Fine-tuning the foundation LLM
Foundation model fine-tuning is crucial as it adapts the model’s generalized knowledge to the specific nuances and requirements of a particular task or domain, significantly enhancing its performance and relevance. For a web page Q&A application, fine-tuning a foundation model on domain-specific data ensures that the generated answers are more accurate, contextually appropriate, and tailored to the unique content and user queries associated with the website. Let’s review the code that fine-tunes our T5 model with some examples from Feast:
from transformers import T5Tokenizer, T5ForConditionalGeneration
from feast import FeatureStore
import pandas as pd
import torch
fs = FeatureStore(repo_path="/path/to/your/feast_project")
entity_df = pd.DataFrame({
"entity_id": [1, 2],
"event_timestamp": pd.to_datetime(["2022-01-01", "2022-01-02"]),...
