Large Language Models - Labellerr

Large Language Models

A collection of 14 posts

Google Gemini: What All The Hype Is About?

Table of Contents 1. Introduction 2. Advanced Performance and Technical Prowess 3. Specifications, dimensions, and current market availability of Gemini 4. Practical Applications and Use Cases 5. Interacting with Gemini through multi-modal prompting 6. Ethical Integration and Safety Assurance 7. The Age of Gemini: Empowering Future Innovation 8. Conclusion 9.

InstructGPT by OpenAI

Unveiling InstructGPT: A Powerful Language Model by OpenAI

In the fast-paced world of natural language processing, InstructGPT has emerged as a game-changing innovation. Developed by OpenAI, this advanced language model has a unique capability: understanding and executing specific instructions with remarkable precision. In this comprehensive blog, we'll delve into the intricacies of InstructGPT, explore its practical applications, and

How to choose LLM to suit for use case

How To Chose Perfect LLM For The Problem Statement Before Finetuning

Large Language Models (LLMs) are AI-enabled tools that excel at producing text responses that are human-like when given particular prompts, and they are radically changing how people interact with computers. One of the most well-known LLMs is ChatGPT, which shot to fame in the latter half of 2022. Large Language

What different methods to employ for Data Labelling in Machine Learning Tasks

Machine Learning

Different Methods To Employ Data Labelling In Machine Learning Tasks

Often, data labeling goes hand in hand with data acquisition. When compiling information from the Web to create a knowledge base, each fact is considered accurate and is therefore implicitly labeled as true. In the context of data labeling literature, it's useful to separate it from data acquisition due to

Hands-on with Fine-Tuning LLM

fine-tuning of large language models

Fine-Tuning Tutorial: Falcon-7b LLM To A General Purpose Chatbot

LLM models undergo training on extensive text data sets, equipping them to grasp human language in depth and context. In the past, most models underwent training using the supervised method, where input features and corresponding labels were fed. In contrast, LLMs take a different route by undergoing unsupervised learning. In

Comprehensive Guide for Fine Tuning of LLMs

Large Language Models

Everything You Need To Know About Fine Tuning of LLMs

Pre-trained large language models (LLMs) come with impressive capabilities, such as text generation, summarization, and coding. Nevertheless, these LLMs are only universally suitable solutions for some scenarios. Depending on your specific application, you may encounter tasks the language model cannot handle successfully. In such situations, one viable approach is to

Challenges faced during the Development and usage of LLMs

Large Language Models

8 Challenges Of Building Own Large Language Model

Table of Contents 1. Benefits of Large Language Models 2. Utilizing Large Language Models 3. Why Build Your Own LLM? 4. Understanding the Challenges with LLMs 5. Indistinguishability Between Generated and Human-Written Text 6. Conclusion Large Language Models (LLMs) are advanced artificial intelligence systems that have been trained on massive

Training Small-Scale Vs Large-Scale Language Models: The Difference

Large Language Models

Training Small-Scale Vs Large-Scale Language Models: The Difference

Language model development has significantly evolved, leading to the emergence of small-scale and large-scale models. These models have revolutionized various natural language processing related tasks and profoundly impacted the artificial intelligence field. Understanding the differences between small-scale and large-scale language models is crucial for grasping their capabilities and implications. Figure:

Data Collection and Preprocessing for Large Language Models

Data Collection and Preprocessing for Large Language Models

LLMs, or Large Language Models, are advanced AI models that undergo extensive training using massive amounts of text data. Through this training, they learn language structure and patterns and can perform various language-related tasks such as summarization, translation, sentiment analysis, and more. Due to their remarkable ability to perform natural

Exploring Architectures and Configurations for Large Language Models (LLMs)

Language Models

Exploring Architectures and Configurations for Large Language Models (LLMs)

Table of Contents 1. Introduction 2. General Architecture 3. Activation Functions 4. Conclusion Introduction Language models have become increasingly successful in recent years, especially large language models (LLMs) like GPT-4. These models have shown remarkable abilities in various natural language processing (NLP) tasks, such as text generation, language translation, question-answering,

DragGAN: An AI Magic Tool For Editing Images

Language Models

DragGAN: An AI Magic Tool For Editing Images

Welcome to the enchanted world of DragGAN, where magic and artificial intelligence combine to create masterpieces from seemingly ordinary photographs. This blog explores the fascinating powers of DragGAN, an AI-driven wizard with the dexterous ability to magically give your images a life of their own. Join us on this enthralling

RoBERTa: A Robustly Optimized BERT Pretraining Approach

Language Models

RoBERTa: A Robustly Optimized BERT Pretraining Approach

Keeping up with the latest developments in the rapidly expanding field of natural language processing is challenging. But fear not—a strong ally is on hand to completely alter the way we think about language comprehension. Welcome to RoBERTa, the robustly optimized BERT pretraining approach set to revolutionize NLP. Join

Secret To Enhance Performance and Task Generalization in LLMs

Large Language Models

How To Enhance Performance and Task Generalization in LLMs

From the previous blog, we studied that Pre-training forms the foundation of LLMs' abilities. LLMs acquire crucial language comprehension and generation skills through pre-training on extensive corpora. The size and quality of the pre-training corpus play a vital role in enabling LLMs to possess powerful capabilities. Moreover, the design of

Efficient Tuning LLMS: Optimize Performance with Fewer Parameters

Efficient Tuning LLMS: Optimize Performance with Fewer Parameters

Table of Contents 1. Introduction 2. Parameter-Efficient Fine-Tuning Methods 3. Adapter Tuning 4. Prefix Tuning 5. Prompt Tuning 6. Low-Rank Adaptation (LoRA) 7. Conclusion Introduction Tuning plays a crucial role in the development and effectiveness of Language Models (LLMs). LLMs are pre-trained on vast amounts of text data to acquire