Compute-efficient Way to Scale LLM — Journey around data, model, and compute
Author(s): Anish Dubey Originally published on Towards AI. Context We have repeatedly seen that increasing the model parameters results in better performance (GPT-1 has 117M parameters, GPT-2 has 1.5B parameters, and GPT-3 has 175B parameters). But the next set of questions is …
The Voice of AI
Author(s): Sarah Cordivano Originally published on Towards AI. And how it creates overconfidence in its output Non-members of medium can read this story for free through this friend link. In the last year, ChatGPT and similar tools have written a fair amount …
Counter Overfitting with L1 and L2 Regularization
Author(s): Eashan Mahajan Originally published on Towards AI. Photo by Arseny Togulev on Unsplash Overfitting. A modeling error many of us have encountered or will encounter while training a model. Simply put, overfitting is when the model learns about the details and …
BERT: In-depth exploration of Architecture, Workflow, Code, and Mathematical Foundations
Author(s): JAIGANESAN Originally published on Towards AI. Delving into Embeddings, Masked Language Model Tasks, Attention Mechanisms, and Feed-Forward Networks: Not Just Another BERT Article — A Deep Dive Like Never Before🦸♂️ Image by Vilius Kukanauskas from Pixabay If you’ve been in the …
Genai With Python: Give Your AI a Personality and Speak With ”Her”
Author(s): Mauro Di Pietro Originally published on Towards AI. LLM & Speech Recognition — Build a voice assistant ChatBot on your laptop with OllamaImage by author In this article, I will show how to build an AI with a specific personality and …
Speed up Your ML Projects With Spark
Author(s): Mena Wang, PhD Originally published on Towards AI. Image generated by Gemini Spark is an open-source distributed computing framework for high-speed data processing. It is widely supported by platforms like GCP and Azure, as well as Databricks, which was founded by …
TAI #105: Claude Sonnet 3.5; price alone is progress.
Author(s): Towards AI Editorial Team Originally published on Towards AI. What happened this week in AI by Louie AI news this week was dominated by the surprise release of a new model from Anthropic, which now tops most LLM benchmarks on most …
Evaluating LLMs
Author(s): Louis-François Bouchard Originally published on Towards AI. What, why, when, and how… We always see LLMs beating all benchmarks, like the recent mysterious GPT-2 chatbot beating all models, which was actually GPT-4o. You may have heard similar claims about some models …
A Novel Retrieval-Augmented Generation with Autoencoder-Transformed Embeddings
Author(s): Shenggang Li Originally published on Towards AI. Integrating NLP Techniques for Optimized Query Representation in LLMsPhoto by Kier in Sight Archives on Unsplash If you’ve researched LLMs, you’ve likely encountered Retrieval-Augmented Generation (RAG). It’s a useful technique that improves text generation …
Increasing Robustness and Equity in NLP for Various English Dialects
Author(s): Eera Bhatt Originally published on Towards AI. Natural language processing (NLP) is a popular subfield of machine learning that enables computers to interpret and use human language to achieve certain tasks. To do this, we have to train the computer on …
Understanding Mamba and Selective State Space Models (SSMs)
Author(s): Matthew Gunton Originally published on Towards AI. Image by Author The Transformer architecture has been the foundation of most majorlarge language models (LLMs) on the market today, delivering impressiveperformance and revolutionizing the field. However, this success comeswith limitations. One major challenge …
Want to Learn Quantization in The Large Language Model?
Author(s): Milan Tamang Originally published on Towards AI. Want to Learn Quantization in The Large Language Model? 1. Image by writer: Flow shows the need for quantization. (The happy face and angry face image is by Yan Krukau, https://www.pexels.com/) Before I explain …
A Complete Guide to RAG
Author(s): Igor Novikov Originally published on Towards AI. If you haven’t heard about RAG from your refrigerator yet, you surely will very soon, so popular this technique has become. Surprisingly, there is a lack of complete guides that consider all the nuances …
A Visual Walkthrough of DeepSeek’s Multi-Head Latent Attention (MLA) 🧟♂️
Author(s): JAIGANESAN Originally published on Towards AI. A Visual Walkthrough of DeepSeek’s Multi-Head Latent Attention (MLA) 🧟♂️ Exploring Bottleneck in GPU Utilization and Multi-head Latent Attention Implementation in DeepSeekV2. Image by Vilius Kukanauskas from Pixabay In this article, we’ll be exploring two …
Retrieval Augmented Generation (RAG): A Comprehensive Visual Walkthrough 🧠📖🔗🤖
Author(s): JAIGANESAN Originally published on Towards AI. Retrieval Augmented Generation (RAG): A Comprehensive Visual Walkthrough 🧠📖🔗🤖 Photo by Andrea De Santis on Unsplash You might have heard of Retrieval Augmented Generation, or RAG, a method that’s been making waves in the world …
Popular posts
Updates
Recent Posts
