Understanding RAG Part IX: Fine-Tuning LLMs for RAG
Be sure to check out the previous articles in this series: •
How to Perform Scikit-learn Hyperparameter Optimization with Optuna
Optuna is a machine learning framework specifically designed for automating hyperparameter optimization , that is, finding an externally fixed setting of machine learning model hyperparameters that optimizes the model’s performance.
5 Reasons Why Traditional Machine Learning is Alive and Well in the Age of LLMs
Nowadays, everyone across AI and related communities talks about generative AI models, particularly the large language models (LLMs) behind widespread applications like ChatGPT, as if they have completely taken over the field of machine learning.
Repurposing Protein Folding Models for Generation with Latent Diffusion
PLAID is a multimodal generative model that simultaneously generates protein 1D sequence and 3D structure, by learning the latent space of protein folding models. The awarding of the 2024 Nobel Prize to AlphaFold2 marks an important moment of recognition for the of AI role in biology. What comes next after protein folding? In PLAID, we …
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Example Applications of Text Embedding
This post is divided into five parts; they are: • Recommendation Systems • Cross-Lingual Applications • Text Classification • Zero-Shot Classification • Visualizing Text Embeddings A simple recommendation system can be created by finding a few of the most similar items to the target item.
Using Auto Classes in the Transformers Library
This post is divided into three parts; they are: • What Is Auto Classes • How to Use Auto Classes • Limitations of the Auto Classes There is no class called “AutoClass” in the transformers library.
Text Embedding Generation with Transformers
This post is divided into three parts; they are: • Understanding Text Embeddings • Other Techniques to Generate Embedding • How to Get a High-Quality Text Embedding? Text embeddings are to use numerical vectors to represent text.
The Beginner’s Guide to Clustering with Python
Clustering is a widely applied method in many domains like customer and image segmentation, image recognition, bioinformatics, and anomaly detection, all to group data into clusters in terms of similarity.
The Roadmap for Mastering MLOps in 2025
Organizations increasingly adopt machine learning solutions into their daily operations and long-term strategies, and, as a result, the need for effective standards for deploying and maintaining machine learning systems has become critical.
Fine-Tuning DistilBERT for Question Answering
This post is divided into three parts; they are: • Fine-tuning DistilBERT for Custom Q&A • Dataset and Preprocessing • Running the Training The simplest way to use a model in the transformers library is to create a pipeline, which hides many details about how to interact with it.