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3 months ago
1
Auto‑generate synthetic training data, fit an ultra‑compact model, and deploy anywhere—from microcontrollers to mobile apps—with a single command.
docker run --rm \ docker run --rm -v "$(pwd)":/app/models -e
-v "$(pwd)":/app/models \ OPEN_ROUTER_API_KEY="YOUR_KEY_HERE"
-e OPEN_ROUTER_API_KEY="sk-..." \ ghcr.io/inoxoft/whitelightning:latest
ghcr.io/inoxoft/whitelightning:latest \ python -m text_classifier.agent
python -m text_classifier.agent \ -p "Classify customer reviews
-p "Categorize customer reviews as positive, neutral, or negative" as positive or negative sentiment"
Copy:
🧠 - INFO - Generate Edge Cases: True (Target Volume per class: 5)
🧠 - INFO - Successfully generated initial configuration.
🧠 - INFO - DataGenerator initialized. Config Model: 'x-ai/grok-3-beta', Model: 'openai/gpt-4o-mini'
🧠 - INFO - Prompt Refinement Cycles: 1 🧠 Created 1000 examples
🧠 - INFO - Generate Edge Cases: True (Target Volume per class: 50) 🧠 Generate Edge Cases...
📦 - INFO - === Starting Data Generation & Model Training Process === 📦 Starting training process...
📦 - INFO - Classification type: multiclass 📦 Type: multiclass
📦 - INFO - Class labels: ['negative', 'neutral', 'positive'] (Count: 3) 📦 Labels: negative, neutral, positive
📦 - INFO - --- Edge Case Generation Finished --- 📦 Edge cases generated
⚙️ - INFO - Starting model training using tensorflow strategy... ⚙️ Training started
📈 ━━━━━━━━━━━━━━━━━━━━ Epoch 1/20 - accuracy: 0.4164 - loss: 0.6194 📈 Epoch 1/20 - accuracy: 0.4164
📈 ━━━━━━━━━━━━━━━━━━━━ Epoch 20/20 - accuracy: 1.0000 - loss: 5.3911e-05 📈 Epoch 20/20 - accuracy: 1.0000
✅ - INFO - Test set evaluation - Loss: 0.0006, Accuracy: 1.0000 ✅ Test accuracy: 1.0000
📤 - INFO - Model exported to ONNX: 📤 Model exported
models_multiclass/customer_review_classifier/customer_review_classifier.onnx customer_review_classifier.onnx
⚡ - INFO - === Data Generation & Model Training Process Finished. Duration: 0:10:42 === ⚡ Process completed in 10:42
