Principle:Mistralai Client python Training Data Preparation
| Knowledge Sources | |
|---|---|
| Domains | Fine_Tuning, Data_Preparation |
| Last Updated | 2026-02-15 14:00 GMT |
Overview
A data formatting pattern that structures training examples into JSONL conversation format required by the Mistral fine-tuning API.
Description
Training Data Preparation transforms raw training data into the JSONL (JSON Lines) format required by the Mistral fine-tuning API. Each line contains a JSON object with a messages array following the chat format (system, user, assistant roles). The data must be saved as a .jsonl file for upload. Quality and diversity of training examples directly impact the fine-tuned model's performance.
Usage
Use this principle before uploading training data for fine-tuning. Ensure each example follows the conversation format with proper roles and content. A minimum number of examples is required (typically 10+), and validation data is recommended for monitoring overfitting.
Theoretical Basis
The JSONL format requirements:
- One JSON object per line (no trailing commas or array wrappers)
- Each object has a messages key with a list of role/content dicts
- Roles: "system" (optional), "user" (required), "assistant" (required — the target)
- The model learns to generate the assistant's content given the preceding context
# Example JSONL format (pseudocode)
{"messages": [{"role": "user", "content": "Q"}, {"role": "assistant", "content": "A"}]}
{"messages": [{"role": "system", "content": "..."}, {"role": "user", "content": "Q"}, {"role": "assistant", "content": "A"}]}