> The largest model in the PaLM 2 family, PaLM 2-L, is significantly smaller than the largest PaLM model but uses more training compute
The largest PaLM model is 540B. So all of PaLM 2 is potentially double-digit parameters.
Note though that GPT-3.5 was plausibly not a finetuning of the 175B model, but instead a finetuning of Codex which was based on the 12B version of GPT-3.
Token embeddings can be trained without changing the other parameters. There is a number of models which add tokens as a finetuning step. Here is recently StarCoder adding ChatML-equivalent tokens: https://huggingface.co/blog/starchat-alpha#a-standard-format...
If the extrapolation is not too flawed, it looks like PaLM 2-S might be about 120B, PaLM 2-M 180B, PaLM 2-L 280B.
Still, I would expect GPT-4 trained for way longer than Chinchilla, so it could be smaller than even PaLM 2-S.
There's no way it's 120B parameters. It's probably not even 12B.
At I/O, I think they were referencing the scaling law experiments: there are four of them, just like the number of PaLM 2 codenames they cited at I/O (Gecko, Otter, Bison, and Unicorn). The largest of those smaller-scale models is 14.7B, which is too big for a phone too. The smallest is 1B, which can fit in 512MB of RAM with GPTQ4-style quantization.
Either that, or Gecko is the smaller scaling experiment, and Otter is PaLM 2-S.