This short course is a great introduction to RLHF. Highlights: 1. Instructor explained what is RLHF, how it is used in LLMs to align responses to human preferences. 2. How to prepare preference datasets and do reward modelling . 3. How to use reward model in RL Loop to do fine tuning of LLM . All of the above was demonstrated on GCP vertex platform using pipelines and llama2-7B model on summarization task . Next logical learning adventure would be to go through trl library from hugging face , going through blogs and their smol course https://lnkd.in/gBKpJ7vS #rl #rlhf #llm #finetuning #learninginpublic
Pulkit Mehta’s Post
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Let’s rethink education together! | Principal Data Engineer | LLM | Federated learning | Mental Health | Software Engineering
#Day24 of #30DaysOfFLCode Federated Learning in Action Using PySyft I completed the MNIST tutorial: https://lnkd.in/gVr7Q-qD. Thanks to Andrew Trask, Valerio Maggio, Ph.D. and other OpenMined team members for taking the time to create this step-by-step tutorial for running the end-to-end federated learning experience. Go ahead and try this tutorial to experience federated learning firsthand! #30DaysOfFLCode #FederatedLearning
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NeurIPS is finally here! Lambda’s own Corey Lowman is giving a talk tomorrow on harnessing the power of distributed training. Stop by if you want to learn best practices like scaling your training code from single instance to multi-node, diagnostic techniques for quickly identifying cluster issues and freezes during training, and sharding large models with PyTorch FSDP. #NeurIPS2024
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AI & Machine learning Enthusiast | Aspiring Full Stack Developer | Passionate Learner | MongoDB & Web Development Specialist |
Completed a 7-day Machine Learning bootcamp by GDSC in collaboration with TensorFlow! Excited to apply my new skills in real-world projects. 🚀 #MachineLearning #GDSC #TensorFlow
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GPT o1-preview and o1-mini are now on the LiveBench leaderboard. The overall difference compared to Claude 3.5 Sonnet seems quite significant. However, I’m surprised that o1-preview ranks much lower than Claude 3.5 in the coding category — a domain where OpenAI claimed substantial improvements. I’m curious to see how o1-preview will perform on the LMSYS leaderboard, which we might see early next week.
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Helping Mindset & Performance Coaches Attract More Clients with Authentic Copy & Websites | Strategic Partner | Writer
🚀 Completing Step 2 of Strivers' A2Z Course #striversa2zdsa! 🚀 🔍 Exploring Sorting Algorithms: 🔍 🔹 Selection Sort 🔹 Bubble Sort 🔹 Insertion Sort 🔹 Merge Sort 🔹 Quick Sort 🔹 Bubble Sort and Insertion Sort with Recursion Unlock the power of sorting algorithms and elevate your DSA skills with Strivers' A2Z DSA Course. Join me on this exciting learning journey today! 💡 #DataStructures #Algorithms #SortingAlgorithms #StriversA2ZDSACourse #TechEducation
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Senior Solution Architect | Solution Manager - 5G/6G Telco Applications on Cloud | Intrapreneur @ Ericsson ONE
Great to work through the different classification models available in sklearn ( https://lnkd.in/eg7Wga5G ) as part of this bootcamp. Pleasantly surprised by the performance of XGBoost even with default parameters.
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Amit Kesarwani is providing an online virtual workshop, From Chaos to Control: Mastering ML Reproducibility at Scale today (Wednesday July 10) between 2:00-3:30 PM Eastern Time. In this session, you will learn how to use a data versioning engine (@lakeFS) to intuitively and easily version #mlexperiments and reproduce any specific iteration of the experiment. Using a live code example, this talk will teach you: » How to create a basic ML experimentation framework with lakeFS using a Jupyter notebook » How to reproduce ML components from a specific iteration of an experiment » How to build intuitive, zero-maintenance experiments infrastructures #mlreproducibility #TMLS #machinelearning Toronto Machine Learning Society (TMLS) Register here https://lnkd.in/dVbmYzwK
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🚢 Titanic Survival Prediction Project 🎥 Excited to share my latest project! I built a model that predicts the survival of passengers on the Titanic using machine learning techniques. Watch the video for insights into the project and my approach to solving this classic problem. Check out the source code on GitHub: https://lnkd.in/eDu3CYpE I'd love to hear your feedback! CodSoft #CodSoft #MachineLearning #DataScience #ProjectShowcase #Titanic #MLJourney
