Large Language Models - Labellerr

Large Language Models

A collection of 16 posts

GPT-OSS Review: OpenAI's Free Model

GPT-OSS is an open-source framework for working with GPT-like models. It supports training, fine-tuning, deployment, and integration while ensuring transparency, community-driven development, and flexibility for research and production.

Baidu’s ERNIE 4.5 & X1: Are They China’s Answer to GPT-4?

Baidu’s Ernie 4.5 Outperforms GPT 4.5 By A Mile

Baidu’s ERNIE 4.5 & X1 signal China’s relentless AI expansion. Following DeepSeek R1 and Manus AI, these new models aim to challenge global AI leaders. Can they compete with OpenAI’s GPT-4 and beyond?

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 changing how people interact with technology, creating natural, human-like responses that streamline communication and productivity. The global AI market is projected to grow by 28.46% (2024-2030) resulting in a market volume of US$826.70bn in 2030. Therefore, choosing the right LLM for your

Data Collection and Preprocessing for Large Language Models

Data Collection and Preprocessing for Large Language Models

Are you struggling to harness the full potential of Large Language Models (LLMs) due to the complexities of data collection and preprocessing? You're not alone. Many developers and researchers face significant challenges in sourcing and preparing the vast amounts of text data necessary for training these advanced AI

Challenges in Developing Large Language Models (LLMs)

Large Language Models

8 Challenges Of Building Your Own Large Language Model

Explore the top LLM challenges in building your own large language model, from managing massive datasets to high fine-tuning costs and data privacy.

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

Large Language Models

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

Explore the contrasts between training small and large-scale language models, from data requirements and computational power to model complexity and performance nuances in NLP applications

Comprehensive Guide for Fine Tuning of LLMs

Large Language Models

Everything You Need To Know About Fine Tuning of LLMs

Fine-tuning LLMs customizes pre-trained models for specific tasks by adjusting parameters with new data, enhancing model precision and relevance across unique applications from legal analysis to healthcare.

silicon board shaped as human brain

Language Models

Exploring Architectures and Configurations for Large Language Models (LLMs)

Large Language Models (LLMs) like GPT-4 excel in NLP tasks through advanced architectures, including encoder-decoder, causal decoder, and prefix decoder. This article delves into their configurations, activation functions, and training stability for optimal performance.

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

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

Fine-Tuning Falcon-7b LLM for a General-Purpose Chatbot

fine-tuning of large language models

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

Learn to fine-tune the Falcon-7b LLM into a versatile chatbot using techniques like PEFT and QLoRA. This guide walks through adapting large models for specific tasks, enhancing chatbot performance while optimizing resource use with tools like Hugging Face.

DragGAN AI-powered tool for image editing and manipulation

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

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