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.

ruvnet

Sentient Systems Architecture (SSA): Unlocking Embodied Intelligence

Introduction

Artificial Intelligence (AI) has evolved rapidly, with language models like GPT-4 capturing attention. However, the real future of AI lies in embodied intelligence—systems that can interact with the physical world through robotics and sensory perception. Unlike disembodied language models that operate in digital spaces, embodied AI must navigate complex environments, interpret sensory data, and perform physical tasks. This shift towards embodied intelligence opens the door to groundbreaking applications and significant economic impact.

Unique Challenges of Embodied Intelligence

Developing embodied AI systems is far more complex than working with traditional language models. Embodied agents need to:

Adapt to ever-changing real-world conditions.

ruvnet

import asyncio
from pathlib import Path
from lionagi.libs import SysUtil, ParseUtil
from typing import Any
from pydantic import Field
from lionagi.core import Session
from lionagi.core.form.action_form import ActionForm

import importlib
import subprocess

ruvnet

AI Digital Twin: Bridging Generations

Introduction

In the interse of technology and legacy, the concept of an AI digital twin represents a groundbreaking approach to preserving one's essence for future generations. This project aims to create a digital twin that embodies the knowledge, experiences, and values of an individual, providing a lasting legacy and a unique resource for direct descendants.

Concept

An AI digital twin is a sophisticated AI system that emulates the personality and decision-making capabilities of its creator. Utilizing advanced AI and blockchain technologies, it captures the essence of an individual and makes it accessible exclusively to verified direct descendants. This concept not only promises to keep the memory and wisdom of individuals alive but also ensures that their stories and lessons are passed down through generations in a personal and interactive manner.

ruvnet

import os
import asyncio
import subprocess
import importlib
import sys
from dotenv import load_dotenv
from lionagi import Session
from e2b_code_interpreter import CodeInterpreter
from llama_index.core import (
    VectorStoreIndex,

ruvnet

import asyncio
import random
import lionagi as li
from typing import Dict, List

templates = {
    "Business Analysis": {
        "context": "Acme Corporation, a leading multinational conglomerate, is actively exploring strategic investment opportunities in emerging technologies to maintain its competitive edge and drive future growth. The board of directors has convened a special committee to conduct a comprehensive analysis of the technological landscape and identify the most promising areas for investment. The committee seeks in-depth insights and recommendations on which cutting-edge technologies have the potential to revolutionize Acme's core industries and create new market opportunities over the next decade.",
        "prompt": "Conduct a thorough evaluation of the potential impact and investment viability of four key emerging technologies: artificial intelligence (AI), blockchain, quantum computing, and biotechnology. For each technology, provide a detailed assessment of its current state of developme

ruvnet

import os
from llama_index.core import (
    VectorStoreIndex,
    SimpleDirectoryReader,
    StorageContext,
    load_index_from_storage,
)

# Configure logging
import logging

ruvnet

AI-Powered Function Generator

This project is an AI-powered tool that generates Python functions based on natural language prompts. It leverages the power of large language models (LLMs) like GPT-3.5 and GPT-4 to understand the user's intent and generate code that meets the specified requirements.

Introduction

The AI-Powered Function Generator is designed to streamline the process of writing Python functions. Instead of manually coding each function from scratch, developers can provide a high-level description of what they want the function to do, and the tool will automatically generate the corresponding Python code.

This project aims to improve developer productivity, reduce coding errors, and enable rapid prototyping. By leveraging the capabilities of advanced language models, it can generate functions that adhere to best practices and coding standards.

ruvnet

ruvnet

Personalized Speaking Pattern Guide

Overview

This document outlines the preferred speaking pattern, tone, structure, and cadence for future articles and content creation. It emphasizes a conversational style that avoids certain phrases and words to maintain authenticity and avoid sounding artificial.

1. Tone

• Conversational and Engaging: The dialogue should be light and engaging, like a chat with a friend.
• Reflective and Personal: Offers a thoughtful tone, sharing personal insights and observations.

2. Structure