Principle:Googleapis Python genai Training Dataset Preparation
| Knowledge Sources | |
|---|---|
| Domains | Fine_Tuning, Data_Preparation |
| Last Updated | 2026-02-15 00:00 GMT |
Overview
A structured specification of training data for supervised fine-tuning of language models, supporting both file-based and inline example formats.
Description
Training Dataset Preparation defines how training data is provided to a fine-tuning job. Data can be supplied as a reference to a JSONL file in Google Cloud Storage, a Vertex AI Multimodal Dataset resource, or inline as a list of input/output text examples. The JSONL format contains examples with input-output pairs that teach the model to produce specific outputs for given inputs. Proper dataset preparation is critical for fine-tuning quality, as the model learns to mimic the patterns in the training examples.
Usage
Use GCS URI for production fine-tuning with large datasets stored in Cloud Storage. Use inline examples for small-scale experimentation or when data is dynamically generated. The dataset format must match the base model's expected schema (typically {"text_input": "...", "output": "..."} for text models).
Theoretical Basis
Supervised fine-tuning optimizes the model on labeled examples:
Where x_i are inputs and y_i are target outputs from the training dataset. The model parameters θ are updated to maximize the probability of generating the correct output for each input.
Dataset format (JSONL):
# Each line is a JSON object with input/output pair
{"text_input": "Classify: I love this product", "output": "positive"}
{"text_input": "Classify: Terrible experience", "output": "negative"}