AI Terminology
Navigator Terminology
Use Cases & AI Architectures
Element Library
How to Deploy a Solution
How to create a local expert LLM
LLM Dataset Generator
LLM Trainer
LLM Chat
Document QnA
How to create a computer vision model
Building a Dataset: Image Classifier
Building a Dataset: Object Detector
Prototyping & Hardening for Production
How to implement your AI model
Templates: Document QnA
Templates: LLM Dataset Generation
Templates: Train an LLM
Templates: Custom LLM Chatbot
Templates: Local Expert
Supported LLM Base Models

Table of Contents

Templates: Train an LLM

LLM Model Training

Now that you have generated your LLM Dataset, you can train your custom LLM Model.

  • Open the LLM Trainer Element settings and make the following adjustments
  • Base Model Architecture: The default model is great for creating a model quickly. Want to train something tailored to a specific idea? Check out our Supported LLM Base Models page to learn more about all the models in Navigator
  • Dataset Folder Path: Using the “Select Directory” button, choose the folder where you saved your LLM dataset during LLM Dataset Generation.
  • Artifact Save Path: Using the “Select Directory” button, choose the folder where you would like to save your trained adapter.
  • Base Model Assets Path: Using the “Select Directory” button, choose the folder where you would like to save your base model.
  • Evaluator API Key: Add a Groq, OpenAI, Claude, or Gemini API key to enable the Faithfulness and Relevancy benchmarks in your training metrics. If you need a free API key, you can generate one for Groq here.
  • Batch Size: 4 is recommended for testing
  • Leave all other settings as the default.
  • You can now hit run. This process may take a while, so be patient.