Tokenization in LLMs – BPE and WordPiece (part-1)

     Tokenization plays a key role in large language models—it turns raw text into a format that the models can actually understand and work with. 

    When building RAG (Retrieval-Augmented Generation) systems or fine-tuning large language models, it is important to understand tokenization techniques. Input data must be tokenized before being fed into the model. Since tokenization can vary between models, it’s essential to use the same tokenization method that was used during the model’s original training.

    In this tutorial, we'll go through the tokenization and its practical applications in LLM tasks. The tutorial will cover:

1. Introduction to Tokenization
2. Tokenization in LLMs
3. Byte Pair Encoding (BPE)
4. WordPiece
5. Key Differences Between BPE and WordPiece  
6. Conclusion

     Let's get started.

Building RAG-Based QA System with LlamaIndex

           In this tutorial, we will implement a RAG (Retrieval-Augmented Generation) chatbot using LlamaIndex, Hugging Face Transformer, and Flan-T4 model. We use a sample industrial equipment documentation as our knowledge base and allow an LLM (Flan-T5) to generate responses using retrieved external data. We also add relevance filtering for accuracy control. The tutorial covers:

1. Introduction to RAG
2. Why LlamaIndex?
   
3. Setup and custom data preparation
4. Creating a vector store index
5. Load a pre-trained LLM (Flan-T5)
6. Retrieval with relevance check
7. Enhanced QA method
8. Execution
   
9. Conclusion
10. Full code listing
    

Implementing Retrieval-Augmented Generation (RAG) for Custom Data Q&A

          In this tutorial, we will implement a Retrieval-Augmented Generation (RAG) system in Python using LangChain, Hugging Face Transformers, and FAISS. We will use custom equipment specifications as our knowledge base and allow an LLM (Flan-T5) to generate responses using retrieved external data. The tutorial covers:

1. Introduction to RAG
   
2. Setup and custom data preparation
3. Creating a vector store (FAISS)
   
4. Load a pre-trained LLM (Flan-T5)
5. Building the RAG system
6. Execution
   
7. Conclusion
8. Full code listing
   

Fine-Tuning a Large Language Model (LLM) for Text Classification

         In this tutorial, we will learn how to fine-tune a pre-trained large language model (LLM) for a text classification task using the Hugging Face transformers library. We will use the DistilBERT model, a smaller and faster version of BERT, and fine-tune it on the IMDb movie review dataset for sentiment analysis (positive or negative). The tutorial covers:

1. Introduction to fine-turing LLMs
   
2. Loading and preparing a dataset
3. Data tokenization
4. Fine-tuning the model
5. Prediction and model evaluation
6. Execution
   
7. Conclusion
8. Full code listing
   

PCA-Based Anomaly Detection in Python

     Anomaly detection is a technique used to identify unusual patterns that do not conform to expected behavior. Principal Component Analysis (PCA) is a dimensionality reduction technique that can be used for anomaly detection by projecting data into a lower-dimensional space and identifying anomalies as points that deviate significantly from the projected data. 

    In this tutorial, we will learn how to perform PCA-based anomaly detection using Python. We will generate synthetic 3D data, apply PCA, and detect anomalies based on the reconstruction error. Finally, we will evaluate the performance using a confusion matrix and classification report and visualize the results in a 3D plot.

    The tutorial covers:

1. Introduction to PCA and Anomaly detection
2. Generating test data
3. Applying PCA
   
4. Detecting anomalies
   
5. Conclusion
   
6. Source code listing 
   

Graph-based Anomaly Detection Example

    Graph-based anomaly detection identifies unusual data points by analyzing graph structures, where nodes represent data points and edges depict relationships, such as distances or similarities. In this tutorial, I explain how to detect anomalies using a graph-based method in Python. The tutorial covers:

1. Introduction to Graph method
2. Generating test data
3. Graph construction
   
4. Anomaly detection
5. Conclusion
   
6. Source code listing