[joshstrange]

> This will reduce token size, performance & operational costs.

How? The models aren't trained on compressed text tokens nor could they be if I understand it correctly. The models would have to uncompress before running the raw text through the model.

[the_arun]

That is what I am looking for. a) LLMs are trained using compressed text tokens and b) use compressed prompts. Don't know how..but that is what I was hoping for.

[deepdarkforest]

The whole point of embeddings and tokens are that they are a compressed version of text, a lower dimensionality. now, how low depends on performance, lower amount of vectors=more lossy (usually). https://huggingface.co/spaces/mteb/leaderboard

You can train your own with very very compressed, i mean you could even go down to each token=just 2 float numbers. It will train, but it will be terrible, because it can essentially only capture distance.

Prompting a good LLM to summarize the context is probably funnily enough the best way of actually "compressing" context

[rattyJ2]

Tokens are already compressed. That's what tokenisation is.