PRATYUSH-BHARDWAJ/Cortex_A_0.5

Cortex A 0.5

General-purpose edge checkpoint: Qwen3.5-0.8B full SFT with Unsloth int8-int4 QAT (4-bit weights + 8-bit dynamic activations). Target inference footprint ≈ 450MB including the vision tower.

This repo stores:

• checkpoint-* — resumable Trainer states (optimizer + fake-quant QAT model)
• training/live_metrics.json — loss, MTP loss, ppl, val loss/ppl, tok/s, grad norm, lr
• training/RESUME_POINTER.json — last step for the next 12h Kaggle session
• qat_converted/ — real 4-bit TorchAO export (only after a completed epoch run)

Training hardware: Kaggle 2× Tesla T4, hard stop 11.5h, DDP via torchrun. QAT scheme: int8-int4.