license, license_name, license_link, pipeline_tag, library_name, base_model, datasets, language, tags, model-index

license	license_name	license_link	pipeline_tag	library_name	base_model	datasets	language	tags	model-index
other	weft-research	https://github.com/wallfacers/data-weave	text-generation	transformers	Qwen/Qwen2.5-Coder-0.5B-Instruct	wallfacers/weft-script-lineage-synth	en	research-artifact negative-result memorization domain-shift data-lineage etl lora	name results weft-lineage-extractor-0.5b task dataset metrics type name table-level-lineage-extraction ETL table-level data-lineage extraction type name synthetic-etl synthetic held-out (structural-form isolated) type value name precision 0.994 Table precision (synthetic held-out) task dataset metrics type name table-level-lineage-extraction ETL table-level data-lineage extraction type name real-github-etl real GitHub ETL (human gold) type value name precision 0.243 Table precision (real, out-of-distribution)

weft-lineage-extractor-0.5b — smallest scale point of a NEGATIVE RESULT

⚠️ RESEARCH ARTIFACT — the 0.5B point of a synthetic-only training study. Not a production tool.

✅ Resolved by real data: use weft-lineage-extractor-3b (real corpus, real precision 0.64). Full study: weft-lineage-extractor-1.5b.

The 0.5B point of a study showing that synthetic-only training induces a verbatim memorization leak in small models for ETL table-lineage extraction. It is the smallest scale point and shows the worst leak: near-perfect synthetic precision (0.994) collapses to 0.243 on real GitHub scripts, with 37.4% of hallucinations being table names recited verbatim from the synthetic training pool.

Same recipe as the 1.5B main model (LoRA on Qwen2.5-Coder-Instruct, Python/Shell synthetic ETL scripts, zero real scripts); only the base size differs.

This point's numbers (table-level, Convention A)

metric	synthetic held-out	real GitHub ETL
precision	0.994	0.243
direction accuracy	—	0.369
verbatim memorization leak	—	37.4%

Where it fits (scale curve)

scale	real precision	real direction	verbatim leak
0.5B (this)	0.243	0.369	37.4%
1.5B (main)	0.270	0.496	22.4%
3B (synthetic)	0.325	0.468	10.9%
3B (real corpus)	0.64	—	~0

Leak shrinks with scale (capacity), but only real training data closes the real-world gap (bottom row). Direction confusion does not improve with size.

Intended use

• ✅ Reproducing / studying the synthetic-training memorization-leak failure at minimal scale.
• ❌ Not for production lineage — use the real-corpus 3B.

Usage, prompt format, training details, citation

Identical to the main model (this variant uses task_type: PYTHON | SHELL; everything else the same): weft-lineage-extractor-1.5b.

• Dataset & reports: wallfacers/weft-script-lineage-synth
• Base model: Qwen/Qwen2.5-Coder-0.5B-Instruct
• Platform: Weft (data-weave)