README.md

metadata

license: apache-2.0
task_categories:
  - other
tags:
  - model-provenance
  - lineage
  - ai-bom
  - model-merging

Stemma lineage benchmark (NagaYu/stemma-bench)

Labelled ordered pairs over a generated model-lineage DAG. Every edge was produced by really running the operation on real safetensors weights (short fine-tunes, a hand-written LoRA, INT8/INT4 fake-quantisation round trips, magnitude and structured pruning, vocabulary extension, SLERP/TIES/DARE merges with recorded mixing ratios, and distillation), so the labels are recipes, not annotations.

• models: 20
• edges: 21
• pairs: ancestral 28, sibling 80, unrelated 82
• relations: continued_pretrain, distilled, lora_merged, merge_dare, merge_slerp, merge_ties, pruned_magnitude, pruned_structured, quantized_int4, quantized_int8, sft, vocab_extended, vocab_extended_trained

Columns

a, b (model ids), related (bool), direction (a->b | sibling | none), relation, plus the per-side family/op of each model.

Caveats

• Distillation edges are marked weak_weight_lineage=true (distilgpt2, smollm2-distil-half). Weight-level lineage across a distillation edge is expected to be weak; it is included so it can be scored honestly rather than quietly excluded.
• Direction accuracy must be reported per relation type. Scar-free relations (SFT, LoRA, continued pretraining) are much harder than lossy ones (quantisation, pruning, vocabulary extension).
• Unrelated pairs include same-architecture / different-initialisation models. Those are the pairs a false-positive rate should be measured on.
• Builds made with --skip-train carry training: synthetic and must not be reported as fine-tuning measurements.

What these labels can and cannot support

Measured on this dataset after it was first published:

1. Direction is near-deterministic only for lossy operations. Quantisation, pruning and vocabulary extension are recovered at 100%; scar-free sft / lora / continued_pretrain edges abstain (mean |llr| ~0.02). Scoring direction as one aggregate over all relations hides this, which is why the harness reports it per relation.
2. Outgroup rooting is invalid for the merge rows. Rooting assumes descendants drift monotonically away from the root. Merging is a contraction toward the centroid: smollm2-merge-ties2 = 0.6*sft + 0.4*cpt partly cancels two perturbations of the root and lands closer to the root than either parent (root->sft 0.000820, root->cpt 0.001610, root->merge-ties2 0.000678). Every correctly chosen sibling outgroup then pushes the answer the wrong way. Direction for a merged model has to come from the decomposition, not from distance geometry.
3. Cross-architecture distillation rows are a known-weak case, not a bug. They are included and scored honestly rather than excluded.

Current reference results on this dataset (python benchmarks/run.py): relatedness AUC 0.994 with 0.000 false-positive rate on the 25 same-architecture / different-seed controls; merge parent-set precision 1.000, recall 0.792, F1 0.867, mixing MAE 0.070 (DARE MAE 0.0004).

Derivations: https://github.com/NagaYu/stemma/blob/main/docs/FINDINGS.md

These labels describe generated derivations, not claims about anyone's published models. Stemma reports statistical evidence with a confidence and never a determination of infringement.