Principle:Mistralai Client python Model And File Management

Knowledge Sources	Mistral AI Docs Mistral Client Python
Domains	Fine_Tuning, Resource_Management
Last Updated	2026-02-15 14:00 GMT

Overview

A resource lifecycle management pattern that lists/retrieves fine-tuned models and deletes uploaded training files after job completion.

Description

Model and File Management covers the post-training resource lifecycle. Models.list() and Models.retrieve() enable discovering and inspecting available models (both base and fine-tuned). Files.delete() removes uploaded training files that are no longer needed, reclaiming storage. This cleanup step prevents unnecessary resource consumption.

Usage

Use model management after a fine-tuning job succeeds to verify the fine-tuned model is available. Use file deletion after training to clean up uploaded training data files that are no longer needed.

Theoretical Basis

Resource lifecycle management:

1. Models.list() — Discover all available models (base + fine-tuned)
2. Models.retrieve(model_id=...) — Get detailed info about a specific model
3. Files.delete(file_id=...) — Remove training files after job completion
4. Clean up prevents storage costs and data retention issues

Related Pages

Implemented By

• Implementation:Mistralai_Client_python_Models_And_Files_Management