Fine-tuning LLM model

• Create a project or select existing
• Enable App Engine and Firestore API's
• Create Firestore database
• Create bucket for the training data:

gsutil mb -l us-central1 gs://fine-tuning-training-data

• Run pip install -r requirements. txt
• Run gcloud app create
• Run gcloud app deploy
• To create a cloud function for extracting the training data and transforming in to .jsonl file, inside the /cloudfunction dir run:

gcloud functions deploy export_to_storage --runtime python39 --trigger-http --region us-central1 --entry-point export_to_storage --allow-unauthenticated --timeout 540s --memory 128MB

• Test cloud function with:

curl -m 550 -X POST https://us-central1-YOUR_PROJECT_ID_HERE.cloudfunctions.net/export_to_storage \ -H "Authorization: bearer $(gcloud auth print-identity-token)" \ -H "Content-Type: application/json" \ -d '{}' dont forget to set your project id./

TODO

• Train the model at OpenAI API with custom data
• Build the full pipeline on the cloud

*maybe some instructions are missing