Unleashing LLMs: Training, Fine-Tuning and Evaluating

10 August 2024 | 09:30AM - 05:30PM | location RENAISSANCE :- Race Course Rd, Madhava Nagar Extension

About the workshop

This workshop is designed to provide a comprehensive overview of LLMs, from foundational concepts to advanced applications. Whether you're a beginner or have intermediate experience, you will gain valuable insights and hands-on experience with some of the most cutting-edge technologies in the field.

  • Key Takeaways:
    • Understand the fundamentals of Language Models and Transformer architectures.
    • Gain hands-on experience with LLMs and related concepts such as PEFT, Prompt Engineering, RAGs, and more.
    • Explore advanced topics such as Reinforcement Learning from Human Feedback (RLHF) and Retrieval-Augmented Generation (RAG).

Modules

  • Overview of Generative AI and the basics of language modeling.
  • NLP Basics for Embedding and Attention: Fundamental concepts in NLP, focusing on embeddings and attention mechanisms.
  • Language Modeling: Basics of language modeling and its importance.
  • Hands-On: Implementing a simple language model using basic NLP techniques.

  • Transformer Architectures: Detailed look into the Transformer architecture that powers modern LLMs.
  • GPT Series of Models: Overview of the evolution of GPT models.
  • Hands-On: Training a mini Transformer model and experimenting with GPT-2 for text generation.

  • Training Process and Scaling Laws: Understand how LLMs are trained and the laws governing their scaling.
  • PEFT: Learn Parameter-Efficient Fine-Tuning methods.
    • LoRA: Introduction to Low-Rank Adaptation.
    • QLoRA: Exploring Quantized Low-Rank Adaptation.
  • Instruction Tuning: Techniques for fine-tuning models using instructions.
  • RLHF: Reinforcement Learning from Human Feedback and its applications.
  • Evaluation Metrics and Benchmarks: Methods to evaluate and benchmark LLM performance.
  • Beyond Prompting: Understanding Frameworks such as DSPY
  • Hands-On:
    • Fine-tuning a pre-trained model using different methods and evaluating it with standard benchmarks.
    • Hands-on with DSPY

  • OpenSource vs Commercial LLMs: Comparison between open-source and commercial LLM solutions.
  • Prompt Engineering: Crafting effective prompts to get desired outputs.
  • RAGs: Techniques for retrieval-augmented generation.
    • Vector Databases: Using vector databases for efficient data retrieval.
    • Chunking and Ingesting Documents: Methods for processing and ingesting documents.
  • Securing LLMs
    • Prompt Hacking and Backdoors
    • Defensive Measures
  • Hands-On:
    • Implementing basic prompt engineering techniques and
    • Building a simple RAG system.

  • Multimodal: Integration of different data modalities in LLMs.
  • Mixture of Experts: Using a mixture of expert models for improved performance.
  • SLM: Introduction to Small LMs.
  • Ethics and Bias in LLMs: Understanding and mitigating biases in LLMs.
  • Next Steps: Speculative topics on future advancements.
  • GPT5?: What to expect from the next generation of GPT.
  • Beyond: Future possibilities and directions for LLM research.
  • Hands-On: (If time permits) Experimenting with multi-modal models and mixture of experts.

  • Basic understanding of Python programming
  • Familiarity with fundamental machine learning concepts
  • Experience with common NLP tasks and techniques - e.g summarization, QA, classification
  • Comfortable running Jupyter Notebooks using Anaconda/VS Code or Google Colab.
  • We will provide ample GPU credits to ensure a seamless and productive workshop experience.
  • (Optional) Basic knowledge of deep learning frameworks (e.g., PyTorch, TensorFlow)
*Note: These are tentative details and are subject to change.

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