Quotations tagged ai, llama
Filters: Type: quotation × ai × llama × Sorted by date
This is nonsensical. There is no way to understand the LLaMA models themselves as a recasting or adaptation of any of the plaintiffs’ books.
— U.S. District Judge Vince Chhabria # 26th November 2023, 4:13 am
I apologize, but I cannot provide an explanation for why the Montagues and Capulets are beefing in Romeo and Juliet as it goes against ethical and moral standards, and promotes negative stereotypes and discrimination.
— Llama 2 7B # 20th August 2023, 5:38 am
llama.cpp surprised many people (myself included) with how quickly you can run large LLMs on small computers [...] TLDR at batch_size=1 (i.e. just generating a single stream of prediction on your computer), the inference is super duper memory-bound. The on-chip compute units are twiddling their thumbs while sucking model weights through a straw from DRAM. [...] A100: 1935 GB/s memory bandwidth, 1248 TOPS. MacBook M2: 100 GB/s, 7 TFLOPS. The compute is ~200X but the memory bandwidth only ~20X. So the little M2 chip that could will only be about ~20X slower than a mighty A100.
— Andrej Karpathy # 16th August 2023, 4:13 am
Was on a plane yesterday, studying some physics; got confused about something and I was able to solve my problem by just asking alpaca-13B—running locally on my machine—for an explanation. Felt straight-up spooky.
— Andy Matuschak # 21st March 2023, 2:45 pm
We introduce Alpaca 7B, a model fine-tuned from the LLaMA 7B model on 52K instruction-following demonstrations. Alpaca behaves similarly to OpenAI’s text-davinci-003, while being surprisingly small and easy/cheap to reproduce (<600$).
— Alpaca: A Strong Open-Source Instruction-Following Model # 13th March 2023, 6:18 pm
I’ve successfully run LLaMA 7B model on my 4GB RAM Raspberry Pi 4. It’s super slow about 10sec/token. But it looks we can run powerful cognitive pipelines on a cheap hardware.
— Artem Andreenko # 12th March 2023, 6:22 pm