Memo: Production-Grade Transformers + Safetensors Implementation

Overview

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.

What We've Built

✅ Core Requirements Met

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

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

3. Production Architecture

   • Tier-based model management (Free/Pro/Enterprise)
   • Memory optimization and performance tuning
   • Background processing for long-running tasks
   • Proper error handling and logging

File Structure

📁 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

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

from core.scene_planner import plan_scenes

scenes = plan_scenes(
    text_bn="আজকের দিনটি খুব সুন্দর ছিল।",
    duration=15
)

Tier-Based Generation

from config.model_tiers import get_tier_config
from models.image.sd_generator import get_generator

config = get_tier_config("pro")
generator = get_generator(
    model_id=config.image_model_id,
    lora_path=config.lora_path,
    use_lcm=config.lcm_enabled
)

frames = generator.generate_frames(
    prompt="Beautiful landscape scene",
    frames=5
)

API Usage

curl -X POST "http://localhost:8000/generate" \\
  -H "Content-Type: application/json" \\
  -d '{
    "text": "আজকের দিনটি খুব সুন্দর ছিল।",
    "duration": 15,
    "tier": "pro"
  }'

Training Custom LoRA

from scripts.train_scene_lora import SceneLoRATrainer, TrainingConfig

config = TrainingConfig(
    base_model="google/mt5-small",
    rank=32,
    alpha=64,
    save_safetensors=True  # MANDATORY
)

trainer = SceneLoRATrainer(config)
trainer.load_model()
trainer.setup_lora()
trainer.train(training_data)

Security Validation

from config.model_tiers import validate_model_weights_security

result = validate_model_weights_security("data/lora/memo-scene-lora.safetensors")
print(f"Secure: {result['is_secure']}")
print(f"Issues: {result['issues']}")

What This Guarantees

✅ 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

❌ Make GPUs cheap
❌ Fix bad prompts
❌ Read your mind
❌ Guarantee perfect results

Next Steps

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

Running the System

# Install dependencies
pip install -r requirements.txt

# Train custom LoRA
python scripts/train_scene_lora.py

# Start API server
python api/main.py

# Check health
curl http://localhost:8000/health

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.