I’m planning to fine-tune a mistral model with my own dataset. (full fine-tune, not LORAs)

The dataset is not that large, around 120 mb in jsonl format.

My questions are:

  1. Will I be able to fine-tune the model with 4 cards of 40G A100?
  2. If not, is using runpod the easiest approach?
  3. I’m trying to instill knowledge in a certain language, for a field it does not have sufficient knowledge in said language. Is fine-tuning my only option? RAG is not viable in my case.

Thanks in advance!

  • thereisonlythedance@alien.topBEnglish
    1·
    1 year ago
    1. I think you should be okay. I’ve been doing full fine-tunes on Mistral using either two A100s (80GB VRAM) with a total batch size of 8, or three A100s (80GB again) with a batch size of 12. This is using the Adam 8-bit optimizer and training with a max sequence length of 4096, lots of long samples. I think it should be possible to do a full fine tune on a single 80GB A100, but I haven’t tried. This is without Deep Speed. I’ve done a few Deep Speed runs and that significantly lowers VRAM usage.
    2. RunPod is what I’ve been using and it’s straight forward.
    3. Instilling new knowledge is possible with a fine tune, much less possible with a LoRA. Can’t comment on languages. People seem to have had mixed success creating multi-lingual models.
    • herozorro@alien.topBEnglish
      1·
      1 year ago

      how much does it cost to do these fine tunes on RunPod? How much compute time is used

      Lik $1000+?

      • thereisonlythedance@alien.topBEnglish
        1·
        1 year ago

        An A100 ((80GB) costs between $1.70=$1.99 per hour on RunPod. How long you need depends on dataset size, sequence length, the optimizer you choose and how many epochs you train for. I can get a full finetune of Mistral (5 epochs) with an Adam 8-bit optimizer done on my small (1300 samples) but long sequence length (most samples are 4096 tokens) dataset in around an hour with 3x A100s.