Yes. Just write your thoughts in a rough and unpolished form, say rough paragraphs that contain terse points you want to make. then let 'er rip

Section 7 is a wonderful description of the process they went through.

something just isn't fully clicking. if you look at total yards and time of possession, they should have blown them out. well, better anyway to peak later in season, so let's hope that's what happens (like two seasons ago)

Packers get the win, but it wasn't pretty.

Google promotes box shirts too

Pour into

More likely midges. The truest sign of a healthy ecosystem

This is collaboration with Ziyue Luo, @shroffness and @kevinlauka

Jifan’s on the industry job market now, and his expertise in efficient training, distillation, and data curation couldn't be more timely. Feel free to reach out to him at [email protected].
📄 Paper: arxiv.org/abs/2410.02755

SIEVE improves upon existing quality filtering methods in the DataComp-LM challenge, producing better LLM pretraining data that led to improved model performance.
This work is part of Jifan's broader research on efficient ML training, from active learning to label-efficient SFT for LLMs.

Why does this matter? High-quality data is the bedrock of LLM training. SIEVE enables filtering trillions of web data for specific domains like medical/legal text with customizable natural language prompts.

SIEVE distills GPT-4's data filtering capabilities into lightweight models at <1% of the cost. Not just minor improvements - we're talking 500x more efficient filtering operations.

Maybe Trump should have read my mom's book: "For the first six weeks, the embryo, whether XX or XY, coasts along in sexual ambiguity." p. 25

Good luck with that

p.s. we don't know for sure if I said this or not

Is the solution treating everything electronic as "fake"?
Maybe?