Export Fine-Tuned LLM after Trainer is Complete

[andreyvelich]

We discussed here: kubeflow/website#3718 (comment) that our LLM Trainer doesn't export the fine-tuned model.
So user can't re-use that model for inference or other purposes.

We should discuss how user can get the fine-tuned artifact after LLM Trainer is complete.
/cc @kubeflow/wg-training-leads @deepanker13

Would be nice to see integration with Kubeflow Model Registry as well. cc @kubeflow/wg-data-leads

[tarilabs]

Would be nice to see integration with Kubeflow Model Registry as well. cc @kubeflow/wg-data-leads

If there is a tutorial of the part specific to this project that exhibit the metadata we want to capture on Model Registry, I would be very happy to complement that example with indexing those metadata on MR ! 🚀👍

[StefanoFioravanzo]

@andreyvelich I may have misunderstood the initial context of this API because I was under the impression that you could serve the model once fine-tuned. Can you elaborate on this?

So user can't re-use that model for inference or other purposes.

[andreyvelich]

@andreyvelich I may have misunderstood the initial context of this API because I was under the impression that you could serve the model once fine-tuned. Can you elaborate on this?

So user can't re-use that model for inference or other purposes.

I think, right now the only way is to use output_dir for model checkpoints.
In that case, user can get the model from PVC that we attach to the PyTorchJob.
Like in this example: https://github.com/kubeflow/training-operator/blob/master/examples/pytorch/language-modeling/train_api_hf_dataset.ipynb
Right @johnugeorge @deepanker13 ?