Building a Vision App with Ollama Structured Outputs
In this video, I look at the Ollama structured outputs and how you can use it to do various tasks, such as named entity recognition and information extractio...
DSPy Documentation
The framework for programming—rather than prompting—language models.
pytudes/ipynb/CherylMind.ipynb at main · norvig/pytudes
There has been much debate on the degree to which Large Language Models (LLMs) have a theory of mind: a way of understanding what other people know and don't know. In this notebook I explore one small part of the issue by asking nine LLM chatbots to solve the Cheryl's Birthday Problem, a well-known logic puzzle in which different characters have different states of knowledge at different times.
leapingio/leaping
Leaping's pytest debugger is a simple, fast and lightweight debugger for Python tests. Leaping traces the execution of your code and allows you to retroactively inspect the state of your program at any time, using an LLM-based debugger with natural language.
It does this by keeping track of all of the variable changes and other sources of non-determinism from within your code.
Code LoRA from Scratch - a Lightning Studio by sebastian
LoRA (Low-Rank Adaptation) is a popular technique to finetune LLMs more efficiently.
This Studio explains how LoRA works by coding it from scratch, which is an excellent exercise for looking under the hood of an algorithm.
srush/MiniChain: A tiny library for coding with large language models.
A tiny library for coding with large language models. - srush/MiniChain: A tiny library for coding with large language models.
jackmpcollins/magentic: Seamlessly integrate LLMs as Python functions
Seamlessly integrate LLMs as Python functions. Contribute to jackmpcollins/magentic development by creating an account on GitHub.
Google Colaboratory