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In this tutorial, we explore how to apply post-training quantization to an instruction-tuned language model using llmcompressor . We start with an FP16 baseline and then compare multiple compression strategies, including FP8 dynamic quantization, GPTQ W4A16, and SmoothQuant with GPTQ W8A8. Along the way, we benchmark each model variant for disk size, generation latency, throughput, perplexity, and output quality. We also prepare a reusable calibration dataset, save compressed model artifacts, and inspect how each recipe changes practical inference behavior. By the end, we get a practical understanding of how different quantization methods affect model efficiency, deployment readiness, and performance trade-offs. [ Codes with Notebook ] Copy Code Copied Use a different Browser import subprocess, sys def pip(*pkgs): subprocess.check_call([sys.executable, "-m", "pip", "install", "-q", *pkgs]) pip("llmcompressor", "compressed-...

Most programming languages were designed for humans who read error messages, interpret warnings, and manually trace through stack output to fix bugs. AI agents do none of those things well. They work better with structured data: predictable tokens, stable codes, and machine-parseable repair hints. That gap is what Vercel Labs is trying to close by releasing Zero , an experimental systems language that is faster, smaller, and easier for agents to use and repair. What is Zero Language Zero is a systems programming language that sits in the same design space as C or Rust. It compiles to native executables, gives you explicit memory control, and targets low-level environments. What separates Zero from existing systems languages is that its compiler output and toolchain were designed from day one to be consumed by AI agents, not just human engineers. The Agent-First Toolchain The core problem Zero addresses is how agents interact with compiler feedback. In a typical...

In this tutorial, we implement SHAP workflows as a practical framework for interpreting machine learning models beyond basic feature-importance plots. We start by training tree-based models and then compare different SHAP explainers, including Tree, Exact, Permutation, and Kernel methods, to understand how accuracy and runtime change across model-aware and model-agnostic approaches. We also examine how maskers affect explanations when features are correlated, how interaction values reveal pairwise feature effects, and how link functions alter interpretation between the log-odds and probability spaces. Also, we use Owen values, cohort testing, SHAP-based feature selection, drift monitoring, and custom black-box explanations to build a complete interpretability workflow that can run directly in Google Colab. Copy Code Copied Use a different Browser !pip install -q --upgrade shap xgboost transformers import warnings, time, numpy as np, pandas as pd, matplotlib.pyplot as plt from sc...

Training large language models on long sequences has a well-known problem: attention is expensive. The scaled dot-product attention (SDPA) at the core of every transformer scales quadratically Θ(N²) in both compute and memory with sequence length N. FlashAttention addressed this through IO-aware tiling that avoids materializing the full N×N attention matrix in high-bandwidth memory, reducing the memory footprint significantly, but the underlying Θ(N²) compute scaling remains. Researchers at Nous Research have introduced a new method called Lighthouse Attention that addresses this bottleneck specifically at pretraining time, achieving a 1.40× to 1.69× end-to-end wall-clock speedup against a cuDNN-backed SDPA baseline, with matching or lower final training loss. The core problem with existing sparse attention methods To understand why Lighthouse works the way it does, it helps to know what existing sparse attention methods do. Most prior work like NSA, HISA, DSA, MoBA makes the ...