BSVGK/phi35-mini-lora-text2kg-adapter

Phi-3.5 Mini Instruct — LoRA Adapter (Text-to-KG)

Model Summary

This is the LoRA adapter for the Phi-3.5 Mini Instruct model fine-tuned to extract structured RDF knowledge graph triples from UK government procurement contract text.

For the full merged model ready for inference, use: 👉 BSVGK/phi35-mini-lora-text2kg-merged

Key Results

Metric	Score
F1 Score	0.9954
BERTScore F1	0.9997
Hallucination Rate	0.00% (Zero)
Test Contracts	1,387 unseen contracts

Model Details

• Base Model: microsoft/Phi-3.5-mini-instruct
• Adapter Type: LoRA (Low-Rank Adaptation)
• Task: Text-to-KG — RDF triple extraction from contract text
• Domain: UK Government Procurement Contracts
• Training Dataset: 9,244 verified UK contracts
• Hardware: NVIDIA A100
• Framework: PyTorch, Hugging Face PEFT, TRL, SFTTrainer

Usage

from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel

# Load base model
base_model = AutoModelForCausalLM.from_pretrained(
    "microsoft/Phi-3.5-mini-instruct"
)
tokenizer = AutoTokenizer.from_pretrained(
    "microsoft/Phi-3.5-mini-instruct"
)

# Load LoRA adapter
model = PeftModel.from_pretrained(
    base_model,
    "BSVGK/phi35-mini-lora-text2kg-adapter"
)

prompt = """Extract RDF triples from the following UK government contract:

Contract: [paste your contract text here]

RDF Triples:"""

inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))