How to stop parsing JSON by hand and start trusting your model's output The post Pydantic + OpenAI: The Cleanest Way to Get Structured Outputs from LLMs appeared first on Towards Data Science .
Getting reliable, readable responses out of your LLM, and knowing which tool to reach for The post Structured Outputs with LLMs: JSON Mode, Function Calling, and When to Use Each appeared first on Towards Data Science .
The article discusses insights gained from a deeper examination of Structured Outputs when dealing with messy, incomplete data. It highlights that even when a large language model returns perfectly formatted JSON, the content can still be incorrect. The author reflects on the implications of this observation for data science practices.
Why use it? How to implement it?
We are introducing Structured Outputs in the API—model outputs now reliably adhere to developer-supplied JSON Schemas.
The article discusses five failure modes that can slip through constrained decoding in large language models, explaining why these errors are not detected by schema validators. It highlights that even when JSON output is syntactically valid, the underlying data can still be incorrect. The post serves as a warning that relying solely on schema validation is insufficient for ensuring correct structured outputs from LLMs.
The article explains how to transform a small open‑source Qwen LLM into a fast, single‑pass text classifier by replacing its language‑modeling head with a JEV model. It provides a step‑by‑step guide to swapping the head, enabling the LLM to perform classification tasks efficiently. The process leverages the flexibility of open‑source models to create a lightweight, high‑performance classifier.
Release: llm 0. 32.
The article announces the release of llm version 0.35, which introduces a new OpenAI model named gpt-6-astra for GPT-6 Astra. It highlights the addition of this model to the llm library and tags the release with openai, llm, and gpt-6-astra.
The article "How to Fine-Tune an LLM: An End-to-End Guide" offers a practical, hands‑on walkthrough for fine‑tuning large language models in real‑world scenarios. It covers the entire process from data preparation to deployment, providing readers with actionable steps to adapt LLMs to specific tasks. The guide is aimed at practitioners looking to implement fine‑tuning in a structured, end‑to‑end manner.
Google's Open Knowledge Format (OKF) is a Markdown+YAML skeleton for sharing knowledge between humans and AI agents. This post reuses that skeleton for a very specific job — an agent-to-agent hand-off of pre-tokenized integer arrays between three Qwen2.
If you have ever wanted to actually build an LLM inference runtime yourself — pack your own weights, own every barrier, capture your own CUDA graphs — this is what that journey looks like on an H100. A step-by-step tour of a small runtime called annotated-llm-runtime, and the three bugs that produced most of the annotations.
Image inputs and structured outputs with Gemma 4 and Ollama The post Building Multimodal Workflows with a Local LLM appeared first on Towards Data Science .