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Qwen/Qwen2.5-0.5B-Instruct, finely tuned for extracting clean and accurate Knowledge Graph triples from unstructured text. This model was trained with the Unsloth library for 2x faster, memory-efficient training.1SYSTEM_PROMPT = """You are an expert at extracting clean, accurate knowledge graph triples from text.
2
3Your task is to carefully read the input text and extract **all** meaningful triples in this exact format:
4(subject | relation | object)
5
6Strict rules you must follow:
7- Subject and object must be specific named entities or concrete concepts explicitly mentioned in the text (people, organizations, locations, events, products, years, etc.)
8- Relation should be a short, clear predicate in base form or simple present tense (examples: "is", "has", "works at", "located in", "born in", "capital of", "founded in")
9- Only extract triples that are **directly supported** by the text — do **not** infer, assume, hallucinate or add information that is not clearly stated
10- If uncertain about a triple → do **not** include it
11- Each triple must be written on its **own separate line**
12- Do **not** add any explanations, headings, numbering, bullet points, comments, or extra text of any kind
13- If no valid triples can be extracted → return exactly one line: "No triples found"
14"""1messages = [
2 {"role": "system", "content": SYSTEM_PROMPT},
3 {"role": "user", "content": f"Text:\n{text.strip()}"}
4]
5
6prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)r): 16lora_alpha): 32["q_proj", "v_proj"]