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Generalized LoRA (GLoRA) Paper Reading

Introduction In this week’s paper reading, we discuss “One-for-All: Generalized LoRA for Parameter-Efficient Fine-tuning.” GLoRA is a universal, parameter-efficient fine-tuning approach for diverse tasks. It enhances LoRA with a generalized…

Paper Readings

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HyDE Paper Reading and Discussion

Introduction In this paper reading, we explore HyDE: Precise Zero-Shot Dense Retrieval without Relevance Labels. HyDE is a thrilling zero-shot learning technique that combines GPT-3’s language understanding with contrastive text…

29 minutes read

By Sarah Welsh | 29 minutes read

Paper Readings

summarize text with large language models like chatgpt cover image

How To Troubleshoot LLM Summarization Tasks

This blog is co-authored by Xander Song, Developer Advocate at Arize Follow along in the Colab version of this blog Introduction Large language models (LLMs) are revolutionizing the field of…

5 minutes read

By Hakan Tekgul | 5 minutes read

Uncategorized

Voyager: An Open-Ended Embodied Agent with LLMs Paper Reading and Discussion

Introduction In this paper reading, we discuss Voyager: the first LLM-powered embodied lifelong learning agent in Minecraft. Voyager autonomously explores the world, acquires skills, and makes discoveries without human intervention….

30 minutes read

By Sarah Welsh | 30 minutes read

Paper Readings

LoRA: Low-Rank Adaptation of Large Language Models Paper Reading and Discussion

Introduction In this paper reading, we discuss LoRA, which freezes the pre-trained model weights and injects trainable rank decomposition matrices into each layer of the Transformer architecture, greatly reducing the…

27 minutes read

By Sarah Welsh | 27 minutes read

Paper Readings

Retrieval-Augmented Generation – Paper Reading and Discussion

Introduction In this paper reading, we discuss “Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks.” We know GPT-like LLMs are great at soaking up knowledge during pre-training and fine-tuning them can lead…

33 minutes read

By Sarah Welsh | 33 minutes read

Paper Readings

AI Ethical Issues Unraveled: Building a Fair, Transparent, and Responsible Future

In recent years, AI has permeated nearly every aspect of our lives, from healthcare and finance to education and entertainment. As AI technologies continue to advance, so do the ethical…

6 minutes read

By Sally-Ann DeLucia | 6 minutes read

Responsible AI

LIMA: Less Is More for Alignment – Paper Reading and Discussion

Introduction In this paper reading, we discuss “LIMA: Less Is More for Alignment.” This research delves into the efficiency and effectiveness of large language models, demonstrating the power of pre-training…

24 minutes read

By Sarah Welsh | 24 minutes read

Paper Readings

Drag Your GAN: Interactive Point-based Manipulation on the Generative Image Manifold – Paper Reading and Discussion

Introduction In this paper reading, we dive into “Drag Your GAN: Interactive Point-based Manipulation on the Generative Image Manifold.” Drag Your GAN introduces a novel approach for achieving precise control…

22 minutes read

By Sarah Welsh | 22 minutes read

Paper Readings

Cross Validation: What You Need To Know, From the Basics To LLMs

From the basics to how to do cross validation on large language models What Is Cross Validation? Imagine the machine learning model you have created has accuracy as 98%. Do…

12 minutes read

By Natasha Sharma | 12 minutes read

Data Science LLMOps

Evaluating Model Fairness

In many ways, bias and fairness in AI are two sides of the same coin; bias is a result, fairness is an action. Bias is inherently present in the world…

9 minutes read

By Sally-Ann DeLucia | 9 minutes read

Responsible AI

Getting To Know MLflow: a Comprehensive Guide to ML Workflow Optimization

What is the purpose of a model store in machine learning? A model store serves as a centralized repository for machine learning models, streamlining storage, organization, and management. During the…

8 minutes read

By Dat Ngo | 8 minutes read

Data Science

Exploring the Future of AI Community with Cerebral Valley Founder Ivan Porollo

Ivan Porollo is a co-founder of Cerebral Valley, a new community at the center of the AI renaissance currently underway in the San Francisco Bay Area. A software engineer-turned-founder, Ivan…

5 minutes read

By Aparna Dhinakaran | 5 minutes read

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Generalized LoRA (GLoRA) Paper Reading

Start your learning or brush up on your skills with our LLM and ML learning courses

HyDE Paper Reading and Discussion

How To Troubleshoot LLM Summarization Tasks

Voyager: An Open-Ended Embodied Agent with LLMs Paper Reading and Discussion

LoRA: Low-Rank Adaptation of Large Language Models Paper Reading and Discussion

Retrieval-Augmented Generation – Paper Reading and Discussion

AI Ethical Issues Unraveled: Building a Fair, Transparent, and Responsible Future

LIMA: Less Is More for Alignment – Paper Reading and Discussion

Drag Your GAN: Interactive Point-based Manipulation on the Generative Image Manifold – Paper Reading and Discussion

Cross Validation: What You Need To Know, From the Basics To LLMs

Evaluating Model Fairness

Getting To Know MLflow: a Comprehensive Guide to ML Workflow Optimization

Exploring the Future of AI Community with Cerebral Valley Founder Ivan Porollo

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