LocalLLaMA

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Community to discuss about Llama, the family of large language models created by Meta AI.

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submitted 10 months ago by manjimin@alien.top to c/localllama@poweruser.forum

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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:

Will I be able to fine-tune the model with 4 cards of 40G A100?
If not, is using runpod the easiest approach?
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!

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[–] thereisonlythedance@alien.top 1 points 10 months ago (3 children)

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.
RunPod is what I've been using and it's straight forward.
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.top 1 points 10 months ago (2 children)

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

Lik $1000+?

[–] DirectionOdd9824@alien.top 1 points 10 months ago

you get 14hrs of a100 80gb with 25 dollars.

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