4 ms·

2k lines is (probably) not a lot of data.

• phillipcarter 3y ago
  That may be the issue. I can get about 6k lines of data today and in time, could have a bunch more. Each line represents a full request (user input, model output, full prompt, etc.) so I would intuitively think that 2k of these is good enough. And it's actually good enough for llama-2-7b, insofar as it transforms this model from "terrible with my prompt in production today" to "this could be workable, actually". But my bar is to be better than gpt-3.5 and require less text in my prompt, but no fine-tuning I've done so far has achieved that.
  • kiratp 3y ago
    It’s not reasonably possible (currently?) to get the same performance from a 7 billion parameter model as a 175 billion parameter model with just an additional 6000 lines of finetuning data.
    • phillipcarter 3y ago
      It gets very similar performance, so maybe it is possible. But also when I fine-tune 3.5, its response quality is indistinguishable from when I use the base model. All of this is to say: this shit is way harder than it needs to be. I'm not an ML engineer but I do know my data and how to get it. Why is it still so hard to specialize a model?
      • kiratp 3y ago
        Because we don’t have a clear understanding of how the things work yet.