Skip to main content

​
Which Interface to Use

AI Training offers three ways to work. Pick based on your needs.

​
Quick Comparison

InterfaceBest ForWhat It Does
CLITraining modelsRun training jobs, automation
ChatTesting modelsInteractive chat with trained models
APIBuilding appsIntegrate training into applications

​
The Three Options

CLI

Training modelsType commands to train models. Perfect for automation and scripts.aitraining llm --train ...

Chat

Testing modelsInteractive browser interface to test trained models.aitraining chat

Python API

Building appsImport and use in Python code.from autotrain.trainers.clm import train

​
Simple Decision Guide

Training a model? → Use the CLI Testing a trained model? → Use the Chat interface Building an application? → Use the Python API

​
Typical Workflow

Most users follow this pattern:
  1. Train with CLI - aitraining llm --train --model meta-llama/Llama-3.2-1B --data-path ./data
  2. Test with Chat - aitraining chat → open browser → try your model
  3. Iterate - Adjust training, test again
  4. Deploy with API - Integrate into your application

​
They All Work Together

The interfaces complement each other:
  • CLI handles the heavy lifting (training)
  • Chat lets you verify results quickly
  • API enables production integration

​
Combining Interfaces

You’re not locked into one:
  1. Train with CLI → Test with Chat → Deploy with API
  2. Experiment with API → Automate with CLI
  3. Quick test with Chat → Full training with CLI

​
Migration is Easy

The interfaces share the same underlying settings: CLI uses flags → API uses parameters 
CLI: --learning-rate 2e-5 --batch-size 4
API: learning_rate=2e-5, batch_size=4
Same values, different format.

​
Next Steps

Ready to choose? Dive into your interface:

When to Use CLI

Command line training

When to Use Chat

Testing models

When to Use API

Python integration