---
license: apache-2.0
base_model: Qwen/Qwen3.5-0.8B
library_name: transformers
tags:
  - qwen3.5
  - unsloth
  - qat
  - edge
  - sft
  - int8-int4
---

# 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`**.