Menu

Professor Codephreak

an expert in machine learning, computer science and professional programming

chmod +x automindx.install && sudo ./automindx.install

is working. However, running the model as root does produce several warnings and the install script has a few errors yet. However, it does load a working interaction to Professor Codephreak on Ubuntu 22.04LTS

So codephreak is.. and automindx.install is the installer with automind.py interacting with aglm.py and memory.py as version 1 point of departure. From here model work continues on fixing the installer to more sanity, and including the components of MASTERMIND rationality to create logic and prediction. Following MASTERMIND integration adding the RAGE components and putting memory into a vector database seems logical. Am exploring llamaindex a bit more. Work continues. I found pipx today, and this is likely more sane than miniconda3. Also looking at venv to control the environment variables for multi-model scenarios.

Interaction with Professor Codephreak GPT4 Platform Architect and Software Engineer available at

Professor Codephreak Software Engineer Machine Learning Platform Architect

codephreak.openai.agent

Related articles

autotrain

===== Application Startup at 2024-04-27 19:17:38 ===== ========== == CUDA == ========== CUDA Version 12.1.1 Container image Copyright (c) 2016-2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved. This container image and its contents are governed by the NVIDIA Deep Learning Container License. By pulling and using the container, you accept the terms and conditions of this license: https://developer.nvidia.com/ngc/nvidia-deep-learning-container-license A copy of this license is made available in this container at /NGC-DL-CONTAINER-LICENSE for your convenience. WARNING: […]

Learn More

Reliable fully local RAG agents with LLaMA3

https://github.com/langchain-ai/langgraph/blob/main/examples/rag/langgraph_rag_agent_llama3_local.ipynb Building reliable local agents using LangGraph and LLaMA3-8b within the RAGE framework involves several key components and methodologies: Model Integration and Local Deployment: LLaMA3-8b: Utilize this robust language model for generating responses based on user queries. It serves as the core generative engine in the RAGE system. LangGraph: Enhance the responses of LLaMA3 by integrating structured knowledge graphs through LangGraph, boosting the model’s capability to deliver contextually relevant and accurate information. Advanced RAGE Techniques: […]

Learn More
fundamental augmented general intelligence

funAGI workflow fundamental autonomous general intelligence framework

The funAGI system is designed as a modular framework for developing an autonomous general intelligence. The workflow integrates several components and libraries to achieve adaptability, dynamic interaction, continuous optimization, and secure data management. Below is a detailed explanation of the funAGI workflow based on the provided files and documentation. 1. Component Initialization 2. Core AGI Logic 3. User Interaction 4. Reasoning and Logic 5. API and Integration 6. Communication and Interaction 7. Installation and Requirements […]

Learn More
Close Menu