Thursday, 16th March 2023
Train and run Stanford Alpaca on your own machine. The team at Replicate managed to train their own copy of Stanford’s Alpaca—a fine-tuned version of LLaMA that can follow instructions like ChatGPT. Here they provide step-by-step instructions for recreating Alpaca yourself—running the training needs one or more A100s for a few hours, which you can rent through various cloud providers. # 4:10 pm
Not By AI: Your AI-free Content Deserves a Badge (via) A badge for non-AI generated content. Interesting to note that they set the cutoff at 90%: “Use this badge if your article, including blog posts, essays, research, letters, and other text-based content, contains less than 10% of AI output.” # 4:05 pm
As an NLP researcher I’m kind of worried about this field after 10-20 years. Feels like these oversized LLMs are going to eat up this field and I’m sitting in my chair thinking, “What’s the point of my research when GPT-4 can do it better?”
— Jeonghwan Kim # 5:39 am
I expect GPT-4 will have a LOT of applications in web scraping The increased 32,000 token limit will be large enough to send it the full DOM of most pages, serialized to HTML—then ask questions to extract data Or... take a screenshot and use the GPT4 image input mode to ask questions about the visually rendered page instead! Might need to dust off all of those old semantic web dreams, because the world’s information is rapidly becoming fully machine readable
bloomz.cpp (via) Nouamane Tazi Adapted the llama.cpp project to run against the BLOOM family of language models, which were released in July 2022 and trained in France on 45 natural languages and 12 programming languages using the Jean Zay Public Supercomputer, provided by the French government and powered using mostly nuclear energy.
It’s under the RAIL license which allows (limited) commercial use, unlike LLaMA.
Nouamane reports getting 16 tokens/second from BLOOMZ-7B1 running on an M1 Pro laptop. # 12:24 am