Nebulixlabs/Quantum-Classifier-Calibrated

Quantum-Clasifier-Finetuned

Fine-tuned version of:

Nebulixlabs/Quantum-Classifier

Architecture

• —Parameters: 1,000,000
• —Vocabulary: 4096
• —Context length: 128
• —Hidden size: 112
• —Attention heads: 7
• —Transformer layers: 4
• —FFN: 336
• —Classifier hidden: 170

Fine-tuning

Datasets:

• —Nebulixlabs/safety-dataset
• —KimDongH/spam_dataset-train-eval-test2

Batch size:

128

Target tokens:

30,000,000

Actual tokens:

30,010,756

Epochs:

9

Labels

0 = safe

1 = unsafe

Confidence

Temperature calibrated on validation data:

1.479935

Evaluation

The held-out test split, where available, is evaluated only after fine-tuning.

Multilingual smoke tests cover:

• —English
• —Hindi
• —Hinglish