Gemma 1.1 2B IT — LoRA Adapter v2 (DrugBank KG-to-Text)
Model Summary
This is the LoRA adapter v2 for the Gemma 1.1 2B IT model fine-tuned to generate fluent, hallucination-free natural language drug descriptions from pharmaceutical RDF knowledge graph triples sourced from DrugBank. It was developed as part of a UEL–Depixen industrial placement research project focused on building trustworthy, domain-specific SLMs.
For the full merged model ready for inference, use:
👉
BSVGK/gemma-1.1-2b-it-drugbank-kg2text-merged-v2
Key Results
| Metric | Score |
|---|
| BLEU Score | 0.9737 |
| BERTScore F1 | 0.9896 |
| Fact F1 | 0.9966 |
| Hallucination Rate | 0.54% |
| Test Samples | 254 unseen DrugBank entries |
Model Details
- Base Model: google/gemma-1.1-2b-it
- Adapter Type: LoRA (Low-Rank Adaptation)
- Task: KG-to-Text — RDF triples → fluent drug descriptions
- Domain: Pharmaceutical — DrugBank
- Training Dataset: 2,537 verified DrugBank RDF triples
- 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(
"google/gemma-1.1-2b-it"
)
tokenizer = AutoTokenizer.from_pretrained(
"google/gemma-1.1-2b-it"
)
Load LoRA adapter
model = PeftModel.from_pretrained(
base_model,
"BSVGK/gemma-1.1-2b-it-drugbank-kg2text-lora-v2"
)
prompt = """Generate a natural language description from the following RDF triples:
Triples:
- DrugA hasIndication Condition_X
- DrugA hasMechanism Mechanism_Y
- DrugA hasInteraction DrugB
Description:"""
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Dataset
- Dataset: BSVGK/drugbank_dataset
- Size: 2,537 training + 254 test samples
- Source: DrugBank pharmaceutical database
- Format: RDF Triples → Natural Language Drug Description
Intended Use
- Pharmaceutical knowledge graph verbalisation
- Drug information summarisation and description generation
- Research in trustworthy and hallucination-free biomedical NLP
- Natural language generation from biomedical knowledge graphs
Out of Scope
- Non-pharmaceutical domains
- Clinical diagnosis or medical advice
- General purpose text generation
Important Notice
This model is intended for research purposes only. It should not be used for clinical decision-making or medical advice. Always consult a qualified healthcare professional.
Citation
@misc{bubathula2026drugbank_adapter,
author = {Sai Venkata Gopala Krishna Bubathula},
title = {Gemma 1.1 2B IT LoRA Adapter v2: KG-to-Text Generation
for DrugBank Pharmaceutical Data},
year = {2026},
publisher = {HuggingFace},
url = {
https://huggingface.co/BSVGK/gemma-1.1-2b-it-drugbank-kg2text-lora-v2},
institution = {University of East London & Depixen}
}
Developer
Sai Venkata Gopala Krishna Bubathula
-
🎓 MSc Big Data Technologies, University of East London
-
🏢 AI Engineer — UEL–Depixen Industrial Placement
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Adapter intended for KG-to-text generation only
