Artificial Intelligence Theory and Application

  Understanding Runnables and LCEL in LangChain: Building Modular AI Workflows LangChain is a powerful framework for building applications powered by large language models (LLMs). It provides tools to create modular, reusable, and scalable AI workflows. Two key concepts in LangChain are  Runnables  and  LangChain Expression Language (LCEL) , which enable developers to build complex chains of operations with ease. In this blog post, we’ll explore what Runnables and LCEL are, how they work, and how you can use them to create sophisticated AI pipelines. 1. What are Runnables? Definition In LangChain, a  Runnable  is a basic building block that represents a unit of work or operation. It can be a model, a function, or a chain of operations. Runnables are designed to be modular and composable, allowing you to combine them into more complex workflows. Types of Runnables Models : Pre-trained LLMs like GPT, BERT, or custom models. Functions : Custom Python functions...

  Hugging Face vs. Ollama: A Comparison of AI Platforms In the rapidly evolving world of AI and machine learning, platforms like  Hugging Face  and  Ollama  have emerged as powerful tools for developers, researchers, and businesses. While both platforms aim to simplify AI development, they cater to different needs and use cases. In this blog post, we’ll compare  Hugging Face  and  Ollama , highlighting their features, strengths, and differences to help you choose the right platform for your AI projects. 1. Hugging Face What is Hugging Face? Hugging Face is a leading AI platform focused on  Natural Language Processing (NLP)  and  machine learning . It is best known for its open-source libraries like  Transformers ,  Datasets , and  Diffusers , which provide tools for building, training, and deploying state-of-the-art AI models. Key Features: Transformers Library : Offers pre-trained models like BERT, GPT, T5, and more ...

  Understanding RLHF, PEFT, and Continual Pretraining: Advanced AI Fine-Tuning Techniques As AI models grow larger and more complex, fine-tuning them for specific tasks has become a critical area of research. Techniques like  Reinforcement Learning with Human Feedback (RLHF) ,  Parameter-Efficient Fine-Tuning (PEFT) , and  Continual Pretraining  have emerged as powerful methods to adapt pre-trained models to new tasks and domains. In this blog post, we’ll explore these techniques in detail, explaining what they are, how they work, and their applications. 1. Reinforcement Learning with Human Feedback (RLHF) What is RLHF? Reinforcement Learning with Human Feedback (RLHF) is a technique used to fine-tune AI models by incorporating human feedback into the training process. It combines  reinforcement learning  (RL) with  human preferences  to align models with desired behaviors or outputs. How RLHF Works: Pretraining : A base model (e.g., GPT) is ...