• Paper Summaries

    LLaMA-Omni: Seamless Speech Interaction with Large Language Models Introduction In-Depth Look at the LLaMA-Omni Model Architecture Constructing the Training Data: InstructS2S-200K Evaluating LLaMA-Omni’s Capabilities The Perfect Blend: Redefining RLHF with Mixture of Judges Introduction Architecture and Implementation Experimental Results Key contributions of the paper LLaMA-Omni: Seamless Speech Interaction with Large...

  • Diffusion Models

    The Diffusion Process: Reversing the Diffusion: Denoising Synthesizing New Data: Image Generation from Pure Noise Harnessing Text: Conditional Image Generation The Advantages of Diffusion Models: Quality, Diversity, and Stability Diving into the Mechanics of Diffusion Models Forward Diffusion: Reverse Diffusion: The Loss Function: Guiding the Denoising Symphony Time Embeddings: Providing...

  • Building your own Autonomous LLM Agent

    Plan & Tasks Task Object Plan Object Actions and Observations Memory LLM Agent Autonomous LLM Agent References In this blog, we are going to talk about LLM-based autonomous agents. Unlike the typical LLMs we’re accustomed to, which primarily focus on generating textual content, an autonomous LLM agent transcends this by...

  • Aligning LLMs using Human Edits

    Understanding SALT Loss Function Likelihood Loss ((L_p)) Unlikelihood Loss ((L_r)) Additionally Example Catastrophic Forgetting Issue The Concept of Imitation Edits Solution Leveraging RSALT for Effective Training Strategic Training Methodology SALT vs DPO/RLHF Conclusion There has been some cool progress in Aligning Large Language Models to human preferences, and guess what?...

  • Preference Training for LLMs in a Nutshell

    Reinforcement Learning with Human Feedback Two reward models instead of One Rejection Sampling Reinforced Self Training (ReST) Direct Preference Optimization Conclusion References Large Language Models (LLMs) harness the power of unsupervised learning(self-supervised to be precise) on extensive datasets, showcasing remarkable natural language processing capabilities. However, these models exhibit inherent limitations...

  • Efficient Book Summarization using Large Language Models

    Recursive Approach Extractive Summarization Approach What is Extractive Summarization? TextRank Based Approach Clustering Based Approach Combined Approach Conclusion References In the realm of summarizing entire books using Large Language Models (LLMs), the challenge of context length emerges as a prominent hurdle. These models, while powerful, are constrained by their capacity...

  • Building your own Llama(🦙)Chat

    Serving the Llama-2 Model for Live Chat Generation Conclusion References Ever wanted to create your very own chatbot, just like the ones you talk to on the internet? Well, you’re in for a treat! In this blog, I’ll guide you through the process using Llama-2 LLM. But wait, there’s more....

  • Extrapolating to longer sequence lengths using ALiBi

    Positional Encoding-based Attention Drawbacks of Positional Encoding Limited Context Fixed Patterns ALiBi: Attention with Linear Biases Mathematical Expression Advantages of ALiBi Conclusion Reference One of the core components of transformer models is the attention mechanism, which allows the model to focus on specific parts of the input sequence while processing...