--- license: other license_name: weft-research license_link: https://github.com/wallfacers/data-weave pipeline_tag: text-generation library_name: transformers base_model: Qwen/Qwen2.5-Coder-3B-Instruct tags: - data-lineage - etl - sql - code - lora - research-artifact language: - en datasets: - wallfacers/weft-script-lineage-synth --- # weft-lineage-extractor-3b The **efficient tier** of the Weft lineage-extractor family: a 3B code model (LoRA fine-tuned, merged) that extracts **table- and column-level data lineage** from ETL scripts as structured JSON. Deployable on a single 12 GB consumer GPU. Trained on real-world GitHub ETL scripts with tri-vendor consensus silver labels. This repository ships **three branches** covering the table↔column trade-off frontier: | Branch | Variant | Table P / R / F1 | Column P / R / F1 | Positioning | |---|---|---|---|---| | **`main`** | loss-weighted W=3 (`run-tri-3b-lw3`) | 0.834 / 0.734 / **0.781** | 0.910 / 0.755 / **0.825** | Best balanced 3B single model | | `tri-column-specialist` | plain full-column (`run-tri-3b`) | 0.822 / 0.645 / 0.723 | 0.914 / 0.949 / **0.931** | Best column F1 | | `tri-table-specialist` | 31% column density (`run-tri-3b-col50`) | 0.825 / **0.776** / 0.800 | 0.912 / 0.423 / 0.578 | Best table recall | *Benchmark: 129 non-empty real GitHub scripts, tri-vendor consensus gold, greedy decoding at 1024 max new tokens. Column metrics are conditional on matched tables.* **Which branch?** One model for both tables and columns → `main`. Maximum column quality (pair it with a table specialist via inference-time fusion) → `tri-column-specialist`. Maximum table recall → `tri-table-specialist`. ## Method highlight: table-token loss weighting The 3B table↔column frontier is driven by **gradient imbalance, not just capacity**: in the answer JSON, high-entropy column tokens outnumber table tokens **4.39 : 1**, drowning the table-name gradient. Re-weighting the loss on table-structure tokens (W=3) lifts table recall 0.645 → **0.734** with no data removal and no extra capacity, at table-precision parity (0.834, McNemar n.s.). Loss weighting **strictly dominates column-density dilution**: at equal table recall it keeps ~**+0.15 column F1** that dilution would destroy. ## Label credibility (tri-vendor consensus) Gold and silver labels are **2-of-3 consensus across three independent vendors** (qwen-max ∩ deepseek-v4-pro ∩ GPT-5.6). GPT-5.6, which never participated in constructing the earlier two-vendor labels, independently agrees with them at **0.976 (table) / 0.958 (column)**; models trained only on the two-vendor subset reach 0.782 table recall on edges only GPT-5.6 confirms — evidence the model learns real lineage, not one vendor's labeling habits. Total teacher-labeling cost: **$2.42**. ## Honest boundaries - **Reduced, not eliminated, circularity**: labels remain LLM-consensus silver; no human gold. - **Governance routing**: 3-of-3 vendor-unanimous cases (~70%) are candidates for an auto-adopt layer; vendor-disagreement cases (~30%) route to human review. The model narrows the review queue; it does not eliminate review. - **Convention A exclusions** (dynamically-built table names, commented-out/printed SQL, temp views) are deliberate scope boundaries of static extraction, not bugs. - **The strict dual gate (table R ≥ 0.75 and column F1 ≥ 0.85) is unreachable at 3B** — shown twice independently (loss-weight sweep; r=64 capacity stack). It remains unreachable by any *single* model even at 14B; the quality path is [weft-lineage-extractor-14b](https://huggingface.co/wallfacers/weft-lineage-extractor-14b) (best balanced 0.799 / 0.856) plus inference-time dual-expert fusion. ## Training details | | | |---|---| | Base model | [Qwen/Qwen2.5-Coder-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-3B-Instruct) | | Method | LoRA r=32, α=64, bf16 (merged into this checkpoint) | | Data | 1,154 real GitHub ETL scripts, tri-vendor consensus silver labels (tables + columns) | | Branch deltas | `main`: answer table-token loss ×3 · `tri-table-specialist`: 31% column density · `tri-column-specialist`: full columns | | Schedule | 3 epochs, effective batch 16, lr 2e-4 cosine, max seq 2048 | ## Usage ### vLLM (recommended for batch extraction — continuous batching, OpenAI-compatible) ```bash pip install vllm vllm serve wallfacers/weft-lineage-extractor-3b --revision main \ --dtype bfloat16 --max-model-len 2048 --gpu-memory-utilization 0.9 --port 8000 ``` ```python from vllm import LLM, SamplingParams llm = LLM(model="wallfacers/weft-lineage-extractor-3b", revision="main", dtype="bfloat16", max_model_len=2048) sp = SamplingParams(temperature=0.0, max_tokens=256) # deterministic decoding outs = llm.chat([[{"role": "system", "content": SYSTEM_PROMPT}, {"role": "user", "content": "task_type: PYTHON\nscript:\n" + script}]], sp) print(outs[0].outputs[0].text) ``` ### transformers (single request / interactive, p50 ≈ 360 ms) ```python from transformers import AutoModelForCausalLM, AutoTokenizer REPO, REV = "wallfacers/weft-lineage-extractor-3b", "main" tok = AutoTokenizer.from_pretrained(REPO, revision=REV) model = AutoModelForCausalLM.from_pretrained(REPO, revision=REV, dtype="bfloat16", device_map="cuda") ``` System prompt (shared across the family): ```text You are a data lineage extractor for ETL scripts. Given a PYTHON, SHELL, SCALA or JAVA task script (Spark/Flink jobs included), output ONLY a JSON object {"reads": [...], "writes": [...]} where each item is {"table": str, "columns": [str] or null}. Rules: include a table only if its literal name appears in the script text; ignore dynamically-built table names, commented-out SQL, and SQL that is merely printed or logged; if nothing is read or written, output {"reads": [], "writes": []}. ``` **Output schema**: `{"reads": [{"table": str, "columns": [str] | null}], "writes": [...]}`. **Cost note**: lineage answers are short (~22 output tokens/request, prefill-bound). Self-hosted on a single consumer GPU the marginal cost approaches electricity (≈$0.004 / 1k requests) — 1–2 orders of magnitude below cloud LLM APIs for high-volume batch extraction. Full throughput/cost ledger: `out/cost-analysis-068.md` in the GitHub repo. ## Model family | Model | Role | |---|---| | [weft-lineage-extractor-14b](https://huggingface.co/wallfacers/weft-lineage-extractor-14b) | best single models (3 branches) | | [weft-lineage-extractor-7b](https://huggingface.co/wallfacers/weft-lineage-extractor-7b) | scale-curve point (capacity-valley negative result) | | [weft-lineage-extractor-3b](https://huggingface.co/wallfacers/weft-lineage-extractor-3b) | **this repo — efficient tier, 3 branches** | | [weft-lineage-extractor-1.5b](https://huggingface.co/wallfacers/weft-lineage-extractor-1.5b) | synthetic-only negative-result artifact | | [weft-lineage-extractor-0.5b](https://huggingface.co/wallfacers/weft-lineage-extractor-0.5b) | scale-curve point (synthetic-only) | | [weft-lineage-extractor-jvm-1.5b](https://huggingface.co/wallfacers/weft-lineage-extractor-jvm-1.5b) | cross-language (Scala/Java) negative result | | [weft-script-lineage-synth](https://huggingface.co/datasets/wallfacers/weft-script-lineage-synth) | synthetic corpus + evidence reports | Full evidence ledger (4.39:1 token measurement, frontier-dominance +0.15, McNemar parity, dual-gate negative results): [github.com/wallfacers/data-weave](https://github.com/wallfacers/data-weave) (`ml/lineage-extractor/out/PAPER-EVIDENCE-068.md`).