Browsing: FineTuning

In this tutorial, we explore the lambda/hermes-agent-reasoning-traces dataset to understand how agent-based models think, use tools, and generate responses across multi-turn conversations. We start by loading…

Large language models (LLMs) now drive the most advanced conversational agents, creative tools, and decision-support systems. However, their raw output often contains inaccuracies, policy misalignments, or…

Audio AI has had a breakout year. Automatic speech recognition has gotten dramatically better with models like OpenAI’s Whisper variants, NVIDIA’s Parakeet, and Mistral’s Voxtral. Audio…

import subprocess, sys, os, shutil, glob def pip_install(args): subprocess.run([sys.executable, “-m”, “pip”, “install”, “-q”, *args], check=True) pip_install([“huggingface_hub>=0.26,<1.0”]) pip_install([ “-U”, “transformers>=4.49,<4.57”, “accelerate>=0.33.0”, “bitsandbytes>=0.43.0”, “peft>=0.11.0”, “datasets>=2.20.0,<3.0”, “sentence-transformers>=3.0.0,<4.0”, “faiss-cpu”, ])…

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…