21–30 of 244 posts
Quite exciting, this is the first time their "Chat" model is available for finetuning. Has anyone successfully bypassed the current Ai detectors using fine-tuned models? I know it's possible, I'm just trying to conceptualize how the dataset would be organized...
> It is very important to us that the deployment of fine-tuning is safe. To preserve the default model's safety features through the fine-tuning process, fine-tuning training data is passed through our Moderation API and a GPT-4 powered moderation system to detect unsafe training data that conflict with our safety standards. I wish there was some documentation on what kinds of things are determined unsafe. There are…
Quite exciting, this is the first time their "Chat" model is available for finetuning. Has anyone successfully bypassed the current Ai detectors using fine-tuned models? I know it's possible, I'm just trying to conceptualize how the dataset would be organized...
I think you can just use the base model easily.
Generating from a finetuned GPT 3.5 Turbo is 8x the cost of generating from the base model, so you really have to be in the “reduce prompt size by 90%” bucket they mention to get cost effectiveness out of it.
If you do a lot of retrieval augmentation, then the 8x cost might still be cheaper than burning a lot of tokens on injected context.
Can anyone who's worked with fine-tuning models this large weigh in on how much fine-tuning data is typically required to be effective? Can 100k tokens (as mentioned in the docs) really influence the behavior of the base model that much, or was that just a toy example?
Mechanisms like LoRA (a very efficient fine-tuning mechanism that has a accuracy penalty) change only a few layers at the top to alter the model considerably.
Earlier quoted context omitted.
If you do a lot of retrieval augmentation, then the 8x cost might still be cheaper than burning a lot of tokens on injected context.
Fine tuning is not a replacement for retrieval augmentation. If you wanted to do that you'd be better off fine-tuning BERT to retrieve data more effectively (identify key points, smarter summarization, cross-encoder relevancy, etc)
Im definitely waiting for the UI. The process warrants it. Bringing back memories of fine tuning using the console gives me the chills.
Quite exciting, this is the first time their "Chat" model is available for finetuning. Has anyone successfully bypassed the current Ai detectors using fine-tuned models? I know it's possible, I'm just trying to conceptualize how the dataset would be organized...
If you actually try the AI "detectors" you'll find that they're about as accurate as a coin flip. They don't work. You already cannot detect GPT-created text.
Can anyone who's worked with fine-tuning models this large weigh in on how much fine-tuning data is typically required to be effective? Can 100k tokens (as mentioned in the docs) really influence the behavior of the base model that much, or was that just a toy example?
> To fine-tune a model, you are required to provide at least 10 examples. We typically see clear improvements from fine-tuning on 50 to 100 training examples with gpt-3.5-turbo but the right number varies greatly based on the exact use case.
> We recommend starting with 50 well-crafted demonstrations and seeing if the model shows signs of improvement after fine-tuning. In some cases that may be sufficient, but even if the model is not yet production quality, clear improvements are a good sign that providing more data will continue to improve the model. No improvement suggests that you may need to rethink how to set up the task for the model or restructure the data before scaling beyond a limited example set.