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Hi there πŸ‘‹

My name is Bhavul. I'm an Applied ML Researcher/Engineer currently working for Meta AI in their Generative AI team, building on Large Language Models and Latent Diffusion Models to build innovative new products. Before GenAI, I was building foundational models to understand users for improving Reels and Video recommendations. Prior to Meta, I worked as a Research Scientist (ML Tech Lead) at Zapr driving company's Applied NLP Research efforts, and also their Recommendation System projects.

I like to engage in AI research independently too. I'm a core researcher with Manifold RG and I'm also experimenting with a few ideas in the LLM space. Feel free to reach out for collaboration. I also enjoy building products, automating things and so I try to maintain some grip on full stack knowledge. I'm an advocate for Open Source and "AI for Good", and enjoy giving talks at conferences or tech meetups.


  • Mostly AI content : Follow me on Threads
  • AI + Shitposting + Random Blabber : Twitter Follow
  • Less frequent, more serious AI : Linkedin: bhavul

Portfolio Website
Technical Blog

Carpe Diem.

Bhavul's github stats Top Languages


  1. LLM_training_strategies LLM_training_strategies Public

    Index of useful details for training large language models

    13 3

  2. JIRA-Similar-Issue-Finder-App JIRA-Similar-Issue-Finder-App Public

    Classical NLP Algorithms based JIRA Bot that can train a machine learning model and comment related JIRA IDs on a list of JIRA issues.

    Jupyter Notebook 21 14

  3. hinglish-data-generation hinglish-data-generation Public

    This is a try at generating a big dataset of hinglish, which currently does not exist


  4. Headless-RaspberryPi-Twitter-Controller-Bot Headless-RaspberryPi-Twitter-Controller-Bot Public

    A simple twitter bot that runs on raspberry pi and could return its IP address and more info.


  5. Caltech-101-Object-Classification Caltech-101-Object-Classification Public

    Multiple approaches tried for Object classification on Caltech 101 Dataset

    Jupyter Notebook 6 5

  6. Troubleshooting Convolutional Neural... Troubleshooting Convolutional Neural Nets
    ### Troubleshooting Convolutional Neural Networks 
    #### Intro
    This is a list of hacks gathered primarily from prior experiences as well as online sources (most notably [Stanford's CS231n]( course notes) on how to troubleshoot the performance of a convolutional neural network . We will focus mainly on supervised learning using deep neural networks. While this guide assumes the user is coding in Python3.6 using tensorflow (TF), it can still be helpful as a language agnostic guide.