Blogmarks tagged ai in Jan, 2023
Filters: Type: blogmark × Year: 2023 × Month: Jan × ai × Sorted by date
OpenAI Cookbook: Techniques to improve reliability (via) “Let’s think step by step” is a notoriously successful way of getting large language models to solve problems, but it turns out that’s just the tip of the iceberg: this article includes a wealth of additional examples and techniques that can be used to trick GPT-3 into being a whole lot more effective. # 21st January 2023, 5:15 am
Petals (via) The challenge with large language models in the same scale ballpark as GPT-3 is that they’re large—really large. Far too big to run on a single machine at home. Petals is a fascinating attempt to address that problem: it works a little bit like BitTorrent, in that each user of Petal runs a subset of the overall language model on their machine and participates in a larger network to run inference across potentially hundreds of distributed GPUs. I tried it just now in Google Colab and it worked exactly as advertised, after downloading an 8GB subset of the 352GB BLOOM-176B model. # 2nd January 2023, 11:29 pm
nanoGPT. “The simplest, fastest repository for training/finetuning medium-sized GPTs”—by Andrej Karpathy, in about 600 lines of Python. # 2nd January 2023, 11:27 pm