HackerTrans
TopNewTrendsCommentsPastAskShowJobs

float-trip

no profile record

comments

float-trip
·2 years ago·discuss
Reddit's caches are set up to only ever return the last 1,000 of anything. So for example - you can't scroll past 1k items on /new, and if you save more than 1k posts then you'll have to unsave some to retrieve the others.

If this extension only edits comments, it'll only touch the most recent 1k. You would need to retrieve the older ones with a Pushshift replacement like this: https://pullpush.io/. But that also shows how ineffective this is. We still have public reddit archives (like Pullpush and https://github.com/ArthurHeitmann/arctic_shift) which contain comments as they were originally posted. This isn't gonna be a problem for Google.
float-trip
·3 years ago·discuss
Related comment from gwern: https://news.ycombinator.com/item?id=38438859. Can't find the docs now - I think they were the old GPT 3 ones - but they suggested a low value somewhere around 0.01 and 0.1.

Also - why qlora rather than a full finetune? Using LambdaLabs, it'd cost roughly the same as your quote. Cheaper I think if you're willing to gamble with fp8: https://github.com/mosaicml/llm-foundry/tree/main/scripts/tr.... And fewer hyperparameters to tune as well
float-trip
·3 years ago·discuss
That's what I ended up doing (`[Author] username [Title] post title...`)

> Adding new tokens needs a ton of data to train what the token means.

But how much? 300M tokens is fine for a simple version of ChatML with ~4 tokens. Not for 15, at least in my case. How's this relationship scale?

Just trying to offer one datapoint for what doesn't work, with the hedge that I might have just had a bug
float-trip
·3 years ago·discuss
I tried adding special tokens for a reddit-style dataset once. The format was: `<|post_author|>username<|post_title|>title here...`

The resulting model was so much worse than just formatting everything plaintext. This was with MPT-30B, 15 special tokens, 300M training tokens, and a full finetune.

I may have made a mistake, but I haven't seen any open source finetunes successfully add a large number of tokens yet either.
float-trip
·3 years ago·discuss
Thanks for writing up. Rather than zeroing out the loss for the prompt, did you also try using weighted loss with Axolotl? At one point, Microsoft's GPT 3 docs suggested this was beneficial when the responses are short (like you have with "Cut in.") Domain adaptation over subreddits/forums before finetuning may help as well.