To learn about Large Language Models (LLMs) for AI/ML ( AI generated)

To learn about Large Language Models (LLMs) for AI/ML, follow these steps:

1. Prerequisites

Before diving into LLMs, you should have a strong foundation in:

  • Python (libraries like NumPy, Pandas, Matplotlib)
  • Machine Learning (scikit-learn, linear algebra, optimization)
  • Deep Learning (PyTorch or TensorFlow)
  • Natural Language Processing (NLP) (Tokenization, embeddings, transformers)

2. Understanding LLM Fundamentals

  • Learn about transformers, the core architecture behind LLMs (e.g., BERT, GPT).
  • Study self-attention, positional encoding, and attention mechanisms.
  • Read the original "Attention Is All You Need" paper by Vaswani et al. (2017).

3. Hands-on with LLMs

  • Use Hugging Face Transformers to load and fine-tune pre-trained models: 
    from transformers import pipeline
generator = pipeline("text-generation", model="gpt2")
print(generator("Once upon a time", max_length=50))
  • Train or fine-tune models using Google Colab, PyTorch, or TensorFlow.
  • Experiment with LLM APIs (OpenAI, Cohere, Mistral, Hugging Face Inference).

4. Deploy and Optimize LLMs

  • Learn about model quantization (bitsandbytes), distillation, and LoRA (Low-Rank Adaptation).
  • Deploy LLMs using FastAPI, Flask, or LangChain.
  • Explore vector databases (FAISS, Pinecone, ChromaDB) for RAG (Retrieval-Augmented Generation).

5. Advanced Topics

  • Study Mixture of Experts (MoE), multimodal models, reinforcement learning from human feedback (RLHF).
  • Explore open-source LLMs like LLaMA, Mistral, and Falcon.
  • Read research papers from ArXiv and follow AI communities on Twitter, Hugging Face, and GitHub.

Would you like a structured roadmap with projects?

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