light24bulbs t1_jee9lvm wrote

I agree with you. Looking at papers like ToolFormer and so on, we are very close.

We are only a couple years away from AGI, which is what I've been saying for YEARS and getting yelled at here. The WaitButWhy article in 2016 was dead right


light24bulbs t1_je7mr9p wrote

False, they all do. The process of fine-tuning is identical to the initial pre-training, though perhaps with different settings. They're mostly setup to take q&a data for getting llama to take instructions better, but actually that's just text wrapped in some context and passed in straight up.

I was very confused by this as well but no, you can train new stuff.


light24bulbs t1_je7ilvq wrote

I disagree that it's not viable to train a model. The problem is that the best public model (llama) is non-commercial.

That said, it's extremely possible to train things into it. There's a ton of new scripts floating around online. The Lora training is especially good.

The trouble with vectors is they are so limited. They're fine if you need to look up one distinct thing (and the vector gets the match right) but they're utterly useless if you'd like the model to learn about something in general.


light24bulbs t1_jdzzeh4 wrote

Yes, I'm into it now. Code like this can be adapted to load bulk data instead of q&a.

I suspect some of the training parameters need to be adjusted a bit to prevent over fitting and obviously the data loading and templating needs to be removed. Or for a cooler approach where you make a Lora layer


light24bulbs t1_jdsulyn wrote

By "in context learning" i take it you mean zero shot.

Yes, you can hot swap. Id be unsurprised if what Open-AI did is fine tune on how to use plugins in general by giving some examples combined with a little bit of zero-shot primer.

Something trained with ToolFormers technique and then told it can use a new, but similar, plugin is IMO going to generalize way better than something that's never used a plugin before.


light24bulbs t1_jds3mdl wrote

What's the underlying approach here? Just prompt engineering right?

I really really want to apply the ToolFormer paper to llama. They're both Facebook systems, you can get they've done it.

ToolFormer just seems like SUCH a good and thorough approach. There are quite a few gaps between the paper and building a working example, IMO, but it's clearly doable.

The way Facebook licensed the weights is frustrating me. We should all be passing around Alpaca trained, GPTQ quantized, SparseGpt optimized Llama derived models by now. Is there some telegram group i need to be in or something?