Instructions to use wallfacers/weft-lineage-extractor-3b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use wallfacers/weft-lineage-extractor-3b with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="wallfacers/weft-lineage-extractor-3b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("wallfacers/weft-lineage-extractor-3b")
model = AutoModelForCausalLM.from_pretrained("wallfacers/weft-lineage-extractor-3b", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use wallfacers/weft-lineage-extractor-3b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "wallfacers/weft-lineage-extractor-3b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "wallfacers/weft-lineage-extractor-3b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/wallfacers/weft-lineage-extractor-3b

SGLang

How to use wallfacers/weft-lineage-extractor-3b with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "wallfacers/weft-lineage-extractor-3b" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "wallfacers/weft-lineage-extractor-3b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "wallfacers/weft-lineage-extractor-3b" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "wallfacers/weft-lineage-extractor-3b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use wallfacers/weft-lineage-extractor-3b with Docker Model Runner:
```
docker model run hf.co/wallfacers/weft-lineage-extractor-3b
```

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 (best balanced 0.799 / 0.856) plus inference-time dual-expert fusion.

Training details


Base model	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)

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

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)

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):

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	best single models (3 branches)
weft-lineage-extractor-7b	scale-curve point (capacity-valley negative result)
weft-lineage-extractor-3b	this repo — efficient tier, 3 branches
weft-lineage-extractor-1.5b	synthetic-only negative-result artifact
weft-lineage-extractor-0.5b	scale-curve point (synthetic-only)
weft-lineage-extractor-jvm-1.5b	cross-language (Scala/Java) negative result
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 (ml/lineage-extractor/out/PAPER-EVIDENCE-068.md).

Downloads last month: 1,336

Safetensors

Model size

3B params

Tensor type

BF16

Model tree for wallfacers/weft-lineage-extractor-3b

Base model

Qwen/Qwen2.5-3B

Finetuned

Qwen/Qwen2.5-Coder-3B

Finetuned

Qwen/Qwen2.5-Coder-3B-Instruct

Adapter

(64)

this model

wallfacers
/

weft-lineage-extractor-3b