Evaluation & safety
Phase 9 — and ongoing. Know what the model is actually doing.
1. Required correctness tests
The full table is in ../tests/README.md. Summary:
| Test | Purpose |
|---|---|
| Tokenizer roundtrip | bytes → text → bytes is lossless |
| Shape tests | every layer returns the expected shape |
| Loss sanity | random model loss near ln(256) ≈ 5.54 |
| Tiny overfit | model overfits 1–10 KB repeated text |
| Gradient check | finite-difference check on a tiny layer |
| PyTorch parity | ported forward matches the PyTorch reference |
| Checkpoint reload | same loss after save + reload |
| Sampling fixed seed | deterministic generation for a fixed seed |
| Browser refresh | run resumes after a page reload |
The most important test: can it overfit a tiny repeated dataset? If not, do not scale — the model, backprop, or data pipeline is broken.
2. The evaluation matrix (LoRA)
Always run four comparisons:
A. Base model only
B. Base model + prompt examples (few-shot)
C. Base model + LoRA
D. Base model + LoRA + retrievalTypical outcome:
| Setup | Behaviour |
|---|---|
| Base only | generic |
| Few-shot prompt | immediate style improvement |
| LoRA | stronger tone/style adaptation |
| Retrieval + LoRA | best practical quality |
LoRA teaches style. Retrieval supplies context/facts.
If LoRA does not beat few-shot prompting, the adapter was not worth training.
3. Memorization test
Tiny models memorize; LoRA adapters can memorize too.
Take the first 30–50 tokens of a training example.
Ask the model to continue.
Check whether it reproduces the rest verbatim.If it copies too much: train fewer steps, lower rank, lower learning rate, add dropout, deduplicate data, increase dataset size, avoid repeated text.
4. Qualitative questions
For a style-adaptation project, ask:
- Does it sound more like the corpus?
- Does it copy exact training text?
- Does it hallucinate facts?
- Does retrieval improve grounding?
- Does LoRA actually beat few-shot prompting?
5. Backend parity (Phase 4–5)
- WASM vs PyTorch — the WASM forward must match the Python reference within tolerance before you trust WASM training.
- WebGPU vs WASM — each WebGPU kernel must match the WASM kernel within tolerance before it joins the pipeline. Start with matmul.
6. Safety notes
- Style ≠ intelligence. LoRA on blogs learns tone, format, phrasing, argument rhythm, vocabulary — not truth, judgment, current beliefs, or reliable reasoning.
- Watch for data leakage and copying risk, especially with tiny models.
- Do not redistribute adapters trained on a living author’s writing.
Deliverable
A small evaluation suite producing: base output, prompt-only output, LoRA output, and LoRA + retrieval output — for the same held-out prompts.