MODEL_CARD.md

---

language: en license: mit library_name: openpeerllm tags:

• distributed-training
• cloud-computing
• language-model
• grid-computing
• openpeerllm datasets:
• OpenPeerAI/OpenPeerLLM pipeline_tag: distributed-training mask: sequential

Model Card: Cloud Agents for OpenPeerLLM

Model Details

• Model Type: Distributed Training System for Language Models
• Primary Purpose: Training Large Language Models in a distributed environment
• Framework: PyTorch with Ray
• Target Model: OpenPeerLLM
• License: MIT

Intended Use

Primary Use

• Distributed training of large language models
• Grid computing/distributed computing-based learning for tensors
• Horizontal scaling of model training infrastructure

Out-of-Scope Uses

• Production deployment of models
• Single-machine training
• Real-time inference

System Architecture

Components

1. Distributed Agents

   • Lightweight worker nodes for distributed computing
   • Automatic scaling based on workload
   • Built-in fault tolerance and recovery

2. CouchDB Coordination Layer

   • Job distribution and management
   • State synchronization
   • Agent discovery and registration

3. Tensor Operations

   • Distributed gradient computation
   • Efficient parameter updates
   • Gradient averaging and clipping

4. Training Orchestration

   • Automated model checkpoint management
   • Dynamic load balancing
   • Progress monitoring and reporting

Performance

Scaling Characteristics

• Minimum Agents: 2
• Maximum Agents: 10 (configurable)
• Scale-up Threshold: 80% utilization
• Scale-down Threshold: 30% utilization
• Auto-scaling: Yes, based on workload

Resource Requirements

• Per Agent:
  • CPU: 1 core minimum
  • GPU: Optional, supports fractional GPU allocation
  • Memory: Varies based on model size
  • Network: Reliable connection to CouchDB and other agents

Limitations

1. Network Dependency

   • Requires stable network connectivity between agents
   • CouchDB must be accessible to all agents

2. Scaling Limits

   • Upper bound on number of concurrent agents
   • Network latency can impact synchronization

3. Resource Management

   • Requires careful monitoring of resource utilization
   • GPU memory management crucial for large models

Training Details

Training Data

• Uses the same training data as OpenPeerLLM
• Supports distributed batch processing
• Configurable gradient accumulation steps

Training Procedure

1. Initialization

   • Model weights loaded from HuggingFace hub
   • Agents register with coordinator
   • Initial state distributed to all agents

2. Training Loop

   • Distributed gradient computation
   • Synchronized parameter updates
   • Regular checkpointing
   • Automatic agent scaling

Hyperparameters

Configurable through environment variables:

• Batch size
• Gradient accumulation steps
• Number of epochs
• Learning rate
• Scaling thresholds

Getting Started

1. Installation

   pip install -r requirements.txt
   

2. Configuration

   • Copy .env.example to .env
   • Configure CouchDB connection
   • Set desired training parameters

3. Launch Training

   python -m cloud_agents.cli train --num-epochs 3 --steps-per-epoch 100
   

4. Monitor Progress

   python -m cloud_agents.cli status
   

Ethical Considerations

• Resource efficiency through intelligent scaling
• Environmental impact minimization via workload-based scaling
• Distributed approach reduces single-point-of-failure risks

Maintenance

This system is maintained as an open-source project. Users are encouraged to:

• Report issues and bugs
• Suggest improvements
• Contribute to the codebase
• Share performance metrics and optimization strategies

Citation

If you use this system in your research, please cite:

@software{cloud_agents_2025,
  title = {Cloud Agents: Distributed Training System for OpenPeerLLM},
  year = {2025},
  author = {Andrew Magdy Kamal},
  url = {hhttps://huggingface.co/OpenPeerAI/Cloud-Agents},
  note = {Distributed computing framework for training large language models}
}