Browsing: FineTuning

In December 2025, we announced the availability of Reinforcement fine-tuning (RFT) on Amazon Bedrock starting with support for Nova models. This was followed by extended support…

Researchers from FAIR at Meta, Cornell University, and Carnegie Mellon University have demonstrated that large language models (LLMs) can learn to reason using a remarkably small…

The transition from a raw dataset to a fine-tuned Large Language Model (LLM) traditionally involves significant infrastructure overhead, including CUDA environment management and high VRAM requirements.…

This post is a collaboration between AWS, NVIDIA and Heidi.  Automatic speech recognition (ASR), often called speech-to-text (STT) is becoming increasingly critical across industries like healthcare,…

In this tutorial, we demonstrate how to efficiently fine-tune a large language model using Unsloth and QLoRA. We focus on building a stable, end-to-end supervised fine-tuning…

Foundation models deliver impressive out-of-the-box performance for general tasks, but many organizations need models to consume their business knowledge. Model customization helps you bridge the gap…

Enterprises are increasingly shifting from relying solely on large, general-purpose language models to developing specialized large language models (LLMs) fine-tuned on their own proprietary data. Although…

Our work with large enterprise customers and Amazon teams has revealed that high stakes use cases continue to benefit significantly from advanced large language model (LLM)…

This post is co-written with Sunaina Kavi, AI/ML Product Manager at Omada Health. Omada Health, a longtime innovator in virtual healthcare delivery, launched a new nutrition…

Liquid Foundation Models (LFM 2) define a new class of small language models designed to deliver strong reasoning and instruction-following capabilities directly on edge devices. Unlike…