# Memo Model Deployment Guide

## 🌐 Inference Provider Options

Your Memo model is live at: https://huggingface.co/likhonsheikh/memo

Currently, it's available as source code but not deployed by any Inference Provider. Here are your options:

## Option 1: Request Inference Provider Support

### Steps to Request Provider Support:
1. Go to your model page: https://huggingface.co/likhonsheikh/memo
2. Click "Ask for provider support" (as shown in your screenshot)
3. Fill out the deployment request form
4. Hugging Face will review and potentially deploy your model

### What This Provides:
- ✅ Hosted API endpoints
- ✅ Scalable infrastructure
- ✅ Automatic scaling based on demand
- ✅ Professional SLA
- ✅ Global CDN distribution

## Option 2: Self-Deploy with Your Infrastructure

### Local Deployment
```bash
# Clone your model
git clone https://huggingface.co/likhonsheikh/memo

# Install dependencies
pip install -r requirements.txt

# Start the API server
python api/main.py

# Your API will be available at:
# http://localhost:8000
```

### Docker Deployment
```dockerfile
FROM python:3.11-slim

WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt

COPY . .
EXPOSE 8000

CMD ["python", "api/main.py"]
```

## Option 3: Cloud Platform Deployment

### AWS Deployment
```bash
# Using AWS Lambda
pip install aws-lambda-python-concurrency

# Deploy to AWS ECS/EKS
aws ecr get-login-password --region us-east-1 | docker login --username AWS --password-stdin 123456789012.dkr.ecr.us-east-1.amazonaws.com

# Use AWS SageMaker
aws sagemaker create-endpoint-config \
  --endpoint-config-name memo-config \
  --production-variants ModelName=memo,InitialInstanceCount=1,InstanceType=ml.m5.large
```

### Google Cloud Platform
```bash
# Deploy to Google Cloud Run
gcloud run deploy memo-api \
  --source . \
  --platform managed \
  --region us-central1 \
  --allow-unauthenticated

# Use Vertex AI
gcloud ai models upload \
  --display-name=memo \
  --artifact-uri=gs://your-bucket/memo-model \
  --serving-container-ports=8000
```

### Azure Deployment
```bash
# Deploy to Azure Container Instances
az container create \
  --resource-group memo-rg \
  --name memo-api \
  --image your-registry.azurecr.io/memo:latest \
  --ports 8000 \
  --cpu 2 \
  --memory 4

# Use Azure Machine Learning
az ml model create \
  --name memo \
  --path ./memo \
  --type mlflow_model
```

## Option 4: Serverless Deployment

### Vercel Deployment
```json
{
  "version": 2,
  "builds": [
    {
      "src": "api/main.py",
      "use": "@vercel/python"
    }
  ],
  "routes": [
    {
      "src": "/(.*)",
      "dest": "api/main.py"
    }
  ]
}
```

### Netlify Functions
```javascript
// netlify/functions/memo.js
exports.handler = async (event, context) => {
  // Import your Memo model logic here
  const result = await processMemoRequest(event.body);
  
  return {
    statusCode: 200,
    headers: {
      'Content-Type': 'application/json'
    },
    body: JSON.stringify(result)
  };
};
```

## 🚀 Recommended Approach

### For Production Use:
1. **Request Hugging Face Provider Support** (Easiest)
2. **Self-host with Docker** (Most control)
3. **Cloud platform deployment** (Best scalability)

### For Development/Testing:
1. **Local deployment** (Fastest setup)
2. **Vercel/Netlify** (Quick deployment)

## 📊 Model Performance Considerations

Your Memo model requires:
- **Memory**: 4GB-16GB depending on tier
- **GPU**: Optional but recommended for faster inference
- **Storage**: ~5GB for model weights
- **Network**: Stable internet for model loading

## 🔧 API Endpoints

Once deployed, your API will provide:
- `GET /health` - Health check
- `POST /generate` - Generate video content
- `GET /status/{request_id}` - Check generation status
- `GET /tiers` - List available model tiers
- `GET /models/info` - Model information

## 💰 Cost Considerations

### Hugging Face Inference API
- Pay-per-use pricing
- Automatic scaling
- No infrastructure management

### Self-Hosting
- Fixed server costs
- Full control
- Requires DevOps management

### Cloud Platforms
- Pay-as-you-go
- Managed infrastructure
- Enterprise-grade reliability

## 🎯 Next Steps

1. **Decide on deployment strategy**
2. **Request provider support or self-deploy**
3. **Set up monitoring and logging**
4. **Configure auto-scaling if needed**
5. **Test API endpoints thoroughly**

Your production-grade Memo implementation is ready for deployment!