43 lines
2.5 KiB
Markdown
43 lines
2.5 KiB
Markdown
# Actual-model shell census
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Read [PROTOCOL.md](PROTOCOL.md) before interpreting the results. This analyzes
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retained parameter arrays, not model activations or task accuracy.
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Run only on the authorized CPU server from the repository root:
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```sh
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OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=1 venv/bin/python -m unittest model_shell.test_analysis -v
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OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=1 venv/bin/python -m model_shell.analyze
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OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=1 venv/bin/python -m model_shell.report
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```
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Inputs are the already retained `runs/cpu-qwen35-2b/weights` and
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`runs/finetune-head-comparison/adapter.safetensors` with their existing JSON
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configuration and metadata. The adapter is evaluated as FP32 W+2BA. No weights
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are modified, no training or inference is run, and no files are deleted.
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`runs/model-shell/checkpoints` is a resumable group ledger. Re-running skips
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completed groups; do not reuse a ledger after changing its protocol/code/inputs.
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One invocation stops at 7,200 seconds, RSS above 512 MiB or available memory below
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128 MiB. Resume after addressing a resource stop; do not relax guards silently.
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`results/model-shell` contains the report, group inventory, per-profile metric
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CSV, compressed complete CDF/control summaries, and measured calibration/held-out
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radii. The NPZ keys are `GROUPINDEX_MODEL_CENTER_cal` / `_eval`, with zero-based
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group indices into `inventory.json`. Unchanged fine-tune profiles explicitly
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reference the original profiles in `profiles.json.gz`; the CSV expands both.
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Raw null draws are reproducible from the recorded deterministic seeds; null CDFs,
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scales and participation summaries are stored. `audit.json` verifies coverage,
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recomputes empirical CDFs from the saved norms, checks finite-sample rank limits,
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verifies the original asset SHA256 values, and hashes the output artifacts.
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`example_radii.npz` also saves calibration/held-out radii for the three fixed role
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examples and their regenerated nulls; their grid CDFs are checked against the
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original ledger before plotting full empirical step curves from radius zero.
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The count of views is defined by the protocol and is not a unique number of
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distributions inherent in a neural network. These overlapping views are equally
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weighted for description; they are not independent replicates. The box nulls
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match fitted marginal ranges or moments, not correlations. The maximum fitted
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radius is not the unknown support boundary. Quantities near numerical or Monte
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Carlo resolution cannot support a definitive population statement.
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