Rapid Application Development Using Large Language Models (RADLLM)
- Course Code GK847004
- Duration 1 day
Course Delivery
Course Delivery
This course is available in the following formats:
-
Company Event
Event at company
Request this course in a different delivery format.
Course Overview
TopGain a strong understanding and practical knowledge of LLM application development by exploring the open-sourced ecosystem including pretrained LLMs.
Recent advancements in both the techniques and accessibility of large language models (LLMs) have opened up unprecedented opportunities to help businesses streamline their operations, decrease expenses, and increase productivity at scale. Additionally, enterprises can use LLM-powered apps to provide innovative and improved services to clients or strengthen customer relationships. For example, enterprises could provide customer support via AI companions or use sentiment analysis apps to extract valuable customer insights
Company Events
These events can be delivered exclusively for your company at our locations or yours, specifically for your delegates and your needs. The Company Events can be tailored or standard course deliveries.
Course Schedule
TopCourse Objectives
TopIn this course, you will learn about:
- Find, pull in, and experiment with the HuggingFace model repository and Transformers API.
- Use encoder models for tasks like semantic analysis, embedding, question-answering, and zero-shot classification.
- Work with conditioned decoder-style models to take in and generate interesting data formats, styles, and modalities.
- Kickstart and guide generative AI solutions for safe, effective, and scalable natural data tasks.
- Explore the use of LangChain and LangGraph for orchestrating data pipelines and environment-enabled agents.
Course Content
TopModule 1: Early and Late Fusion
- Use camera and LiDAR data to predict object positions.
- Convert various datatypes to make them neural network ready.
Module 2: Intermediate Fusion
- Explore the theory behind effective multimodal model architecture.
- Train a Contrastive Pretraining model.
- Create a vector database.
Module 3: Cross-modal Projection
- Converting a Language model into a Vision Language Model (VLM).
- Process PDFs with Optical Character Recognition (OCR) tools.
Module 4: Model Orchestration
- Analyze video using Cosmos Nemotron.
- Use VSS to answer user queries about video content.
- Orchestrate with NVIDIA AI Blueprints.
Module 5: Assessment
- Convert a pre-trained model to input a different datatype using projection.
