Fine-tuning GPT-J for conversations
HelloI would like to fine-tune a GPT-J model for conversations that is running locally on my machine. There are two models that I can use: Theoriginal GPT-J modelor theQuantized EleutherAI/gpt-j-6b with 8-bit weights. I have a machine with a 24GB GPU (RTX 3090). How much GPU memory would the original GPT-J model need for fine-tuning and for inference? As far as I understand, the main advantage of the quantized GPT-J is that it needs less GPU memory.Second, I would like to fine-tune the GPT-J model on conversation datasets such asdaily dialog,Blended Skill Talk(but without different personas), Multi-Session Chat and Wizard of the Internet.In general, for fine-tuning GPT-J should I just format the conversation in the following way?Person_a: Say , Jim , how about going for a few beers after dinner ?Person_b: You know that is tempting but is really not good for our fitness.Person_a: What do you mean ? It will help us to relax .…Or are there any other delimiters such as<|endoftext|>necessary? During inference, when the user is for example sending “Hello, how are you?” to the chatbot, I would then format it as “Person_a: Hello, how are you? Person_b:”.Finally, for fine-tuning I see the following options:Fine-tuning on only one conversation dataset.Fine-tuning on several conversation dataset and just stacking the datasets.Fine-tuning on the first dataset, then fine-tuning on the second dataset and so on.Which of these three options is best?I’m happy about any input. Thank you very much in advance.
2022-07-29T22:19:41Z
You could use deepspeed to reduce the system requirements needed for training.https://github.com/mallorbc/Finetune_GPTNEO_GPTJ6B
2023-01-15T08:42:29Z
Have you found the answer? I am looking to finetune the model for these conversation dataset as well!