Bunty Shah’s Post

“Common Agentic Workflow Patterns” - A quick read from Mastering LLM (Large Language Model) 'Coffee Break Concepts' Vol.5 This document deeps dive into Common Agentic Workflow Patterns like 1. Serial Workflow 2. Hierarchical Workflow 3. State Machine Workflows 4. Human Centric Workflows 5. Group Chat Workflows #Comment below on which topic you want to understand next in this "Coffee Break Concepts" series and we will include those topics in upcoming weeks. Explore our comprehensive ‘Mastering LLM Interview Prep Course’ for more insightful content like this. Course Link: https://lnkd.in/dv_DmGhB 50% off using Coupon Code: LLM50 (Limited time) Start your journey towards mastering LLM today! #llm #llms #agenticworkflow #RAG #llmagents #agents #agenticworkflows #CoffeeBreakConcepts #interviewquestions #interviewpreparation #genai #llmtraining #llminterviewprep #llminterviewqn #generativeai

We give pre-trained language models the ability to remember and keep track of information throughout the conversation.  We augment the language model with a small auxiliary recurrent network that passes information by prefixing the regular input with a sequence of vectors, akin to soft prompts, without requiring full-finetuning. https://lnkd.in/eyQPDscW #LRECCOLING2024 #Memory #LanguageModels

LLM Evaluation: A Decision Tree to Get You Started Couple of days back I shared a "LLM Evaluation Guide" and that inspired to create a basic decision tree for selecting the right metrics. Evaluating Large Language Models (LLMs) can be overwhelming. That's why I'm sharing this handy LLM Evaluation Decision Tree. It's a simplified guide to help you get started with choosing the right metrics for your specific needs. Remember, this is just a starting point! There's a whole world of evaluation techniques and nuances to explore like bias and fairness suggested by Aman Kumar in my last post. Let me know what you think and feel free to share what other metric did you find it useful.

Tiny Llama — a Performance Review and Discussion - Learn how you can utilize a tiny large language model, fine-tune it, and achieve high performance by Eivind Kjosbakken

Simplified and intuitive explanation…

https://lnkd.in/dY_2QH2q #langchain4j #ma #llm << ...The Mixture-of-Agents methodology leverages multiple models to boost performance, capitalizing on the collaborative nature of Large Language Models (LLMs). LLMs can be categorized into two roles: Proposers and Aggregators. Proposers generate useful reference responses, providing context and diverse perspectives. Aggregators synthesize responses from other models into a single, high-quality answer. This leads to superior outcomes by leveraging the strengths of multiple models... >>

RAG is a cutting-edge approach that enhances large language models (LLMs) by integrating external data to ensure reliable and consistent information. Explore how RAG can transform your business with accurate and standardized outputs. Swipe through to learn more! #RAG #LLM #Innovation #techtrends

Ready to follow guide to understand how ChatGPT and Gemini are trained and most importantly why their answers feel different. The more time and effort is put into training a model the better its performance will be.