# Phase Two — Checkpoint Integrity & Strict-Load Audit Report

## 1. Forensic Inspection of `qtensorformer_small.pt`

Data source: `results/raw/checkpoint_integrity.json` (`MEASURED`).

- **File Path:** `qtensorformer_small.pt`
- **File Size:** `5,432,305` bytes (`5.18 MB`)
- **SHA-256 Digest:** `9a4e61f70ba084e6c4e38b3e7cc8479d1f2ff43c51fff7bcf2ee07191dc3b0db`
- **Git Provenance:** Committed in `82fd973c` (`May 6, 2026`) from an external container (`/app/qtensorformer`).
- **Top-Level Dictionary Keys:** `['model_state_dict', 'config', 'params', 'baseline_params', 'compression_ratio', 'version']`
- **Recorded Metadata:**
  - `version`: `"1.0.0"`
  - `params`: `1,347,747`
  - `baseline_params`: `16,146,432`
  - `compression_ratio`: `11.9803`
- **Actual State-Dict Structure:**
  - Contains `113` tensors summing to exactly `1,347,747` elements.
  - Sample keys:
    - `embeddings.token_embedding.weight` (`[10000, 128]`)
    - `embeddings.position_embedding.weight` (`[64, 128]`)
    - `embeddings.quantum_enhancer.qlayer.weights` (`[2, 4]`)
    - `blocks.0.attention.q_proj.weight` (`[128, 128]`)
    - `blocks.0.ffn.layer.weight.factors.factor_0` .. `factor_5` (TensorLy `FactorizedTensor` format)
    - `blocks.0.ffn.rank_scheduler.net.0.weight`

---

## 2. Canonical `strict=True` Loading Test Results

Per Phase Two rules, `model.load_state_dict(sd, strict=True)` was executed against the repository's canonical implementations without altering checkpoint tensors or model dimensions:

| Target Model Class | Config Class | Model Params | Model Keys | Checkpoint Keys | Exact Key Overlap | Missing Keys | Unexpected Keys | `strict=True` Result |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| `src.models.QTensorFormer` | `src.config.ModelConfig` | `1,524,186` | `185` | `113` | **`0` (0.0%)** | `185` | `113` | **`FAIL`** (`RuntimeError`) |
| `modeling_qtensorformer.QTensorFormerForCausalLM` | `QTensorFormerConfig` | `2,767,296` | `52` | `113` | **`0` (0.0%)** | `52` | `113` | **`FAIL`** (`RuntimeError`) |

### Why `qtensorformer_small.pt` Cannot Load Under `strict=True`
1. **Incompatible Tensor Factorization Library:** `qtensorformer_small.pt` stores FFN weights using `tensorly` / `tltorch` 6-core `factors.factor_0..5` naming on a single fused FFN layer (`blocks.i.ffn.layer`), whereas `src.tensor_layers.TTFeedForward` uses two 4-core pure-PyTorch `TTLinear` projections (`blocks.i.tt_ffn.up_proj.cores.0..3` and `blocks.i.tt_ffn.down_proj.cores.0..3`).
2. **Different Module Hierarchy:** `qtensorformer_small.pt` uses `embeddings.token_embedding` and `blocks.i.ffn.rank_scheduler.net`, whereas `src.models.QTensorFormer` uses `embedding.weight`, `blocks.i.info_engine`, and `blocks.i.allocator`.

---

## 3. Canonical Round-Trip `strict=True` Verification

While the legacy external file `qtensorformer_small.pt` is incompatible, both canonical repository architectures pass `strict=True` save/load round-trip verification (`results/raw/checkpoint_integrity.json` and `scripts/smoke_test.py`):

| Canonical Implementation | Serialization Method | Reload Method | `strict=True` Result | Output Numerical Match |
| :--- | :--- | :--- | :--- | :--- |
| `src.models.QTensorFormer` | `torch.save(model.state_dict(), path)` | `model.load_state_dict(sd, strict=True)` | **`PASS` (185/185 keys)** | Max diff `0.0` |
| `modeling_qtensorformer.QTensorFormerForCausalLM` | `model.save_pretrained(dir)` | `QTensorFormerForCausalLM.from_pretrained(dir)` | **`PASS` (52/52 keys)** | Max diff `< 1e-6` |