Add comprehensive deployment guide for inference providers

DEPLOYMENT_GUIDE.md

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+# 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!