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")
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 — 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)

Downloads last month: 491

Safetensors

Model size

0.5B params

Tensor type

BF16

Model tree for wallfacers/weft-lineage-extractor-0.5b

Base model

Qwen/Qwen2.5-0.5B

Finetuned

Qwen/Qwen2.5-Coder-0.5B

Finetuned

Qwen/Qwen2.5-Coder-0.5B-Instruct

Adapter

(50)

this model

Dataset used to train wallfacers/weft-lineage-extractor-0.5b

Evaluation results

Table precision (synthetic held-out) on synthetic held-out (structural-form isolated)
self-reported

0.994
Table precision (real, out-of-distribution) on real GitHub ETL (human gold)
self-reported

0.243

wallfacers
/

weft-lineage-extractor-0.5b