Intro to LLM Agents with Langchain: When RAG is Not Enough
Hello everyone, this article is a written form of a tutorial I conducted two weeks ago with Neurons Lab. If you prefer a narrative walkthrough, you can find the YouTube video here: As always, you can find the code on GitHub, and here are separate Colab Notebooks: Planning and reasoningDifferent types of memoriesVarious types of toolsBuilding complete agentsIntroduction to the agents Illustration b
Fine-Tuning Large Language Models (LLMs)
This is the 5th article in a series on using Large Language Models (LLMs) in practice. In this post, we will discuss how to fine-tune (FT) a pre-trained LLM. We start by introducing key FT concepts and techniques, then finish with a concrete example of how to fine-tune a model (locally) using Python and Hugging Face’s software ecosystem. Tuning a language model. Image by author.In the previous art
Simple Implementation of OpenAI CLIP model: A Tutorial
IntroductionIt was in January of 2021 that OpenAI announced two new models: DALL-E and CLIP, both multi-modality models connecting texts and images in some way. In this article we are going to implement CLIP model from scratch in PyTorch. OpenAI has open-sourced some of the code relating to CLIP model but I found it intimidating and it was far from something short and simple. I also came across a
The 7 Steps of Machine Learning
From detecting skin cancer, to sorting cucumbers, to detecting escalators in need of repairs, machine learning has granted computer systems entirely new abilities. But how does it really work under the hood? Let’s walk through a basic example, and use it as an excuse talk about the process of getting answers from your data using machine learning. Let’s pretend that we’ve been asked to create a sys
Beam Search Decoding in CTC-trained Neural Networks
Neural networks (NN) consisting of convolutional NN layers and recurrent NN layers combined with a final connectionist temporal classification (CTC) layer are a good choice for (handwritten) text recognition. The output of the NN is a matrix containing character-probabilities for each time-step (horizontal position), an example is shown in Fig 1. This matrix must be decoded to get the final text.
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