My Current Red Hat Enterprise Linux 10.2 AI/ML Setup


Core & Foundations

  • Red Hat Enterprise Linux 10.2 (Workstation edition)
  • Python 3 with pip
  • PyTorch (CPU version)
  • Development Tools (gcc, make, git, etc.)

Major AI Frameworks

  • LangChain + LangGraph
  • Llama-Index (great for RAG)
  • CrewAI (for building AI agents)
  • Gradio and Streamlit (for creating web UIs)
  • Diffusers (for image generation)

Supporting Libraries

  • numpy, pandas, scikit-learn, matplotlib
  • ChromaDB, FAISS (vector databases)
  • Sentence-Transformers
  • Unstructured, PyMuPDF (document processing)
  • PEFT, TRL, Accelerate (model fine-tuning)

Local LLM

  • Ollama with llama3.2:1b model

Tools

  • Visual Studio Code
  • htop, gnome-tweaks, etc.

What I Can Build Now:

  1. Local Chatbots with nice web interfaces (Gradio)
  2. RAG Systems — Chat with my own PDFs and documents
  3. AI Agents using CrewAI
  4. Image Generation apps
  5. Streamlit Dashboards