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 StudioBase 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)
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 moduleSelect training data → Choose a prepared dataset in the Training Data moduleConfigure compute → Select appropriate compute resourcesAdd metadata → Provide model name, description, tags, and licenseTrain model → Click Train Model and monitor progress
Related content
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
