A service to evaluate the performance of Generative AI Models using metrics like BLEU or ROUGE among others. — The evaluation service allows the evaluation of the PaLM 2 (text-bison) foundation and tuned models. This evaluation uses a set of metrics against an evaluation dataset you provided. The process involves creating an evaluation dataset containing prompts and their ideal responses (ground truth pairs).

How to tune LLMs in Generative AI Studio. Vertex AI allows you to fine-tune PaLM models for text, chat, code, and embeddings intuitively and easily. — With all its amazing techniques, Prompt Engineering is a fantastic method that works for many use cases. And I always recommend starting with prompt engineering before thinking about fine-tuning. Still, fine-tuning with all its amazing benefits can be useful:

Vertex AI Model Garden is a collection of pre-built foundation models, task-specific models, and Google ML APIs. — Today, we’re diving into Google’s Vertex AI Model Garden. It’s like a playground for AI models, with an impressive collection of pre-built foundation models, task-specific models, and Google ML APIs. As of this article, 127+ models are available. …

Are the Giants taking the same approach? — AWS Bedrock is the latest GenAI offering on AWS. A successor of its private beta launched in April, Bedrock opens doors to using foundation models (FMs) of leading AI corporations through a single API. Sparing developers the headache of deploying and managing those models themselves. But it also comes months…

We must presume that an attacker can completely manipulate the actions of our LLM and gain valuable insights. — 🛡️ In the digital battlefield of Large Language Models (LLMs), a new adversary known as “Prompt Injection” has risen. Cloaked in the style of normal input, it influences the LLM to execute unintended actions, posing a threat to our LLM applications. In this article, I strip away its camouflage, inspect…

Imagen is one of Google's foundation models, a text-to-image diffusion model developed by Google AI. — Google’s new foundation model, Imagen, is here to bring your dreams to life. Ever wondered what your dreamy doughnut descriptions would look like in real life? Or fantasized about being a pirate with a parrot on your shoulder? (Arr, Matey! 🦜) Take a peek into the world of Image Generation…

LangChain is a framework that’s like a Swiss army knife for large language models (LLMs). — We’ll delve into LangChain’s components by implementing an engaging tour guide, making it an “all-knowing” Berlin expert. Follow along to see why LangChain is truly a game-changer by helping us easily create advanced LLM applications. So fasten your seatbelts and prepare for an engaging journey. With our AI companion powered…

Have you ever wondered if AI could dream? Interestingly, AI doesn’t dream, but it does “hallucinate”. — Large Language Models (LLMs) like PaLM or GPT occasionally demonstrate this intriguing behavior called hallucination. As the use of these models increases in various applications, understanding and managing these hallucinations has become essential. Understanding LLM Hallucinations An LLM hallucination is when the model makes stuff up that either doesn’t make sense or doesn’t…

Parameters in large language models are crucial because they help control the model's behavior. — PaLM 2 and GPT provide parameters for temperature, token limit, top-k, and top-p. This Article explains those parameters with everyday language and relatable examples. Before we dive into the parameters, it's crucial to understand the concept of tokens. The Language of Tokens In the world of large language models, a token can be as…