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](https://huggingface.co/OpenPeerAI/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**
   ```bash
   pip install -r requirements.txt
   ```

2. **Configuration**
   - Copy `.env.example` to `.env`
   - Configure CouchDB connection
   - Set desired training parameters

3. **Launch Training**
   ```bash
   python -m cloud_agents.cli train --num-epochs 3 --steps-per-epoch 100
   ```

4. **Monitor Progress**
   ```bash
   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:

```bibtex
@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}
}
```