LLMs, Embeddings, Vector Search and More!

As we stand on the brink of AI-driven transformation, it's fascinating to look back at how far we've come. The image showcases the rapid evolution of language models, from BERT to the latest Gemini, highlighting their release dates and the staggering number of parameters they're trained on.

These models, developed by pioneers like Google, OpenAI, and others, are not just a testament to technological advancement but a beacon of the potential AI holds. Each model, with its billions of parameters, represents a leap forward in understanding and generating human-like content. But with great power comes great responsibility. As we celebrate these milestones, it is also highly recommended to prioritise the ethical considerations while using these LLMs.

The Evolution of GPT Models

Let's understand the evolution of GPT models.

Here's a brief overview of the evolution from GPT-1 to GPT-4.

? GPT-1 [Release: June 2018]:

Developed by OpenAI, GPT-1 was the first in the series, introducing the transformer-based architecture for natural language understanding. It had 117 million parameters and was trained on the BooksCorpus dataset. GPT-1 showcased the potential of transformers in generating coherent and contextually relevant text over several sentences.

Key Features: Demonstrated the effectiveness of unsupervised pre-training and fine-tuning for a wide range of tasks without task-specific architecture modifications.

? GPT-2 [Release: February 2019]:

GPT-2 marked a significant leap in the scale and capabilities of language models. It was much larger, with 1.5 billion parameters, and was trained on a dataset called WebText, containing a diverse range of internet text.

Key Features: Demonstrated strong performance on many NLP tasks without task-specific training. Its release was staggered due to concerns about potential misuse.

? GPT-3 [Release: June 2020]:

GPT-3 was a monumental step in language model capabilities, scaling up to an unprecedented 175 billion parameters. It showed remarkable performance across a wide range of tasks, often requiring very few examples to adapt to new types of language tasks (few-shot learning).

Key Features: Its ability to perform tasks with little to no task-specific data (zero-shot or few-shot learning) set it apart. GPT-3 could generate creative content, solve problems, and even emulate conversation to a high degree of sophistication.

? GPT-3.5 [Release: 2022]:

GPT-3.5 was an iterative improvement over GPT-3, addressing some of its weaknesses and improving its understanding and generation capabilities. While the parameter count remained similar to GPT-3, it included improvements in training techniques and fine-tuning, leading to better performance in certain areas.

Key Features: Enhanced performance in understanding context, reduced harmful outputs, and better handling of nuanced instructions. It served as a bridge in performance and capabilities leading up to GPT-4.

? GPT-4 [Release: 2023]:

GPT-4 represents the latest and most advanced iteration in the GPT series. While specific details about its architecture and training might not be fully disclosed, it is expected to have more parameters and be trained on even more extensive and diverse datasets. GPT-4 aims to push the boundaries of what's possible in natural language understanding and generation.

Key Features: Even better understanding of context, more nuanced and sophisticated generation, and further capabilities in reasoning and problem-solving. It also focuses on reducing biases and errors, making it more reliable and safe for various applications.

Know more: https://lnkd.in/gSh85h23

RAG with LangChain

LangChain uses RAG to make its language models smarter. It's like the chef using that secret ingredient to make the dish more flavorful. The diagram illustrates the integration of LangChain with Retrieval-Augmented Generation (RAG) to enhance language models. LangChain is a framework designed for building and operating language models. It integrates with RAG, a method that augments language models by retrieving relevant information from a data or document base. This integration allows LangChain to leverage RAG's capability of enhancing language models by providing them with access to a vast repository of information.

Try this LangChain RAG hands-on tutorial : https://lnkd.in/d3zfqXg9

But, When Not to Use RAG?

RAG is a technique that combines the LLM with external knowledge bases. This allows the model to add relevant information or specific data not included in the original training set to the model.

1. You convert documents into numbers, we call them?embeddings.

2. Then, you also convert the user’s search query into embeddings using the same model.

3. Find the top K closest documents, usually based on?cosine similarity.

4. Ask the LLM to generate a response based on these documents.

When to Use RAG?

→ Need for Current Information:?When the application requires information that is constantly updating, like news articles.

→ Domain-Specific Applications:?For applications that require specialized knowledge outside the LLM’s training data. For example, internal company documents.

When NOT to Use RAG?

→ General Conversational Applications:?Where the information needs to be general and doesn’t require additional data.

→ Limited Resource Scenarios:?The retrieval component of RAG involves searching through large knowledge bases, which can be computationally expensive and slow — though still faster and less expensive than fine-tuning.

Here is a simple RAG tutorial: https://lnkd.in/g9bjmDGu

Know more in this article: https://lnkd.in/g2gns3Bt

My in-depth article on RAG is here: https://lnkd.in/g7XUj-DD

Running LLMs Locally

8 tools every AI/ML/Data enthusiast must know to run LLMs locally. But the question is, do you really need to run LLMs locally?

If you prioritize privacy, cost-effectiveness, or customization, and have the hardware and technical expertise, running an LLM locally might be a good choice. However, if you need quick access to a wider range of models or lack the technical know-how, cloud-based options might be more suitable.

Here are 7 tools you should know to run LLMs locally.

1. GPT4All: https://lnkd.in/gTAWafB4

2. LLM: https://lnkd.in/g4V6GpmV

3. Ollama: https://ollama.ai/

4. h2oGPT: https://h2o.ai/

5. privateGPT: https://lnkd.in/gAvZmdtV

6. llamafile: https://lnkd.in/gyVb8GGQ

7. localGPT: https://lnkd.in/gPH4HVce

8. ML Studio: https://lmstudio.ai/

Evaluating Large Language Models (LLMs)

How to effectively evaluate LLM-based applications?

LLMs are primarily evaluated on open task-specific datasets to analyze their capabilities in doing a variety of tasks like summarisation, open book question answering, etc.

There are several public benchmarks available. The metrics used in benchmarking LLMs vary with tasks. Evaluation metrics for LLM can be broadly classified into traditional and nontraditional metrics.

Traditional evaluation metrics rely on the arrangement and order of words and phrases in the text and are used in combination where a reference text (ground truth) exists to compare the predictions against.

Nontraditional metrics make use of semantic structure and capabilities of language models for evaluating generated text. These techniques can be used with and without a reference text.Evaluating LLM-based applications before productizing should be a key part of LLM workflow.

This will act as a quality check and help you improve the performance of your pipeline over time. Know more in the original article: https://lnkd.in/giDwwbFk

A Beginner's Guide to Vector Databases

Did you get a chance to attend my webinar on beginner's guide to vector databases? If not, here is the complete on-demand recording available.

SingleStore's Latest New Features for Vector Search

SingleStore is thrilled to announce the arrival of SingleStore 8.5! One of the highlights of the release includes 'Vector Search Enhancements'.?

Two important new features have been added to improve vector data processing and the performance of vector search.

1. Indexed approximate-nearest-neighbor (ANN) search?
2. A VECTOR data type

Indexed ANN vector search facilitates creation of large-scale semantic search and generative AI applications. Supported index types include inverted file (IVF), hierarchical navigable small world (HNSW) and variants of both based on product quantization (PQ) – a vector compression method. The VECTOR type makes it easier to create, test, and debug vector-based applications. New infix operators are available for DOT_PRODUCT (<*>) and EUCLIDEAN_DISTANCE (<->) to help shorten queries and make them more readable.

Sign up to SingleStore & Enjoy the New Features!