METHOD.md

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.