vincentg64/xLLM
Summarization • Updated
Vincent Granville PRO
vincentg64
AI & ML interests
GenAI, LLM, synthetic data, optimization, fine-tuning, model evaluation
Recent Activity
posted an update 4 days ago
96% Correct Next Token Prediction, with No DNN, no Training, auto-distilled model - https://mltblog.com/4urfvTB
Over the last 12 months, I’ve built a model to predict the next token and to suggest synonyms or related queries to a user prompt, with 100% correct predictions on the training set in one shot, without training or deep neural networks (DNNs). The same model is now integrated in some of the most recent LLM architectures, albeit with costly training via DNNs. My version does not need DNNs or training.
The purpose of this article is to provide validation to my deep neural network alternative in the context of LLMs. The new model is as a substitute to standard DNNs, with increased explainability and higher accuracy. It is designed for corporate corpuses. The end goal is to provide better accuracy at a much lower cost, while providing full control over all the components.
An interesting feature is auto-distillation, whereas the model self-identifies weights that do not contribute over time in 99.9% of user-generated prompts, and drop them, based on prompts from a large, specialized user base. The gain is most spectacular in open-weight LLMs applied to specialized contexts, whether based on DNNs or not.
Read article and download the free technical paper with NVIDIA case study, at https://mltblog.com/4urfvTB
posted an update 4 months ago
BondingAI's new AI Agent for Anomaly Detection & Cybersecurity
With two large litigation firms using it and working on implementation with one of the largest corporate compliance companies (customized version specific to their needs).
The input data comes from an Excel repository, automatically processed by an AI agent part of our BondingAI enterprise solutions. It comes with insights generation via automated SQL queries and pattern detection. In other contexts, the data may come from a PDF repository, the Internet, databases, or a combination of all.
In this article, I showcase animated data produced by the generic anomaly detection agent. The goal is to illustrate granular spatial fraud patterns as they evolve over time in the video, without having to rely on analysts or statisticians to produce striking insights or to clean the data. Each video frame represents a day, with timestamp in the top left corner. The data comes from two different time periods, showing the sharp contrast in fraud patterns between year 2019 and 2022 as you progress in the video.
Once you start the video, use the cursor at the bottom to move backward or forward in time at a different speed, or to stop on any particular day. Read more here.
See also our new book No-Blackbox, Secure, Efficient AI and LLM Solutions. In talks to be published by Wiley, with the following testimonial from a Global Head of AI/ML at JP Morgan Chase: "Your book is great. I have been reading it. It is perfect to be used by regulated industry".
More details at https://www.linkedin.com/pulse/bondingais-new-ai-agent-anomaly-detection-cybersecurity-bonding-ai-kmdwc/ Organizations
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