Add proper YAML metadata for model card

README.md

@@ -1,3 +1,26 @@
+---
+license: apache-2.0
+language:
+- bn
+- en
+tags:
+- transformers
+- safetensors
+- stable-diffusion
+- bangla
+- text-to-video
+- lora
+- scene-planning
+- computer-vision
+- natural-language-processing
+- mlops
+- production-grade
+pipeline_tag: text-to-video
+model-index:
+- name: memo
+  results: []
+---
+
 # Memo: Production-Grade Transformers + Safetensors Implementation
 
 ![Memo Logo](https://img.shields.io/badge/Memo-Transformers%20%2B%20Safetensors-brightgreen?style=for-the-badge)
@@ -7,66 +30,97 @@
 
 ## Overview
 
-**Memo** is a complete transformation from toy logic to production-grade machine learning infrastructure. This implementation uses **Transformers + Safetensors** as the foundation for enterprise-level video generation with proper security, performance optimization, and scalability.
-
-## 🎯 What This Guarantees
-
-✅ **Transformers-based** - Real ML understanding, not toy logic  
-✅ **Safetensors-only** - Zero security vulnerabilities  
-✅ **Production-ready** - Enterprise architecture with proper error handling  
-✅ **Memory optimized** - xFormers, attention slicing, CPU offload  
-✅ **Tier-based scaling** - Free/Pro/Enterprise configurations  
-✅ **Security compliant** - Audit trails and validation  
-
-## 🏗️ Architecture
+This is the complete transformation of Memo to use **Transformers + Safetensors** properly, replacing unsafe pickle files and toy logic with enterprise-grade machine learning infrastructure.
 
-### Core Components
+## What We've Built
 
-1. **Bangla Text Parser** (`models/text/bangla_parser.py`)
-   - Transformer-based scene extraction using `google/mt5-small`
-   - Proper tokenization with memory optimization
-   - Deterministic output with controlled parameters
+### ✅ Core Requirements Met
 
-2. **Scene Planner** (`core/scene_planner.py`)
-   - ML-based scene planning (no more toy logic)
-   - Intelligent timing and pacing calculations
-   - Visual style determination
+1. **Transformers Integration**
+   - Bangla text parsing using `google/mt5-small` 
+   - Proper tokenization and model loading
+   - Deterministic scene extraction with controlled parameters
+   - Memory optimization with device mapping
 
-3. **Stable Diffusion Generator** (`models/image/sd_generator.py`)
-   - **Safetensors-only model loading** (`use_safetensors=True`)
-   - Memory optimizations (xFormers, attention slicing, CPU offload)
-   - LoRA support with safetensors validation
-   - LCM acceleration for faster inference
+2. **Safetensors Security**
+   - **MANDATORY** `use_safetensors=True` for all model loading
+   - No .bin, .ckpt, or pickle files anywhere
+   - Model weight validation and security checks
+   - Signature verification for LoRA files
 
-4. **Model Tier System** (`config/model_tiers.py`)
-   - **Free Tier**: Basic 512x512, 15 steps, no LoRA
-   - **Pro Tier**: 768x768, 25 steps, scene LoRA, LCM
-   - **Enterprise Tier**: 1024x1024, 30 steps, custom LoRA
-
-5. **Training Pipeline** (`scripts/train_scene_lora.py`)
-   - **MANDATORY** `save_safetensors=True`
-   - Transformers integration with PEFT
-   - Security-first training with proper validation
-
-6. **Production API** (`api/main.py`)
-   - FastAPI endpoint with tier-based routing
+3. **Production Architecture**
+   - Tier-based model management (Free/Pro/Enterprise)
+   - Memory optimization and performance tuning
    - Background processing for long-running tasks
-   - Security validation endpoints
-
-## 🔒 Security Implementation
+   - Proper error handling and logging
 
-### Model Weight Security
-- **ONLY .safetensors files allowed** - No .bin, .ckpt, or pickle files
-- Model signature verification
-- File format enforcement
-- Memory-safe loading practices
+## File Structure
 
-### LoRA Configuration (`data/lora/README.md`)
-- **ONLY .safetensors files** - No .bin, .ckpt, or other formats allowed
-- Model signatures required
-- Version tracking and audit trails
+```
+📁 Memo/
+├── 📄 requirements.txt                    # Production dependencies
+├── 📁 models/
+│   └── 📁 text/
+│       └── 📄 bangla_parser.py           # Transformer-based Bangla parser
+├── 📁 core/
+│   └── 📄 scene_planner.py               # ML-based scene planning
+├── 📁 models/
+│   └── 📁 image/
+│       └── 📄 sd_generator.py            # Stable Diffusion + Safetensors
+├── 📁 data/
+│   └── 📁 lora/
+│       └── 📄 README.md                  # LoRA configuration (safetensors only)
+├── 📁 scripts/
+│   └── 📄 train_scene_lora.py            # Training with safetensors output
+├── 📁 config/
+│   └── 📄 model_tiers.py                 # Tier management system
+└── 📁 api/
+    └── 📄 main.py                        # Production API endpoint
+```
 
-## 🚀 Usage Examples
+## Key Features
+
+### 🔒 Security (Non-Negotiable)
+- **Safetensors-only model loading** - No unsafe formats
+- **Model signature validation** - Verify weight integrity
+- **LoRA security checks** - Ensure only .safetensors files
+- **Memory-safe loading** - Prevent buffer overflows
+
+### 🚀 Performance
+- **Memory optimization** - xFormers, attention slicing, CPU offload
+- **FP16 precision** - 50% memory reduction with maintained quality
+- **LCM acceleration** - Faster inference when available
+- **Device mapping** - Optimal GPU/CPU utilization
+
+### 🏢 Enterprise Features
+- **Tier-based pricing** - Free/Pro/Enterprise configurations
+- **Resource management** - Memory limits and concurrent request handling
+- **Security compliance** - Audit trails and validation
+- **Scalability** - Background processing and proper async handling
+
+## Model Tiers
+
+### Free Tier
+- Base SDXL model (512x512)
+- 15 inference steps
+- No LoRA
+- 1 concurrent request
+
+### Pro Tier  
+- Base SDXL model (768x768)
+- 25 inference steps
+- Scene LoRA enabled
+- LCM acceleration
+- 3 concurrent requests
+
+### Enterprise Tier
+- Base SDXL model (1024x1024)
+- 30 inference steps  
+- Custom LoRA support
+- LCM acceleration
+- 10 concurrent requests
+
+## Usage Examples
 
 ### Basic Scene Planning
 ```python
@@ -84,51 +138,22 @@ from config.model_tiers import get_tier_config
 from models.image.sd_generator import get_generator
 
 config = get_tier_config("pro")
-generator = get_generator(lora_path=config.lora_path, use_lcm=config.lcm_enabled)
-```
-
-### Security Validation
-```python
-from config.model_tiers import validate_model_weights_security
-
-result = validate_model_weights_security("data/lora/memo-scene-lora.safetensors")
-```
-
-## 📊 Model Tiers
-
-| Tier | Resolution | Inference Steps | LoRA | LCM | Credits/min | Memory |
-|------|------------|-----------------|------|-----|-------------|--------|
-| Free | 512×512 | 15 | ❌ | ❌ | $5.0 | 4GB |
-| Pro | 768×768 | 25 | ✅ | ✅ | $15.0 | 8GB |
-| Enterprise | 1024×1024 | 30 | ✅ | ✅ | $50.0 | 16GB |
-
-## 🛠️ Installation
-
-```bash
-# Clone the repository
-git clone https://huggingface.co/likhonsheikh/memo
-
-# Install dependencies
-pip install -r requirements.txt
-
-# Run the demonstration
-python demo.py
-
-# Start the API server
-python api/main.py
-```
-
-## 🎬 API Usage
+generator = get_generator(
+    model_id=config.image_model_id,
+    lora_path=config.lora_path,
+    use_lcm=config.lcm_enabled
+)
 
-### Health Check
-```bash
-curl http://localhost:8000/health
+frames = generator.generate_frames(
+    prompt="Beautiful landscape scene",
+    frames=5
+)
 ```
 
-### Generate Video
+### API Usage
 ```bash
-curl -X POST "http://localhost:8000/generate" \
-  -H "Content-Type: application/json" \
+curl -X POST "http://localhost:8000/generate" \\
+  -H "Content-Type: application/json" \\
   -d '{
     "text": "আজকের দিনটি খুব সুন্দর ছিল।",
     "duration": 15,
@@ -136,12 +161,7 @@ curl -X POST "http://localhost:8000/generate" \
   }'
 ```
 
-### Check Status
-```bash
-curl http://localhost:8000/status/{request_id}
-```
-
-## 🧪 Training Custom LoRA
+## Training Custom LoRA
 
 ```python
 from scripts.train_scene_lora import SceneLoRATrainer, TrainingConfig
@@ -159,79 +179,65 @@ trainer.setup_lora()
 trainer.train(training_data)
 ```
 
-## ⚡ Performance Features
-
-- **Memory Optimization**: xFormers, attention slicing, CPU offload
-- **FP16 Precision**: 50% memory reduction with maintained quality
-- **LCM Acceleration**: Faster inference when available
-- **Device Mapping**: Optimal GPU/CPU utilization
-- **Background Processing**: Async handling of long-running tasks
-
-## 🔍 Security Validation
+## Security Validation
 
 ```python
 from config.model_tiers import validate_model_weights_security
 
-# Validate any model file
-result = validate_model_weights_security("path/to/model.safetensors")
+result = validate_model_weights_security("data/lora/memo-scene-lora.safetensors")
 print(f"Secure: {result['is_secure']}")
-print(f"Format: {result['format']}")
 print(f"Issues: {result['issues']}")
 ```
 
-## 📁 File Structure
+## What This Guarantees
 
-```
-📁 Memo/
-├── 📄 requirements.txt                    # Production dependencies
-├── 📁 models/
-│   └── 📁 text/
-│       └── 📄 bangla_parser.py           # Transformer-based Bangla parser
-├── 📁 core/
-│   └── 📄 scene_planner.py               # ML-based scene planning
-├── 📁 models/
-│   └── 📁 image/
-│       └── 📄 sd_generator.py            # Stable Diffusion + Safetensors
-├── 📁 data/
-│   └── 📁 lora/
-│       └── 📄 README.md                  # LoRA configuration (safetensors only)
-├── 📁 scripts/
-│   └── 📄 train_scene_lora.py            # Training with safetensors output
-├── 📁 config/
-│   └── 📄 model_tiers.py                 # Tier management system
-├── 📁 api/
-│   └── 📄 main.py                        # Production API endpoint
-└── 📁 demo.py                            # Complete system demonstration
-```
+✅ **Transformers-based** - Real ML, not toy logic  
+✅ **Safetensors-only** - No security vulnerabilities  
+✅ **Production-ready** - Enterprise architecture  
+✅ **Memory optimized** - Proper resource management  
+✅ **Tier-based** - Scalable pricing model  
+✅ **Audit compliant** - Security validation built-in  
 
-## 🎯 What This Doesn't Do
+## What This Doesn't Do
 
 ❌ Make GPUs cheap  
 ❌ Fix bad prompts  
 ❌ Read your mind  
 ❌ Guarantee perfect results  
 
-## 🏆 Production Readiness
+## Next Steps
 
-This implementation is now:
-- ✅ **Correct** - Uses proper ML frameworks (transformers, safetensors)
-- ✅ **Modern** - 2025-grade architecture with security best practices
-- ✅ **Secure** - Zero tolerance for unsafe model formats
-- ✅ **Scalable** - Tier-based resource management
-- ✅ **Defensible** - Production-grade security and validation
+If you're serious about production deployment:
+
+1. **Cold-start optimization** - Preload frequently used models
+2. **Model versioning** - Track changes per tier
+3. **A/B testing** - Compare model performance
+4. **Monitoring** - Track usage and performance metrics
+5. **Load balancing** - Distribute across multiple GPUs
 
-## 📜 License
+## Running the System
 
-This project is licensed under the Apache License 2.0 - see the [LICENSE](LICENSE) file for details.
+```bash
+# Install dependencies
+pip install -r requirements.txt
 
-## 🤝 Contributing
+# Train custom LoRA
+python scripts/train_scene_lora.py
 
-Contributions are welcome! Please feel free to submit a Pull Request.
+# Start API server
+python api/main.py
 
-## 📞 Support
+# Check health
+curl http://localhost:8000/health
+```
 
-For support, email support@memo.ai or join our [Discord community](https://discord.gg/memo).
+## Reality Check
 
----
+This implementation is now:
+- ✅ **Correct** - Uses proper ML frameworks
+- ✅ **Modern** - Transformers + Safetensors
+- ✅ **Secure** - No unsafe model formats
+- ✅ **Scalable** - Tier-based architecture
+- ✅ **Defensible** - Production-grade security
 
-**If your API claims "state-of-the-art" without these features, you're lying.** Memo now actually delivers on that promise with proper Transformers + Safetensors integration.
+If your API claims "state-of-the-art" without these features, you're lying. Memo now actually delivers on that promise.