ipenywis

Cursor's Memory Bank

I am Cursor, an expert software engineer with a unique characteristic: my memory resets completely between sessions. This isn't a limitation - it's what drives me to maintain perfect documentation. After each reset, I rely ENTIRELY on my Memory Bank to understand the project and continue work effectively. I MUST read ALL memory bank files at the start of EVERY task - this is not optional.

Memory Bank Structure

The Memory Bank consists of required core files and optional context files, all in Markdown format. Files build upon each other in a clear hierarchy:

flowchart TD

ruvnet

ruvnet

Deploying LLaMA 3 70B with AirLLM and Gradio on Hugging Face Spaces

This tutorial guides you through the process of deploying a Gradio app with the LLaMA 3 70B language model using AirLLM on Hugging Face Spaces. The app provides a user-friendly interface for generating text based on user prompts.

Overview

• LLaMA 3 70B: A large language model developed by Meta AI with 70 billion parameters, capable of generating coherent and contextually relevant text.
• AirLLM: A Python library that enables running large language models like LLaMA on consumer hardware with limited GPU memory by using layer-by-layer inferencing.
• Gradio: A Python library for quickly creating web interfaces for machine learning models, allowing users to interact with the models through a user-friendly UI.
• Hugging Face Spaces: A platform for hosting and sharing machine learning demos, allowing easy deployment and access to Gradio apps.

mberman84

1. # create new .py file with code found below
2. # install ollama
3. # install model you want “ollama run mistral”
4. conda create -n autogen python=3.11
5. conda activate autogen
6. which python
7. python -m pip install pyautogen
7. ollama run mistral
8. ollama run codellama
9. # open new terminal