Skip to content

Files

Latest commit

4942b79 · Nov 24, 2024

History

History

nov22

Folders and files

NameName
Last commit message
Last commit date

parent directory

..
Nov 24, 2024
Nov 24, 2024
Loading
graph TD
    A[Worlock: Orchestrator] --> B[Spinoza: Preprocessing & Lightweight Inferencing]
    A --> C[Calisota: High-Performance Inferencing & Embedding Generation]
    A --> D[Distributed Messaging System Ray/ZeroMQ/gRPC]
    B --> E[Text Preprocessing]
    B --> F[Model Evaluation]
    C --> G[Inferencing on Complex Queries]
    C --> H[Vector Embedding & Clustering]
    D --> I[Task Assignment Based on Node Load]
    A --> J[Centralized Feedback Collection]
    J --> K[Reinforcement Learning Loop RLHF]
    K --> L[Model Fine-Tuning LoRA/Deepspeed]
    L --> M[Optimized Models Deployed]
    G --> N[Lightweight Models Deployed for Routine Tasks]
    H --> O[Embedding Updates for Clustering]
    O --> P[Re-cluster Topics Periodically]
    subgraph "Hardware Utilization"
        A1[Worlock GPUs: RTX 3080/3060]
        B1[Spinoza CPU/GPU: AMD Grayskull]
        C1[Calisota GPU: RTX 4080 SUPER]
        A1 --> A
        B1 --> B
        C1 --> C
    end
    subgraph "Optimization Techniques"
        Q[Mixed Precision FP16/FP32]
        R[Model Pruning]
        S[Cache Optimization on NVMe]
        T[Deepspeed Offloading]
        Q --> L
        R --> L
        S --> D
        T --> L
    end

To build a minimally viable, self-improving or self-optimizing language model system across your distributed, GPU-accelerated hardware setup, the following approach could be pursued. This setup focuses on scalability, efficient utilization of available resources, and self-optimization without exponential growth in resource requirements.


Plan: Language Model Swarm with Local Resource Optimization

1. Framework Selection

Use open-source frameworks like PyTorch or TensorFlow for flexibility and compatibility. Integrate tools such as:

  • LoRA (Low-Rank Adaptation): For fine-tuning large language models without the need for full model retraining.
  • Hugging Face Transformers: To leverage pre-trained models for fast prototyping.
  • OLLAMA or Similar: For a lightweight, interactive interface to run optimized language models.

2. Architecture: Distributed Model Swarm

Core Components

  1. Base Models: Deploy lightweight models fine-tuned for specific tasks.

    • Use smaller models (e.g., GPT-2/3-lite, T5-small) for routine tasks.
    • Reserve large-scale models for high-complexity tasks.
  2. Agent-based Swarm:

    • Each node (machine) operates as an autonomous agent with specific roles.
    • Use a distributed messaging system (e.g., Ray, ZeroMQ, or gRPC) to coordinate tasks among nodes.
  3. Task Specialization:

    • Worlock: Main compute-intensive tasks (fine-tuning, large-scale inferencing).
    • Spinoza: Text pre-processing, lightweight inferencing, and evaluation.
    • Calisota: High-performance inferencing, vector embedding generation, clustering.

Distributed System for Self-Optimization

  1. Orchestrator:

    • Central control system on Worlock using Ray for distributed workload management.
    • Assign tasks to other nodes based on load and capability.
  2. Model Improvement Cycle:

    • Data Collection: Aggregate user queries and outputs.
    • Feedback Loop: Use reinforcement learning (e.g., RLHF - Reinforcement Learning with Human Feedback) to refine models.
    • Fine-tuning: Distributed fine-tuning using lightweight LoRA-based updates.

3. Optimization Techniques

Resource Utilization

  1. Mixed Precision Training (FP16/FP32):
    • Use AMP (Automatic Mixed Precision) for faster training and lower memory usage.
  2. Model Pruning:
    • Reduce model size by removing redundant weights without significantly impacting performance.
  3. Offloading and Overlap:
    • Use Deepspeed ZeRO-Offload to manage memory between CPU/GPU for massive models.
  4. Cache Optimization:
    • Use NVMe disks for temporary caching of model weights and datasets.

Task Scheduling

  1. Task Prioritization:
    • Inferencing tasks prioritized on nodes with higher VRAM GPUs (e.g., Calisota).
    • CPU-heavy tasks offloaded to Spinoza.
  2. Load Balancing:
    • Monitor system usage using tools like Prometheus and redistribute tasks dynamically.

4. Language Model Swarm Workflow

  1. Query Handling:

    • User queries are routed to the orchestrator (Worlock).
    • Queries are categorized and routed to specialized models/nodes.
  2. Self-Improvement:

    • Feedback from users stored in a database.
    • Periodic updates:
      • Fine-tune models with new data.
      • Re-cluster embeddings to reflect new topics.
    • Automatically monitor performance metrics to determine when to update models.
  3. Human Oversight:

    • Summarized outputs reviewed by operators periodically to ensure alignment with goals.

5. Key Features

  1. Self-Improving Behavior:
    • Use reinforcement learning and fine-tuning.
  2. Swarm Coordination:
    • Nodes share load and specialize dynamically.
  3. Minimized Exponential Costs:
    • Use pre-trained models and modular updates.
  4. Software Construction Support:
    • Train models specifically on code datasets (e.g., CodeT5, Codex) to assist in generating software solutions.

6. Deployment Steps

  1. Install Software

    • Frameworks: PyTorch, Hugging Face, Ray, Deepspeed, LoRA, OLLAMA.
    • Messaging: gRPC or ZeroMQ.
  2. Pretrained Models

    • Download lightweight models for immediate use.
    • Fine-tune or train on local datasets for specialized tasks.
  3. Distributed Setup

    • Install and configure a distributed workload manager on all nodes.
    • Assign node-specific roles based on hardware.
  4. Monitor and Iterate

    • Use tools like TensorBoard, Prometheus, and Grafana for system monitoring.
    • Regularly refine models based on feedback and performance metrics.

This system leverages your high-end GPUs and distributed nodes effectively, achieving scalability and adaptability while minimizing costs. For software construction, you could further specialize the swarm by training one or more models on open-source code repositories or local datasets.