code repository that's no thicker than a novella - precisely 565 lines. GigaGPT doesn’t just impress with brevity; it's designed to leverage the muscular memory and compute prowess of Cerebras hardware, making it possible to conduct large-scale training directly on the familiar terrain of vanilla torch.nn code. This means embracing long context lengths and a diverse range of optimizers without breaking a sweat. For developers and programmers who've tasted the limits of traditional GPUs and found them wanting, GigaGPT opens up a new frontier where large transformer models can be trained without the customary partitioning headache. So, if you're interested in pushing the boundaries of GPT models without getting entangled in the web of LLM scaling frameworks, then GigaGPT's elegant solution by Cerebras might just be your next port of call.