metadata

license: mit
tags:
  - phi3
  - nlp
  - moe
datasets:
  - BEE-spoke-data/gutenberg-en-v1-clean
  - NeelNanda/pile-10k

phi 3 4x4b

a continually pretrained phi3-mini sparse moe upcycle

benchmarks

ran locally

	Microsoft/phi-3-4k-instruct	Fizzarolli/phi3-4x4b-v1
MMLU acc. (0-shot)	0.6799	0.6781
Hellaswag acc. (0-shot)	0.6053	0.5962
ARC-E acc. (0-shot)	0.8325	0.8367
ARC-C acc. (0-shot)	0.5546	0.5606

honestly i was expecting it to do worse :p, but those are all within a margin of error! so it didn't lose any performance, at least

open llm leaderboard

todo!

support me on ko-fi!

~~please i need money to stay alive and keep making models~~

notes

not trained on instruct data. it's pretty likely that it won't be much different from phi 3 if you use it like that, if not worse due to any forgetting of instruct formats during the continued training.

future experiments

the datasets for this were literally chosen on a whim. perhaps experiment with a further filtered HuggingFaceFW/fineweb-edu?
actually freeze the gate layers next time (see Chen et. al, 2023), oops
MOAR TRAINING, this only went up to ~0.2 of an epoch because i ran out of dolar