Known Contract: Wikipedia Style

The adapter was trained on REBEL, so its output follows a Wikipedia-style contract:

• Entity surface forms follow Wikipedia conventions
• Relations follow Wikidata definitions and granularity

This is the contract the model is built against. Inputs and evaluation should stay inside that contract — domain-specific terminology, informal text, or alternate relation ontologies are out of scope and will degrade quality.

Output Format

The model emits structured JSON:

json
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{
  "entities": ["Entity A", "Entity B"],
  "relations": [
    {"head": "Entity A", "relation": "relation_type", "tail": "Entity B"}
  ]
}

Usage

python
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from transformers import pipeline

pipe = pipeline(
    "text-generation", model="rst0070/tiny-graph-extractor-qwen3.5-0.8b-lora",
    max_new_tokens=1024,
)
messages = [
    {
        "role": "system",
        "content": (
            "You are a knowledge graph extraction assistant. "
            "Given a text, extract all entities and their relations as JSON. "
            "Output only valid JSON with no additional text."
        )
    },
    {
        "role": "user",
        "content": "Apple was founded by Steve Jobs, Steve Wozniak, and Ronald Wayne in 1976. Steve Jobs served as the CEO of Apple.",
    },
]

result = pipe(messages)
response = result[0]['generated_text'][-1]
print(response)

import re
import json

fence_re = re.compile(r"```(?:json)?\s*\n(.*?)\n```", re.DOTALL)
match = fence_re.search(response["content"])
if match:
    print(json.loads(match.group(1)))

Training

The training target was constructed to deviate as little as possible from the base model's natural output, so SFT only has to close the smallest possible gap:

1. Observe how the base model formats answers on REBEL inputs with no fine-tuning.
2. Align the SFT target to match that observed format/phrasing where possible, while staying factually correct.
3. Train with QLoRA SFT against that aligned target.

Limitations

• English / Wikipedia distribution only. Performance on other languages, domains (medical, legal, financial), or informal text is unknown and likely poor.
• Wikidata relation ontology. Relations outside the Wikidata vocabulary will not be produced reliably.
• Sentence-level inputs. Trained on REBEL sentence-level examples; not validated on long documents.
• No factual grounding. The model extracts what it reads; it does not verify claims.

License

Apache 2.0, inheriting from the base model and dataset license terms.