[billythemaniam]

Flan-T5 is much smaller than GPT-3, but was trained on significantly more data resulting in competitive accuracy. It is also Apache licensed. I wonder if that model is fast enough for enough use cases to make it cost effective?

[danielbln]

You can give the Xl (3B parameters) model a try here (would recommend a Colab Pro account): https://colab.research.google.com/drive/1Hl0xxODGWNJgcbvSDsD...

In my Colab Pro it's running this on a A100 (which is a very beefy GPU) and inference is very fast and definitely suitable for interactive use. On a T5 GPU (which is much cheaper) inference is still alright and probably ok for interactive use.

[ntonozzi]

I think Flan-T5 is fast enough, but I don't think it generates text or abstract reasoning at nearly the same level as current GPT-3 models. This indicates a deficiency in the benchmarks and metrics that we use to evaluate LLMs. For generating embeddings it might work well enough though.

[billythemaniam]

It's certainly not quite as good out of the box, at least the open sourced checkpoints. However so far I found it can achieve similar accuracy with enough examples and/or fine-tuning for my use cases. Like everything, it depends on what are doing too.

[throwaway1851]

For embeddings, it may be overkill. Smaller BERT-type models can provide good embeddings when fine tuned with a contrastive learning objective. Eg: https://sbert.net.

[williamcotton]

Fine-tuning on smaller models like GPT-J (also trained on The Pile) worked well for Toolformer:

https://arxiv.org/abs/2302.04761