{"repo":"taylorsatula/TeaLeaves","free":true,"listed":false,"github":"https://github.com/taylorsatula/TeaLeaves","clone":"git clone https://github.com/taylorsatula/TeaLeaves.git","description":"End-to-end pipeline for seeing how LLMs actually process your prompts. Capture attention across every layer, render heatmaps and cooking curves, compare variants with evidence — not vibes.","language":"Python","stars":42,"topics":["ai-safety","attention","attention-mechanism","data-visualization","deep-learning","explainable-ai","generative-ai","huggingface","interpretability","llm"],"license":null,"category":"ai-agents","readme_excerpt":"TeaLeaves This is a people-friendly way of seeing how the model is interpreting your prompts in preparation for the inevitable next token prediction. My theory when making this (that was validated after I used/built TeaLeaves) was that very small models (1b,3b) were nearly as capable as larger (8b,12b) models at really high quality (but tightly bounded) structured outputs. There is a maxiumum \"attention & skill\" density in every model by the limit of the weights. If the layers arent' singing in harmony then they're not going to be as effective. TeaLeaves allows you to visualize if your prompt changes made a demonstrible difference in the chances of the output being what you asked for. For example, if you are asking a model for {structured output} and you can get that understanding out of the way early in the generation process the model has more time to focus on the rest of your task(s). Prompt engineering typically relies on eyeballing model outputs after each change. A tweak that seems to improve one case might silently degrade others. TeaLeaves captures how the model distributes attention across every region of your prompt at every layer, so you can measure whether a change helped or hurt, and where in the forward pass the effect occurs. The pipeline was originally built to tune the subcortical prompt for Mira, a persistent digital entity with self-directed memory and context window management. Because subcortical runs on a small model in a single forward pass with no reas","default_branch":null,"files":null,"tree":[],"storefront":"/r/taylorsatula","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/taylorsatula/TeaLeaves/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}