gemma-1.1-2b-it-drugbank-kg2text-merged-v2/README.md

language, license, base_model, tags, datasets, metrics, pipeline_tag

language	license	base_model	tags	datasets	metrics	pipeline_tag
en	mit	google/gemma-1.1-2b-it	text-generation knowledge-graph kg-to-text pharmaceutical drugbank natural-language-generation nlp trustworthy-ai hallucination-detection lora fine-tuned merged	BSVGK/drugbank_dataset	bleu bertscore	text-generation

Gemma 1.1 2B IT — Merged Model v2 (DrugBank KG-to-Text)

Model Summary

This is the full merged model of Gemma 1.1 2B IT fine-tuned with LoRA 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.

This is the recommended model for inference — no additional adapter loading required.

For the LoRA adapter only, use: 👉 BSVGK/gemma-1.1-2b-it-drugbank-kg2text-lora-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
• Fine-tuning Method: 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

Load merged model directly — no adapter needed

tokenizer = AutoTokenizer.from_pretrained( "BSVGK/gemma-1.1-2b-it-drugbank-kg2text-merged-v2" ) model = AutoModelForCausalLM.from_pretrained( "BSVGK/gemma-1.1-2b-it-drugbank-kg2text-merged-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

Comparison with LoRA Adapter

	Merged Model v2	LoRA Adapter v2
Inference	✅ Direct — no base model needed	❌ Requires base model + PEFT
Storage	Larger (full model)	Smaller (adapter only)
Speed	Faster to load	Slower to load
Recommended for	Production inference	Research & experimentation

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_merged, author = {Sai Venkata Gopala Krishna Bubathula}, title = {Gemma 1.1 2B IT Merged Model 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-merged-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
• 🔗 GitHub
• 🔗 LoRA Adapter
• 🔗 LinkedIn