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

---
language:
- en
license: mit
base_model: google/gemma-1.1-2b-it
tags:
- text-generation
- knowledge-graph
- kg-to-text
- pharmaceutical
- drugbank
- natural-language-generation
- nlp
- trustworthy-ai
- hallucination-detection
- lora
- fine-tuned
- merged
datasets:
- BSVGK/drugbank_dataset
metrics:
- bleu
- bertscore
pipeline_tag: 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](https://huggingface.co/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](https://huggingface.co/datasets/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](https://github.com/BSVGK1919)
- 🔗 [LoRA Adapter](https://huggingface.co/BSVGK/gemma-1.1-2b-it-drugbank-kg2text-lora-v2)
- 🔗 [LinkedIn](https://www.linkedin.com/in/sai-venkata-gopala-krishna-bubathula-a05a26283/)