> ## Documentation Index
> Fetch the complete documentation index at: https://docs.monostate.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# When to Use the Chat Interface

> Visual interface for testing and interacting with trained models

# When to Use the Chat Interface

The Chat interface lets you test and interact with your trained models in a browser.

## What It Does

The Chat interface (`aitraining chat`) provides:

* Interactive conversation with your trained models
* Real-time response generation
* Conversation history
* Model parameter adjustment (temperature, max tokens, etc.)

## Best For

* **Testing trained models** - Verify your fine-tuned model works as expected
* **Quick experiments** - Try different prompts and parameters
* **Demos** - Show stakeholders what your model can do
* **Debugging** - Identify issues in model responses

## What It Looks Like

Open your browser to the chat interface:

* Type messages in a chat box
* See model responses in real-time
* Adjust generation parameters
* View conversation history

## Starting the Chat Interface

```bash theme={null}
# Start the chat interface
aitraining chat

# With custom port
aitraining chat --port 7860

# With custom host
aitraining chat --host 0.0.0.0 --port 7860
```

Then open `http://localhost:7860` in your browser.

## Workflow Example

1. Train your model with CLI: `aitraining llm --train ...`
2. Start chat interface: `aitraining chat`
3. Open browser to `localhost:7860`
4. Select your trained model
5. Start chatting to test responses
6. Adjust temperature/parameters as needed
7. Iterate on training if needed

## Advantages

* **Immediate feedback** - See responses instantly
* **No coding required** - Just type and chat
* **Visual interface** - Easy to use
* **Parameter tuning** - Adjust generation settings in real-time

## Limitations

* **Not for training** - Use CLI or API for training
* **Local only** - Must access the machine running it
* **Single model** - Test one model at a time

## When to Use Something Else

**Use CLI when you:**

* Need to train models
* Want to automate workflows
* Need batch processing
* Want reproducible experiments

**Use API when you:**

* Build applications
* Need programmatic control
* Integrate with other systems
* Deploy to production

## Common Use Cases

### Post-Training Verification

"Did my fine-tuning work?"

* Load trained model
* Test with sample prompts
* Verify response quality

### Parameter Exploration

"What temperature works best?"

* Try different generation settings
* See effects immediately
* Find optimal parameters

### Demo Preparation

"Show the team what we built"

* Visual, easy to understand
* Interactive demonstration
* No technical setup needed

## Tips

1. **Start with low temperature** - More consistent responses for testing
2. **Save good prompts** - Document what works
3. **Compare models** - Test before/after fine-tuning
4. **Check edge cases** - Try unusual inputs

## Next Steps

<CardGroup cols={2}>
  <Card title="Launch Chat" href="/chat/launching">
    Get started with the chat interface
  </Card>

  <Card title="CLI Training" href="/cli/llm-training">
    Train models with the command line
  </Card>
</CardGroup>