Data Preparation for Agent Platform Model Tuning
Agent Platform Model Tuning requires training data in JSON Lines (JSONL) format stored in Google Cloud Storage (GCS).
Supported JSONL Formats for Open Models
1. Conversational (Messages) Format
Recommended for chat-based models (Llama 3.1/3.2/3.3 Chat, Gemma 3 IT, etc.).
{
"messages": [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "What is the capital of France?"},
{"role": "assistant", "content": "The capital of France is Paris."}
]
}
2. Instruction (Prompt/Completion) Format
Suitable for base models or simple completion tasks.
{
"prompt": "Summarize the following text: [TEXT]",
"completion": "[SUMMARY]"
}
Dataset Requirements
- File Type: Must be
.jsonl. - Encoding: UTF-8.
- Location: Must be in a GCS bucket (e.g.,
gs://my-bucket/train.jsonl). - Validation Split: A separate validation file is optional but recommended. It must be no more than 25% of the training dataset size.
Bucket Considerations
If a bucket does not exist, create one in the same region as your tuning job:
gcloud storage buckets create gs://YOUR_BUCKET_NAME --location=YOUR_LOCATION
Formatting Best Practices
- Quality over Quantity: 100 high-quality examples often outperform 1,000 noisy ones.
- Consistency: Use consistent formatting for system prompts and instruction styles.
- No Empty Values: Ensure every example has a valid prompt/user message and completion/assistant response. Use the preparation script to validate this.