“Fine-tuning LoRa, rank 16, on 2,000 rows per arm, one epoch… 50 answers to each of 16 held-out questions at temperature 1, giving 800 answers per run.”

LessWrong (Curated & Popular) · "Personas in a Pre-Internet Language Model" by David Africa

Play from 4:44
4:44

Audio streams from the publisher’s own feed. Verbatim does not host or copy it.

What was described

fine-tuned models with LoRa adapters (rank 16) on 2,000 rows per arm, one epoch, using BF16 precision

4 training seeds per arm, 50 answers per 16 held-out questions at temperature 1 (800 answers per run)

Said nearby on the same subject

“You may need a bit of fine-tuning of your probes so that they work on each person's particular fine-tuned model”
EB
Eric Bigelow
"The Cognitive Revolution" ·
How they work Share this moment
“Model portfolio is 16.4% per annum versus the SPDR200, 7.5%... QAV light portfolio is 21.8% per annum versus 9.2%”
♫
Said on Quality At Value - Value Investing In Shares
Hard number 16.4% p.a. vs 7.5% SPDR200; QAV light 21.8% p.a. vs 9.2% benchmark Share this moment
“We asked inkling to fine tune itself using tinker. The model wrote its own fine tuning job, ran it and evaluated the result.”
♫
Said on Everyday AI Podcast - An AI and ChatGPT Podcast
Why it worked Share this moment
“We have a bunch of fine-tuned models ourselves too… making us more innovative… couldn’t do with the frontier models because of cost and efficiency reasons”
♫
Said on All-In with Chamath, Jason, Sacks & Friedberg
Why it worked Share this moment

Moments like this, every Monday

The strongest new moments on your topics, in the speakers' own words. Free for three topics.

No card. No password. One-click unsubscribe.

Track a competitor?

Pro watches up to 25 names or phrases and emails you the morning after they are said.

See Pro, $29 a month

Verbatim quotes a short extract and links to the episode; the episode belongs to its publisher. Remove a show.