Browsing: LLM

In this tutorial, we build an uncertainty-aware large language model system that not only generates answers but also estimates the confidence in those answers. We implement…

If you’ve got tons of files that you constantly need to search through, you’re likely paying for software that’s reading and summarizing them under the hood.…

Autonomous LLM agents like OpenClaw are shifting the paradigm from passive assistants to proactive entities capable of executing complex, long-horizon tasks through high-privilege system access. However,…

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 build a workflow using Outlines to generate structured and type-safe outputs from language models. We work with typed constraints like Literal, int,…

EAGLE is the state-of-the-art method for speculative decoding in large language model (LLM) inference, but its autoregressive drafting creates a hidden bottleneck: the more tokens that…

The race to build autonomous AI agents has hit a massive bottleneck: data. While frontier models like Claude Code and Codex CLI have demonstrated impressive proficiency…

This post is cowritten by David Stewart and Matthew Persons from Oumi. Fine-tuning open source large language models (LLMs) often stalls between experimentation and production. Training…

Image by Author   # Introduction  For an LLM engineer, the ecosystem of tools and libraries can feel overwhelming at first. But getting comfortable with the right…

Large Language Models (LLMs) are the world’s best mimics, but when it comes to the cold, hard logic of updating beliefs based on new evidence, they…