Browsing: FineTuning

You can use reinforcement Fine-Tuning (RFT) in Amazon Bedrock to customize Amazon Nova and supported open source models by defining what “good” looks like—no large labeled…

print(“\n📊 MODEL EVALUATION\n”) eval_results = trainer.evaluate() print(” Evaluation Results:”) for key, value in eval_results.items(): if isinstance(value, float): print(f” {key:<25}: {value:.4f}”) from sklearn.metrics import classification_report, confusion_matrix preds_output…

Today, we’re sharing how Amazon Bedrock makes it straightforward to customize Amazon Nova models for your specific business needs. As customers scale their AI deployments, they…

In this tutorial, we build a complete end-to-end pipeline using NVIDIA Model Optimizer to train, prune, and fine-tune a deep learning model directly in Google Colab.…

AI demos often look impressive, delivering fast responses, polished communication, and strong performance in controlled environments. But once real users interact with the system, issues surface…

Last year, AWS announced an integration between Amazon SageMaker Unified Studio and Amazon S3 general purpose buckets. This integration makes it straightforward for teams to use…

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,…