Have you ever thought about the necessity of an assistant that could answer an interviewer’s question for you? With the current advancements in AI, this has become a reality! In this article, we will build a small application using Whisper for voice recognition and ChatGPT for text generation. We will…

Is it possible to differentiate between what is written by a large language model and a human? — It’s almost impossible to distinguish between human-written text and large language model (LLM) generated text. Given the potential chaos that can result from the spread of misinformation, fake news, phishing, and social engineering, finding ways to identify LLM-generated text and verify its content is crucial.

Why LLMs hallucinate, approaches for mitigation, challenges with evaluation datasets, and more — Generative Large Language Models (LLMs) can generate highly fluent responses to various user prompts. However, their tendency to hallucinate or make non-factual statements can compromise trust. I think we will get the hallucination problem to a much, much better place… it will take us a year and a half, two…

A comparison of self-hosted LLMs and OpenAI: cost, text generation quality, development speed, and privacy. — During the LLM hype, you can find a lot of articles like “Fine-tune your Private LLaMA/Falcon/Another Popular LLM”, “Train Your Own Private ChatGPT”, “How to Create a Local LLM” and others. At the same time, only few people tell why you need it. I mean, are you really sure you…

Finally, a comprehensive guide into LLMs inference and serving with detailed comparison. — While browsing through LinkedIn, I came across a comment that made me realize the need to write a simple yet insightful article to shed light on this matter: “Despite the hype, I couldn’t find a straightforward MLOps engineer who could explain how we can deploy these open-source models and the…

Key differences from LLaMA 1, safety & violations, Ghost Attention, and model performance. — Instant Access to Resources and Important Links Paper GitHub repository HuggingFace models (you need to request access by agreeing to the license) Playground (using text-generation-inference) LLaMA 2 model family Meta just released the new state-of-the-art open LLM, which is a collection of pre-trained and fine-tuned models ranging in scale from 7 billion to 70 billion parameters:

Unleash your digital twin — This article aims to illustrate how to fine-tune a top-performing LLM efficiently and cost-effectively on a custom dataset. We will explore the utilization of the Falcon-7B model with LoRA adapters using Lit-GPT. Ever wondered what it would be like to have a digital twin? A virtual replica of yourself that…

TLDR; techniques to speed up inference of LLMs to increase token generation speed and reduce memory consumption: mixed-precision, Bfloat16, quantization, fine-tuning with adapters, pruning, continuous batching, and multiple GPUs — Companies, from small startups to large corporations, want to utilize the power of modern LLMs and include them in the company’s products and infrastructure. One of the challenges they face is that such large models require a huge number of resources for deployment (inference).

How we designed a working ML model for scoring DeFi borrowers, solving the problems of pseudoanonymity, huge data size, and ubiquitous bots — Introduction Lending is one of the biggest DeFi categories by TVL: $13.45 billion in June 2023. Leading protocols like Aave and Compound have loan volumes numbering in billions of dollars. Users are attracted by the possibility of borrowing money without any documents or credit checks — in fact, you don’t even…