zlacker

>>mfigui+M3
Frankly, OpenAI seems to be losing its luster, and fast.

Plugins were a failure. GPTs are a little better, but I still don't see the product market fit. GPT-4 is still king, but not by that much any more. It's not even clear that they're doing great research, because they don't publish.

GPT-5 has to be incredibly good at this point, and I'm not sure that it will be.

>>skepti+(OP)
Running Ollama with a 80gb mistral model works as well if not better than ChatGPT 3.5. This is a good thing for the world IMO as the magic is no longer held just OpenAI. The speed at which competitors have caught up in even the last 3 months is astounding.

>>roody1+Sf
But no one cares about 3.5. It’s an order of magnitude worse than 4. An order of magnitude is a lot harder to catch up with.

>>huyter+5h
What Mistral has though is speed, and with speed comes scale.

>>sjwhev+il
Who cares about speed if you’re wrong?

This isn’t a race to write the most lines of code or the most lines of text. It’s a race to write the most correct lines of code.

I’ll wait half an hour for a response if I know I’m getting at least staff engineer level tier of code for every question

>>spacem+Tm
That’s the correct answer. Years ago I worked on inference efficiency on edge hardware at a startup. Time after time I saw that users vastly prefer slower, but more accurate and robust systems. Put succinctly: nobody cares how quick a model is if it doesn’t do a good job. Another thing I discovered is it can be very difficult to convince software engineers of this obvious fact.

>>ein0p+lx
Yes, but for certain classes of problems small LLM are highly performant - in many cases equal to a GPT-4, which sure can do more things well, but adding 2+2 is gonna be 4 no matter what. You don’t need a tank to drive to the grocery store, just a small car with a trunk.

So the assertion that small models aren’t as good just isn’t correct. They are amazing at certain things, and are incredibly faster and cheaper than larger models.