Overview

You are building a multi-agent AI cluster using RWKV-7 models. Your focus is on low-resource, efficient models that support long-term memory, recursion, and modular specialization. You’re using RWKV due to its linear-time, constant-space RNN-style architecture, making it ideal for edge devices and scalable, recursive reasoning.

🔧 System Architecture

• Cluster Composition:
  • Node 0 (192.168.1.30) – Central file share and primary comms.
  • Node 1 (192.168.1.31) – 26 CPUs, 32GB RAM, NVIDIA 2060 GPU.
• Model Deployment:
  • RWKV-7 is the focus; RWKV-65M is used as the fallback base.
  • Tokenizer sourced from rwkv_pip_package/src/rwkv/rwkv_tokenizer.py.
• Environment:
  • Ubuntu 24.04
  • Isolated in Python venv environments
  • MPICH used for message-passing instead of OpenMPI
  • Excluded: fail2ban, ufw, htop, nvtop (lean install)
  • tmux used for persistent sessions
  • Flask + Jinja2 for Web UI, styled as a dark terminal theme

Model Logic and Behavior

• RWKV Purpose:
  • Serves as the default fallback model per node
  • Meant to operate autonomously, adaptively evolving
  • Agents self-assign based on logs, runtime behavior, and symbolic drift
• Tokenizer & Inference:
  • Pipeline inference scripts were developed, including debugging steps for:
    • Tokenizer loading
    • Input/output shape validation
    • CUDA and driver conflicts (e.g., NVIDIA-SMI checks)

Deployment Enhancements

You designed and iterated on an install and diagnostics script, which:

• Parses and validates virtual environment paths
• Confirms NVIDIA GPU visibility
• Logs and verifies each installation step
• Supports AutoFix steps during deployment
• Includes version/timestamped cluster checkpoint JSONs for traceability

Special Features and Goals

• Recursive Memory Engine (planned): Eventually intend to include decentralized memory graph for long-term token tracking.
• Idle-cycle Evolution: RWKV agents mutate and specialize during low usage.
• Persistent Indexing: AI cluster parses and learns from your ChatGPT export logs to form a personal memory base.
• Multi-Agent Design: RWKV nodes are treated as evolving digital organisms with emerging roles.

✅ Achievements

• ✅ Successful installation of RWKV-7 and tokenizer
• ✅ Inference test pipeline created
• ✅ Diagnostic script with error tracing implemented
• ✅ MPICH integrated for inter-node comms
• ✅ Web UI design completed
• ✅ RWKV-65M deployed as fallback on each node

Upcoming/Planned

• Final integration of persistent memory graph
• RWKV agent mutation ruleset (drift logic)
• Multi-agent coordination and specialization engine
• Dynamic RPC/ZeroMQ communication layer
• Connection of model logs to training behaviors