LLM Studio Interface

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 to transform datasets into the fine_tuning_conversation format.

Accessing training data with NQL

To query conversation data from a prepared dataset:

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

Select base model → Choose the foundation model in the Base Model module
Select training data → Choose a prepared dataset in the Training Data module
Configure compute → Select appropriate compute resources
Add metadata → Provide model name, description, tags, and license
Train model → Click Train Model and monitor progress

Prompt Studio

Prepare datasets for fine-tuning with conversation formatting

Model Inference

Run inference using trained models within your data plane

Supported Models

Reference for available AI models

Datasets

Understanding datasets in Narrative

Overview

Connectors

Integrations

Workflow Reference

Webhook Reference

Security Reference

Architecture Reference

Error Reference

Model Inference Reference

Rosetta Stone Reference

UI Reference

SDKs

Glossary

Billing

Overview

Base Model module

Available base models

Training Data module

Dataset requirements

Accessing training data with NQL

Compute module

Compute instance selection

Trained Model Details module

Metadata fields

Actions reference

Configuration actions

Training actions

Training output

Workflow summary

Prompt Studio

Model Inference

Supported Models

Datasets

​Overview

​Base Model module

​Available base models

​Training Data module

​Dataset requirements

​Accessing training data with NQL

​Compute module

​Compute instance selection

​Trained Model Details module

​Metadata fields

​Actions reference

​Configuration actions

​Training actions

​Training output

​Workflow summary

​Related content

Prompt Studio

Model Inference

Supported Models

Datasets

Overview

Base Model module

Available base models

Training Data module

Dataset requirements

Accessing training data with NQL

Compute module

Compute instance selection

Trained Model Details module

Metadata fields

Actions reference

Configuration actions

Training actions

Training output

Workflow summary

Related content