Fine-tuning Gemini models on your own data is less about teaching the model "new facts" and more about teaching it to understand your domain’s language and desired output style.
Let’s see how this looks in practice. Imagine you’re a legal firm and want Gemini to draft initial client intake summaries.
First, you need a dataset. This isn’t just raw text; it needs to be structured. A common format is JSON Lines (JSONL), where each line is a JSON object representing a single example.
{"input_text": "Client Name: John Doe\nDate: 2023-10-27\nIssue: Car accident on Elm Street, rear-ended by a red truck.\nDamages: Vehicle damage, whiplash.\nContact: 555-1234", "output_text": "Client John Doe was involved in a car accident on October 27, 2023. While driving on Elm Street, their vehicle was rear-ended by a red truck. The client sustained vehicle damage and whiplash. They can be reached at 555-1234."}
{"input_text": "Client Name: Jane Smith\nDate: 2023-10-26\nIssue: Slip and fall at grocery store, \"Fresh Foods\". Slipped on a wet floor near produce.\nInjuries: Broken ankle.\nWitnesses: None observed.", "output_text": "Jane Smith reported a slip and fall incident at \"Fresh Foods\" on October 26, 2023. The fall occurred due to a wet floor in the produce section, resulting in a broken ankle. No witnesses were observed at the time of the incident."}
This dataset is crucial. The input_text is what you’d feed the model, and output_text is the exact kind of response you want. The model learns the mapping between these two.
On Vertex AI, you’ll navigate to the Generative AI section and find the "Model Garden." Here, you can select a base Gemini model (e.g., gemini-1.0-pro). You’ll then choose the "Fine-tuning" option.
The fine-tuning job requires several parameters:
- Base Model:
gemini-1.0-pro(or your chosen Gemini variant). - Training Dataset: A Cloud Storage URI pointing to your JSONL file (e.g.,
gs://your-bucket/legal-intake-dataset.jsonl). - Validation Dataset (Optional but Recommended): Another JSONL file for monitoring performance during training.
- Hyperparameters:
epoch_count: Typically 1 to 3. More epochs can lead to overfitting if the dataset is small.learning_rate_multiplier: Often a value like0.00002or0.00005. This controls how aggressively the model updates its weights.batch_size: Depends on your data and available resources. Common values are 16, 32, or 64.
Once you configure these and start the job, Vertex AI provisions the necessary compute resources. The process involves feeding your data to the base model and adjusting its weights to minimize the difference between its generated outputs and your desired output_text for each input_text.
After training, you’ll get a new, custom model endpoint. You can then deploy this model and use it via the Vertex AI API, just like you would a base model, but it will now be specialized.
from vertexai.generative_models import GenerativeModel, Part
model = GenerativeModel("projects/your-project-id/locations/us-central1/models/your-custom-model-id")
prompt = Part.from_text("Client Name: Alice Wonderland\nDate: 2023-10-28\nIssue: Neighbor's dog barking incessantly for weeks.\nImpact: Lack of sleep, stress.\nDesired outcome: Mediation with neighbor.")
response = model.generate_content(prompt)
print(response.text)
The output might look like: "Alice Wonderland has been experiencing significant stress and sleep deprivation due to a neighbor’s dog barking continuously for several weeks. She is seeking mediation with the neighbor to resolve this ongoing nuisance."
The model learns to extract key entities (client name, date, issue, impact, desired outcome) and rephrase them into a concise, professional summary that aligns with the style of your training data. It’s not just regurgitating; it’s synthesizing based on the pattern you’ve shown it.
The most surprising part of fine-tuning is how little data can sometimes yield significant improvements when the task is narrow and the data is high-quality. A few hundred carefully crafted examples can shift a model’s behavior more effectively than thousands of noisy, generic ones. The model isn’t learning what a car accident is, but rather how you want to describe a car accident summary for your intake process.
The next challenge after fine-tuning for summarization is often dealing with nuanced conversational follow-ups or handling cases where the input data is incomplete or ambiguous.