Insights, tips, and product updates

Llama 3.1 is Meta's latest flagship language model, boasting an impressive 405 billion parameters. This article dives into how the model was trained and fine-tuned, and pulls out new insights for domain-specific LLMs.

Imagine a Michelin-starred restaurant. The success of each dish relies on three key elements: premium ingredients (Food), the right tools (Tool), and a skilled team (Team).

Learn more about DBRX's model training, focus on data quality, and evaluation, showcasing how high-performing enterprise-level large language models are made.

This article explores the best practices and insights from FineWeb's documentation, offering a comprehensive guide for anyone seeking to build high quality datasets for their language models.

Generative AI (GenAI) is transforming industries by significantly boosting productivity and efficiency. This article explores GenAI's impact, emphasizing the crucial role of high-quality datasets in its success. We discuss challenges in creating these datasets and propose innovative solutions to enhance data processes, ensuring optimal performance of GenAI models.

Gemma 2 is an advanced AI model from Google, featuring diverse data integration, rigorous preprocessing, and comprehensive evaluations to ensure high performance, safety, and ethical behavior. Learn how Gemma 2 balances cutting-edge capabilities with robust security and ethical standards.

To ensure widespread adoption and long-term value delivery of your RAG application, following best practices when deploying such use cases is essential. By doing so, we can maximize the potential value that they offer.

Fine-tuning and aligning LLMs can be a challenging and complex process. In this article, we hope to clarify and structure this journey based on our experience with clients and existing examples.

Assessing and benchmarking LLMs makes it easier for data science teams to select the right model and develop a strategy to adapt it faster. Here's a guide to building an LLM evaluation dataset.

Learn how to fine-tune large language models (LLMs) for specific tasks using Kili Technology. This tutorial provides a step-by-step guide and example on fine-tuning OpenAI models to categorize news articles into predefined categories.

Discover examples and techniques for developing domain-specific LLMs (Large Language Models) in this informative guide.

We've updated our article and are sharing 17 open-sourced datasets used for training LLMs, and the key steps to data preprocessing.

Reinforcement learning from human feedback (RLHF) allows large language models to understand human instruction naturally. This approach allows LLMs with lesser parameters to perform better. It also ensures the model avoids producing dangerous behavior. Still, RLHF is an evolving process, with challenges researchers must overcome.

We've updated our platform to provide data scientists and annotators with multilayer views for image annotations. Let's dive deep into how this approach can transform our image annotation process and improve model performance.

Explore OpenELM's specifics, focusing on its architecture, training methodologies, and the diverse datasets it has been trained on. Also, learn how to use it for domain-specific use cases.

Despite their compact size, which enables deployment on devices like smartphones, Microsoft's Phi-3 language models demonstrate impressive reasoning and language understanding capabilities that are competitive with and often exceed those of much larger open-source and proprietary models.

Let's deep dive into Llama 3. This article will provide a breakdown of what Meta has released about its training data so we can better understand how it was trained and what processes were used to build the high-performing LLM.

While standard LLMs excel in generalized tasks, real-world applications demand robust and domain-specific models. Thus, practitioners must fine-tune them on domain-specific data to suit downstream real-world tasks. This post explores fine-tuning LLMs, its benefits, challenges, approaches, and the steps involved.

If you're wondering whether your company should invest in a data labeling tool, keep reading. We've gathered the most common reasons why companies hesitate or decide to make the switch.

In our latest webinar recap: learn how insurance companies overcome training data challenges to ship their models faster.