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.
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.
-
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.
-
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.
-
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.
-
Orchestrator:
- Central control system on
Worlock
using Ray for distributed workload management. - Assign tasks to other nodes based on load and capability.
- Central control system on
-
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.
- Mixed Precision Training (FP16/FP32):
- Use AMP (Automatic Mixed Precision) for faster training and lower memory usage.
- Model Pruning:
- Reduce model size by removing redundant weights without significantly impacting performance.
- Offloading and Overlap:
- Use Deepspeed ZeRO-Offload to manage memory between CPU/GPU for massive models.
- Cache Optimization:
- Use NVMe disks for temporary caching of model weights and datasets.
- Task Prioritization:
- Inferencing tasks prioritized on nodes with higher VRAM GPUs (e.g., Calisota).
- CPU-heavy tasks offloaded to Spinoza.
- Load Balancing:
- Monitor system usage using tools like Prometheus and redistribute tasks dynamically.
-
Query Handling:
- User queries are routed to the orchestrator (Worlock).
- Queries are categorized and routed to specialized models/nodes.
-
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.
-
Human Oversight:
- Summarized outputs reviewed by operators periodically to ensure alignment with goals.
- Self-Improving Behavior:
- Use reinforcement learning and fine-tuning.
- Swarm Coordination:
- Nodes share load and specialize dynamically.
- Minimized Exponential Costs:
- Use pre-trained models and modular updates.
- Software Construction Support:
- Train models specifically on code datasets (e.g., CodeT5, Codex) to assist in generating software solutions.
-
Install Software
- Frameworks:
PyTorch
,Hugging Face
,Ray
,Deepspeed
,LoRA
,OLLAMA
. - Messaging:
gRPC
orZeroMQ
.
- Frameworks:
-
Pretrained Models
- Download lightweight models for immediate use.
- Fine-tune or train on local datasets for specialized tasks.
-
Distributed Setup
- Install and configure a distributed workload manager on all nodes.
- Assign node-specific roles based on hardware.
-
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.