Guides and deep dives on fine-tuning, prompt engineering, and shipping AI that works.
If you’re a content marketer worth your weight in SEO keywords, you’ve undoubtedly tried GPT-4 for writing long-form articles and been disappointed. We finally cracked the code on how to generate 3,000 word blog content that matches our style and tone using AI.
In this guide, we’ll show you how to fine-tune a simple Llama-2 classifier that predicts if a text’s sentiment is positive, neutral, or negative. At the end, we’ll download the model weights. And best of all, we’re going to do it without configuring a GPU or writing a line of code.
In this article, we’ll explain how LoRA works in plain English. You will gain an understanding of how it’s similar and different to full-parameter fine-tuning, what is going on behind the scenes, and the hyperparameters you need to adjust to use it in practice.
Large language models are first trained on massive text datasets in a process known as pre-training: gaining a solid grasp of grammar, facts, and reasoning. Next comes fine-tuning to specialize in particular tasks or domains. And let's not forget the one that makes prompt engineering possible: in-context learning, allowing models to adapt their responses on-the-fly based on the specific queries or prompts they are given.
Fine-tuned GPT-3.5 models match the quality of GPT-4 when fine-tuned on high-quality examples—and you only need 50 to start seeing clear quality improvements versus few-shot learning.
The GPT-3.5 Turbo model is now available for fine-tuning on OpenAI and there are a lot of changes for developers to implement in order to start using the new model. Here's our breakdown of what's changed.
Learn how fine-tuned AI models can make your business more efficient, reduce costs, deliver better customer experiences, and enable scaling to new heights.
AI models often start as generalists, much like a jack of all trades. It's through the process of fine-tuning that these systems are sculpted into industry-specific maestros.
Hyperparameters are variables that you set before the training to control the learning process. How you set these will significantly influence your model's performance and speed of learning. Unfortunately, there is no mathematical rule for setting them — the official method is trial and error.
Prompt injection refers to a vulnerability where the user’s input attempts to override instructions in the prompt for a chat-based large language model (LLM) like ChatGPT.
What is a large language model and how does it actually work? We'll explore the ins and outs of large language models – what they are, how they work, their applications, limitations, and what the future might hold for them.
Learn how to fine-tune GPT-3 in 15 minutes & without writing a single line of code. Entry Point AI makes fine-tuning GPT-3 easy.
We will compare prompt engineering, embeddings, and fine-tuning, show you when it makes sense to use each, and even opportunities to use them together.
Fine-tuning has a reputation for being tricky to get right. However, by knowing the most common mistakes people make ahead of time, you can increase your chances of success.
If you're looking to fine-tune large language models (LLMs) and get results fast, you've come to the right place. Entry Point AI is a layer built on top of popular fine-tuning APIs to take care of all the technical details for you.
This was my first time incorporating AI into a product, and I learned a lot building it. Here are 3 of the most important insights I’ve learned about integrating AI into a product and what to do about them.
In this article, we've gathered over 40 interesting statistics that give you an inside look into the world of AI. These numbers cover important topics like market size, revenue, how AI is being used in different industries, and its effects on jobs. We'll also explore what the future might hold for AI, giving you an idea of the changes and advancements we can expect.
Large Language Models are revolutionizing marketing automation by accurately predicting customer intent, requiring less maintenance, and crafting personalized customer journeys — outpacing traditional methods by a long shot.
Recent advances in artificial intelligence are paving the way for even more personalized and efficient recommendations that are set to revolutionize the industry.