DistilBERT was introduced as a smaller, faster, and distilled version of BERT. It maintains 97% of BERT's language understanding capabilities while being 40% small and 60% faster. 

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 Haziqa Sajid, Freelance Technical Writer

 Haziqa Sajid

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DistilBERT maintains 97% of BERT's language understanding capabilities while being 40% small and 60% faster.


Distilbert: A Smaller, Faster, and Distilled BERT  

DistilBERT: A Distilled Version of BERT

Building state-of-the-art embedding models for high-quality RAG systems needs careful attention to pretraining, fine-tuning, and scalability. Zilliz Cloud and Milvus help manage embeddings at scale and create more intelligent and responsive neural search systems.



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Building state-of-the-art embedding models for high-quality RAG systems needs careful attention to pretraining, fine-tuning, and scalability. Zilliz Cloud and Milvus help manage embeddings at scale and create more intelligent and responsive neural search systems.


Harnessing Embedding Models for AI-Powered Search

A knowledge graph is a data structure representing information as a network of entities and their relationships.

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 What is a Knowledge Graph? 

What is a Knowledge Graph (KG)? 

RoBERTa (A Robustly Optimized BERT Pretraining Approach) is an improved version of BERT designed to address its limitations. 


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RoBERTa (A Robustly Optimized BERT Pretraining Approach) is an improved version of BERT designed to address its limitations. 

What is RoBERTa? 

RoBERTa: An Optimized Method for Pretraining Self-supervised NLP Systems

Despite advances in LLMs like ChatGPT, search still matters. Combining GenAI with search and vector databases enhances search accuracy and experience.

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Search and Information Retrieval in the Era of Generative AI 

Search Still Matters: Enhancing Information Retrieval with Generative AI and Vector Databases

Information retrieval (IR) is the process of efficiently retrieving relevant information from large collections of unstructured or semi-structured data. 

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What is Information Retrieval? A Comprehensive Guide. 

What is Information Retrieval?

Vision Transformers (ViTs) are neural network models that use transformers to perform computer vision tasks like object detection and image classification. 

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What are Vision Transformers (ViT)?

A Beginner's Guide to Understanding Vision Transformers (ViT)

HyDE (Hypothetical Document Embeddings) is a retrieval method that uses "fake" documents to improve the answers of LLM and RAG. 

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Better RAG with HyDE - Hypothetical Document Embeddings

Improving Information Retrieval and RAG with Hypothetical Document Embeddings (HyDE)

Knowledge Graphs (KGs) store and link data based on their relationships. KG-enhanced RAG can significantly improve retrieval capabilities and answer quality. 

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Enhancing RAG with Knowledge Graphs

Enhancing Your RAG with Knowledge Graphs Using KnowHow

Using LLMware and the Milvus vector database, we can combine the power of vector similarity search and LLMs to ask questions on our private documents.

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Safeguarding Data Integrity: On-Prem RAG Deployment with LLMware and Milvus 

This article discussed the popular voyage AI embedding models and rerankers and their integration with Zilliz Cloud. 

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Voyage AI Embeddings and Rerankers for Search and RAG 

Image Embeddings are the core of modern computer vision algorithms. Understand their implementation and use cases and explore different image embedding models.

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Image Embeddings for Enhanced Image Search

Image Embeddings for Enhanced Image Search: An In-depth Explainer

Integrating the Milvus vector database and MemGPT has taken one step further in streamlining the development of AI Agents with connections to external data sources. In this post, we share an example demonstrating how to use this integration to build a chatbot with external memories. 


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Introduction to MemGPT and Its Integration with Milvus

In this blog post, we will understand the concept of vector embeddings and explore its applications, best practices, and tools for working with embeddings.


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An Introduction to Vector Embeddings: What They Are and How to Use Them

Understand how vector databases process data to enhance customer experience and drive business growth. 

Exploring VectorDB Use Cases

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Enhancing Customer Experience with Vector Databases: A Strategic Approach

Learn the mechanics, applications, and benefits of lexical and semantic search and how to perform it in Zilliz.

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Navigating the Nuances of Lexical and Semantic Search with Zilliz

Explore different ranking algorithms, their usage, and best practices. Uncover the real-world success stories of ranking algorithms and their evolution over time.


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Ranking Models: What Are They and When to Use Them?  

Mastering color histograms is indispensable for anyone involved in image processing and analysis. By understanding the nuances of color distributions and leveraging advanced techniques, practitioners can unlock the full potential of color histograms in various imaging projects and research endeavors.

Vector Database 101

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 Demystifying Color Histograms: A Guide to Image Processing and Analysis

This article will explore CLIP's inner workings and pioneering potential in multimodal learning.


LLM 101

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Multimodal AI learning can get input and understand information from various modalities like text, images, and audio together, leading to a deeper understanding of the world. Learn more about OpenAI's CLIP (Contrastive Language-Image Pre-training), a popular multimodal model for text and image data.

Exploring OpenAI CLIP: The Future of Multi-Modal AI Learning

This article will discuss the Softmax Activation Function, its applications, challenges, and tips for better performance.


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The softmax function is a mathematical function used in machine learning, particularly in the context of classification tasks. 

Understanding Softmax Activation Function

Decoding Softmax Activation Function

Understand Locality Sensitive Hashing as an effective similarity search technique. Learn practical applications, challenges, and Python implementation of LSH.

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Mastering Locality Sensitive Hashing: A Comprehensive Tutorial and Use Cases

Understand Open AI Whisper and follow this step-by-step article to implement it in projects that can significantly enhance the efficiency of speech-to-text tasks.

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OpenAI Whisper: Transforming Speech-to-Text with Advanced AI

BERTopic is a novel topic modeling technique that allows for easily interpretable topics while keeping important words in the topic descriptions. 

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BERTopic: An Advanced Neural Topic Modeling Technique

Exploring BERTopic: An Advanced Neural Topic Modeling Technique

SPLADE is a technique that uses pre-trained transformer models to process sparse data. This post explores SPLADE, its benefits, and real-world apps. 

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Discover SPLADE: Revolutionizing Sparse Data Processing

Cross-entropy loss is used for training classification models. It’s an easy-to-implement loss function requiring labels encoded in numeric values for accurate loss calculation. 

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Cross Entropy loss is used for training classification models.

Cross-Entropy Loss: Unraveling its Role in Machine Learning

This article explores the memory capabilities of modern LLMs, using LangChain modules to establish memory buffers and build conversational AI applications. 

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LangChain Memory: Enhancing AI Conversational Capabilities