Browsing: LLMs

in American culture is the following: “You can’t have your cake and eat it too.” I find this sentence extremely poetic but also very practical and…

on a feature where I had to transform 100 messy compliance pdfs into structured JSON rules. The brute force approach was obvious: give the agent the…

you ask an LLM to simulate 6,000 American households answering questions about inflation? Recent papers find that large language models can replicate the average responses of…

There’s a growing assumption that if you connect a large language model (LLM) to your production system or application, it will simply “know” how to answer…

import subprocess, sys def pip(*pkgs): subprocess.check_call([sys.executable, “-m”, “pip”, “install”, “-q”, *pkgs]) pip(“llmcompressor”, “compressed-tensors”, “transformers>=4.45”, “accelerate”, “datasets”) import os, gc, time, json, math from pathlib import Path…

manager. Your team has just spent three weeks refactoring the prompt chain for your company’s internal AI research agent. They deploy the new version to a…

Scaling large language models (LLMs) is expensive. Every token processed during inference and every gradient computed during training flows through feedforward layers that account for over…

have a simple problem: they show you the forecast, but they don’t tell you when it actually changed. That might sound trivial. It isn’t. Modern numerical…

LLMs have taken the world by storm. People are mostly using polished APIs of LLMs, they type a prompt, get an answer. What they miss is…

When running LLMs at scale, the real limitation is GPU memory rather than compute, mainly because each request requires a KV cache to store token-level data.…