Update MODEL_CARD.md

MODEL_CARD.md

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-# 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}
-}
+---
+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}
+}
 ```