Browsing: FineTuning

Poetiq has just published some very interesting results showing its Meta-System reached a new state-of-the-art on LiveCodeBench Pro (LCB Pro), a competitive coding benchmark, by automatically…

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…

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…

In this tutorial, we work with Microsoft’s OpenMementos dataset and explore how reasoning traces are structured through blocks and mementos in a practical, Colab-ready workflow. We…

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”, ])…

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…

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.…

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.…

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…