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METHOD.md

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+# Calibration methodology — what we measured
+
+Findings from building `nemotron-3.5-lightning`. They are about calibration in general,
+not about one model, and every number here is reproducible with the tools in this repo.
+
+## A recipe is per-model, and the chat markup is the reason
+
+The pool is model-agnostic: `pool/**/**.jsonl` holds dialogues as structured records, not
+as text. Rendering into a model's chat template happens at build time, so one pool serves
+every target.
+
+This matters more than it sounds. Reusing a build made for another model puts that model's
+special tokens in the corpus. For `muse-glimmer-30b` those are `<|start|>`, `<|message|>`,
+`<|eot|>`; for `nemotron-3.5-lightning` they are `<|im_start|>`, `<|im_end|>`, `<think>`,
+`<tool_call>`. **The two sets do not overlap at all.** Run `llama-imatrix --parse-special`
+with the wrong build and the markup is tokenized as literal punctuation, while the tokens
+the model will really see appear zero times — for these recipes that is the agentic plus
+reasoning slices, about 40% of the corpus, calibrating nothing.
+
+It also shows up in convergence. Same tool, same model, same chunk budget:
+
+| corpus | step 1504 → 2000 | tensors still moving |
+|---|---:|---:|
+| rendered in another model's markup | mean cos 0.9928 | 22 |
+| rendered in the target's own markup | mean cos 0.9985 | 10 |
+
+Correct markup converges about twice as cleanly, because the wrong tokens were injecting
+noise into the estimate.
+
+Add a model with a recipe plus a renderer module; `chat.format` selects it. The vocabulary
+sweep is per-tokenizer and auto-selected by recipe name, so adding a model never requires
+editing recipes that already exist.
+
+## Content matters ~100× more than volume
+
+Cosine similarity between imatrix files, per tensor:
+
+| comparison | mean cos |
+|---|---:|
+| same corpus, 823 vs 3000 chunks (3.6× the data) | 0.9819 |
+| different corpora, both at 823 chunks | 0.8553 |
+
+Tripling the data moves the importance vector by 0.018. Changing what is *in* the corpus
+moves it by 0.145. Beyond the saturation point, more of the same text is close to free of
+effect; a different mix is not.
+
+The two corpora also do not converge toward each other as data grows (0.855 → 0.860 from
+823 to 3000 chunks), so the difference is systematic, not sampling noise.
+
+## When to stop: a criterion, not a feeling
+
+The imatrix estimates a mean, and means converge. The question "have I collected enough"
+has a definite answer.
+
+**Necessary, cheap, no model needed.** Build at N and 2N chunks, take the per-tensor cosine
+between them. Done when no tensor is still moving. `tools/converge.py` does this against
+`--save-frequency` checkpoints, so one run produces the whole curve for free.
+
+For `nemotron-3.5-lightning` that point is ~8,500 chunks (4.3M tokens): past it, zero of
+186 tensors change measurably, min cosine 0.9963.
+
+**Sufficient, expensive, final.** Quantize the same recipe twice, with the imatrix at N and
+at 2N, and measure KL divergence of both against the unquantized model on held-out text.
+Done when the difference between them is below the reported KLD error — at that point more
+data provably cannot change the product.
+
+Note what this criterion does *not* answer: whether the corpus has the right content. That
+question is only well-posed once you fix the evaluation set, because "importance" is defined
+relative to the text the model will actually see. A corpus tuned for agentic work should win
+on agentic held-out and may lose on generic prose — that is the intended outcome, not a bug.
+This is why `eval/` carries three independent sets rather than one.
+
+## `% Active` is not a quality metric
+
+`llama-imatrix --show-statistics` reports a `% Active` column. It is the fraction of columns
+whose mean squared activation exceeds a hard threshold of `1e-5`
+(`tools/imatrix/imatrix.cpp`, `compute_statistics`). It measures activation magnitude, not
+calibration coverage.
+
+Early layers score low on it because their activations are genuinely small — layer 1's
+`ffn_down_shexp` has Σ(Act²) = 0.13. No corpus raises that; it is a property of the model.
+Chasing the number optimizes for nothing.
+
+Two checks that do mean something:
+
+- **Dead experts** — the `.counts` array in the imatrix says how many times each expert was
+  routed to. `counts[i] == 0` is a genuine hole. Measured here: 0 dead of 5,888
+  (46 expert tensors × 128) at every corpus size tested, including 823 chunks. For this
+  model, expert coverage is not what a larger corpus buys.
+- **Per-tensor cosine between imatrix files** — the convergence criterion above.
+
+The per-layer Σ(Act²) curve is a model property, not a corpus property: two corpora with
+cosine 0.855 between their imatrices produce per-layer curves within 8% of each other
+(median 2.6%). Useful as a sanity check for a broken build, useless as a quality claim.
+
+## Practical notes
+
+- `llama-imatrix` defaults to `parse_special = false`. Without `--parse-special` all chat
+  markup is literal text.
+- `-b 8192` instead of the default 512: 1h22m instead of 2h12m for 9.3k chunks on 2×RTX 5090.
+  It plateaus there; `-b 16384` saves another minute.
+- MTP / NextN blocks are never executed in a normal forward pass, so they collect no imatrix
+  data at any corpus size. Pin them explicitly at quantize time.
+- `max_doc_fraction` is not optional. Uncapped, one amalgamated header took 59.8% of a slice
+  — the imatrix then describes that file rather than that category.