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

LLM Dataset Generator

The LLM Dataset Generator is the first step to creating a custom LLM. Whether you’re creating a local expert or a custom model to use with the Document QnA element

Before you can generate your dataset, make sure you have your documents you want to use ready. Gather all relevant documents you want your model to be built off of. These documents must be in one folder and in the following formats - PDFs, text, and docx.

  1. Start by creating a new Canvas. Then open the Elements Drawer and drag the LLM Dataset Generator element onto the Canvas.
  2. Open the LLM Dataset Generator Element settings and adjust the following settings:
  • Topic: This can be anything you would like.
  • References folder path: Using the “Select Directory” button, choose the folder where your documents are located.
  • Output folder path: Using the “Select Directory” button, choose the folder where you would like to save the output of the dataset generation
  • Dataset size: Add the number of topics you want your dataset to train with.

    Note:     We recommend starting with 5 for testing and getting familiar with the process of dataset generation. This generates a list of five topics and is quicker for training, but it will not produce as accurate of a model as a larger dataset size. The higher the dataset size, the more accurate your dataset and trained model will be. However, the larger the dataset size, the longer it will take to generate your dataset. It can take several hours to generate large dataset, so be patient. Next, enter your Groq, GPT, Claude, or Gemini API keys. You can add as many as you like, but 1 is required. You can get a free Groq key here.
  • Now you can now hit run. Dependencies will be installed the first time this flow is run, so it may take a while for them to install.
  • The output will be a folder with the name dataset_[your_topic_name]_[timestamp]‍
  • This folder is what you connect to the Dataset Folder Path in the LLM Trainer Element in the Training step.