5 ms·
Models learn. It just costs $10B and 1 year to do what a human does every night.
by kridsdale1 3mo ago
Models learn. It just costs $10B and 1 year to do what a human does every night.
- wongarsu 3mo agoLoRAs or even full fine-tunes would be much cheaper than that, and with some investment in the right infra could be updated regularly. And at least LoRAs can be swapped in and out cheaply, making them usable in large-scale inference providers. But there seems to be limited appetite in offering this. Both Anthropic and OpenAI no longer offer fine tuning for current models
- 8note 3mo agodoes lora do a good job at teaching the model new things that werent in the training data? without trillions of examples of following instructions at a million context length, im not convinced the behaviour is in the weights to begin with
- irthomasthomas 3mo agoNo. Lora is for tuning behaviour and how the model applies what it learned in training. Teaching a model new facts is still expensive.
- didgeoridoo 3mo agoThere was a recent-ish paper[0] from Sakana AI about baking facts from a document corpus into a LoRA adapter. Claimed near perfect recall on very large needle-in-haystack testing. Haven’t tried it myself though. [0]: https://sakana.ai/doc-to-lora/ https://sakana.ai/doc-to-lora/
- irthomasthomas 3mo agointeresting... thanks.
- cadamsdotcom 3mo agoIs this because of catastrophic forgetting, or is that more or less solved? Is it, alternatively, because loading your LoRA plus someone else's can't be done in the same batch, so inference is made harder or becomes impossible to do at scale? Or something else?
- pixl97 3mo agoAt least these costs are currently preventing the planets surface from being covered in paperclip maximizers for the moment.
- gls2ro 3mo agoThis is not about learning only. It is about context window too so no matter how much money you put to train your model it can still have degraded performance in following instructions but it will be better to execute the instructions they can follow. It is also about the harness and how that can help drive the model and pick and choose what to include or not in the context. These LLMs do not understand the project. For them any next word is good as long as it was picked by the token predictor. It does not have any way to understand but only to do. probability distribution over their vocabulary and if that vocabulary is tainted and lost parts of the original context what is a good candidate there will not match the intention of the initial project.
- deleted 3mo ago[deleted]
- chrisjj 3mo agoThat's not a model learning. It is model replacement.