> ## Documentation Index
> Fetch the complete documentation index at: https://docs.narrative.io/llms.txt
> Use this file to discover all available pages before exploring further.
# LLM Studio Interface
> Reference for the LLM Studio UI for training and fine-tuning AI models
This reference documents the UI elements, configuration options, and actions available in the LLM Studio interface.
## Overview
LLM Studio enables training and fine-tuning of AI models using datasets within Narrative's platform. It integrates datasets, base models, and compute resources into a streamlined workflow.
**Path:** My Models → LLM Studio
***
## Base Model module
The Base Model module lets you select the foundation model for fine-tuning.
| Element | Description |
| ----------------- | ------------------------------------------ |
| **Select button** | Opens the model selection dialog |
| **Model name** | Displays the currently selected base model |
| **Model details** | Shows model size and capabilities |
### Available base models
| Model | Description |
| ------------------- | -------------------------------------------- |
| **Llama-3.2-1B** | Meta's lightweight 1 billion parameter model |
| **Mistral-7b-v0.1** | Mistral AI's 7 billion parameter model |
Additional base models may be available. Check the model selection dialog for the current list.
***
## Training Data module
The Training Data module lets you select the dataset to use for fine-tuning.
| Element | Description |
| ----------------- | ---------------------------------------------------- |
| **Select button** | Opens the dataset selection dialog |
| **Dataset name** | Displays the currently selected training dataset |
| **Row count** | Shows the number of training examples in the dataset |
### Dataset requirements
Datasets must be mapped to a supported attribute and materialized in the corresponding format before use in LLM Studio.
| Attribute | Format | Description |
| ------------------------------ | ---------------------- | ------------------------------------------------------------------------------------- |
| **fine\_tuning\_conversation** | Conversation structure | Each row contains a structured conversation with system, user, and assistant messages |
Use [Prompt Studio](/reference/ui/prompt-studio) to transform datasets into the `fine_tuning_conversation` format.
### Accessing training data with NQL
To query conversation data from a prepared dataset:
```sql theme={null}
SELECT
d._rosetta_stone.fine_tuning_conversation.conversation
FROM company_data.my_dataset_name d
```
Additional fine-tuning attributes will be supported in future updates.
***
## Compute module
The Compute module lets you configure the compute resources for training.
| Element | Description |
| --------------------- | ------------------------------------------- |
| **Select button** | Opens the compute instance selection dialog |
| **Instance type** | Displays the selected compute configuration |
| **GPU configuration** | Shows GPU count and type |
### Compute instance selection
Choose an instance based on your training requirements:
| Factor | Consideration |
| ----------------- | -------------------------------------------------- |
| **Model size** | Larger models require more GPU memory |
| **Dataset size** | Larger datasets benefit from more compute capacity |
| **Training time** | Higher-tier instances reduce training duration |
Available instances include AWS G5 instances with various GPU configurations.
***
## Trained Model Details module
The Trained Model Details module captures metadata for the fine-tuned model.
| Element | Description |
| --------------- | ------------------------------------------------------ |
| **Add button** | Opens the metadata configuration dialog |
| **Edit button** | Modify existing metadata (after initial configuration) |
### Metadata fields
| Field | Required | Description |
| --------------- | -------- | ---------------------------------------------------- |
| **Unique Name** | Yes | Identifier for the trained model |
| **Description** | No | Purpose or use case for the model |
| **Tags** | No | Keywords for identification and categorization |
| **License** | No | License under which the model will be shared or used |
***
## Actions reference
### Configuration actions
| Action | Location | Description | Result |
| ------------------------ | ---------------------------- | ------------------------- | -------------------------------- |
| **Select base model** | Base Model module | Choose foundation model | Model selected for fine-tuning |
| **Select training data** | Training Data module | Choose prepared dataset | Dataset linked to training job |
| **Select compute** | Compute module | Choose compute resources | Instance allocated for training |
| **Add model details** | Trained Model Details module | Configure output metadata | Metadata saved for trained model |
### Training actions
| Action | Location | Description | Result |
| --------------- | ------------ | ----------------- | --------------------------------------------- |
| **Train Model** | Page toolbar | Initiate training | Training job starts and progress is displayed |
***
## Training output
Once training completes, the fine-tuned model is available with:
* The configured metadata (name, description, tags, license)
* Full compatibility with the training dataset format
* Readiness for deployment or inference
***
## Workflow summary
1. **Select base model** → Choose the foundation model in the Base Model module
2. **Select training data** → Choose a prepared dataset in the Training Data module
3. **Configure compute** → Select appropriate compute resources
4. **Add metadata** → Provide model name, description, tags, and license
5. **Train model** → Click Train Model and monitor progress
***
## Related content
Prepare datasets for fine-tuning with conversation formatting
Run inference using trained models within your data plane
Reference for available AI models
Understanding datasets in Narrative