| --- |
| 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. |
|
|
