Instructions to use wallfacers/weft-lineage-extractor-0.5b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use wallfacers/weft-lineage-extractor-0.5b with Transformers:

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

pipe = pipeline("text-generation", model="wallfacers/weft-lineage-extractor-0.5b")
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-0.5b")
model = AutoModelForCausalLM.from_pretrained("wallfacers/weft-lineage-extractor-0.5b", 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-0.5b 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-0.5b"
# 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-0.5b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/wallfacers/weft-lineage-extractor-0.5b

SGLang

How to use wallfacers/weft-lineage-extractor-0.5b 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-0.5b" \
    --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-0.5b",
		"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-0.5b" \
        --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-0.5b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use wallfacers/weft-lineage-extractor-0.5b with Docker Model Runner:
```
docker model run hf.co/wallfacers/weft-lineage-extractor-0.5b
```

weft-lineage-extractor-0.5b / README.md

wallfacers

docs: enterprise-standard card + link to real-corpus 3B resolution

2b929c4 verified about 1 month ago

preview code

Raw

History Blame Contribute Delete

3.66 kB

	---
	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-0.5B-Instruct
	datasets:
	- wallfacers/weft-script-lineage-synth
	language:
	- en
	tags:
	- research-artifact
	- negative-result
	- memorization
	- domain-shift
	- data-lineage
	- etl
	- lora
	model-index:
	- name: weft-lineage-extractor-0.5b
	results:
	- task:
	type: table-level-lineage-extraction
	name: ETL table-level data-lineage extraction
	dataset:
	type: synthetic-etl
	name: synthetic held-out (structural-form isolated)
	metrics:
	- type: precision
	value: 0.994
	name: Table precision (synthetic held-out)
	- task:
	type: table-level-lineage-extraction
	name: ETL table-level data-lineage extraction
	dataset:
	type: real-github-etl
	name: real GitHub ETL (human gold)
	metrics:
	- type: precision
	value: 0.243
	name: 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](https://huggingface.co/wallfacers/weft-lineage-extractor-3b) (real corpus, real precision 0.64). Full study: [weft-lineage-extractor-1.5b](https://huggingface.co/wallfacers/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](https://huggingface.co/wallfacers/weft-lineage-extractor-1.5b)
	(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](https://huggingface.co/wallfacers/weft-lineage-extractor-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](https://huggingface.co/wallfacers/weft-lineage-extractor-1.5b).

	- Dataset & reports: [wallfacers/weft-script-lineage-synth](https://huggingface.co/datasets/wallfacers/weft-script-lineage-synth)
	- Base model: [Qwen/Qwen2.5-Coder-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-0.5B-Instruct)
	- Platform: [Weft (data-weave)](https://github.com/wallfacers/data-weave)