Now, fine-tune the Language Model for text classification using the created training view. In the following sections, you will see a detailed explanation of different parameters used during fine-tuning.

Now, fine-tune the Language Model for text classification using the created training view. In the following sections, you will see a detailed explanation of different parameters used during fine-tuning. Fine-tuned model is pushed to your public Hugging Face Hub periodically. A new repository will be created under your username using your project name (`imdb_review_sentiment` in this case). You can also choose to push the model to a private repository by setting`hub_private_repo: true` in training arguments.

SELECTpgml.tune(

By following these steps, you can effectively restart trainingfrom a previously trained model, allowing for further refinementand adaptation of the model basedon new requirementsor insights. Adjust parametersas needed for your specific use caseand dataset.

We utilize the Hugging Face Hubas the primary repository for fine-tuning Large Language Models (LLMs). Leveraging the HF hub offers several advantages:

* The HF repository servesas the platform for pushing incremental updates to the model during the training process.In the event of any disruptionsin the database connection, you have the flexibility to resume trainingfromwhere it was left off.

* If you prefer to keep the model private, you can push it to a private repository within the Hugging Face Hub. This ensures that the model is not publicly accessible by setting the parameter hub_private_repo to true.

* Thepgml.transform function, designed around utilizing modelsfrom the Hugging Face Hub, can be reused without any modifications.

However,in certain scenarios, pushing the model to a central repositoryand pulling it for inference may not be the most suitable approach. To address this situation, we save all the model weightsand additional artifacts, suchas tokenizer configurationsand vocabulary,in thepgml.files table at the end of the training process. It's important to note that as of the current writing, hooks to use models directly from pgml.files in the pgml.transform function have not been implemented. We welcome Pull Requests (PRs) from the community to enhance this functionality.