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Model: BSVGK/gemma-1.1-2b-it-drugbank-kg2text-merged-v2 Source: Original Platform
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README.md
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README.md
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---
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language:
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- en
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license: mit
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base_model: google/gemma-1.1-2b-it
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tags:
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- text-generation
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- knowledge-graph
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- kg-to-text
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- pharmaceutical
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- drugbank
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- natural-language-generation
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- nlp
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- trustworthy-ai
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- hallucination-detection
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- lora
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- fine-tuned
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- merged
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datasets:
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- BSVGK/drugbank_dataset
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metrics:
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- bleu
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- bertscore
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pipeline_tag: text-generation
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---
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# Gemma 1.1 2B IT — Merged Model v2 (DrugBank KG-to-Text)
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## Model Summary
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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**.
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This is the recommended model for inference — no additional adapter loading required.
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> For the LoRA adapter only, use:
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> 👉 [BSVGK/gemma-1.1-2b-it-drugbank-kg2text-lora-v2](https://huggingface.co/BSVGK/gemma-1.1-2b-it-drugbank-kg2text-lora-v2)
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## Key Results
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| Metric | Score |
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|--------|-------|
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| BLEU Score | **0.9737** |
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| BERTScore F1 | **0.9896** |
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| Fact F1 | **0.9966** |
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| Hallucination Rate | **0.54%** |
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| Test Samples | 254 unseen DrugBank entries |
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## Model Details
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- **Base Model:** google/gemma-1.1-2b-it
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- **Fine-tuning Method:** LoRA (Low-Rank Adaptation)
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- **Task:** KG-to-Text — RDF triples → fluent drug descriptions
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- **Domain:** Pharmaceutical — DrugBank
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- **Training Dataset:** 2,537 verified DrugBank RDF triples
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- **Hardware:** NVIDIA A100
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- **Framework:** PyTorch, Hugging Face PEFT, TRL, SFTTrainer
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## Usage
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from transformers import AutoTokenizer, AutoModelForCausalLM
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# Load merged model directly — no adapter needed
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tokenizer = AutoTokenizer.from_pretrained(
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"BSVGK/gemma-1.1-2b-it-drugbank-kg2text-merged-v2"
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)
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model = AutoModelForCausalLM.from_pretrained(
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"BSVGK/gemma-1.1-2b-it-drugbank-kg2text-merged-v2"
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)
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prompt = """Generate a natural language description from the following RDF triples:
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Triples:
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- DrugA hasIndication Condition_X
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- DrugA hasMechanism Mechanism_Y
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- DrugA hasInteraction DrugB
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Description:"""
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inputs = tokenizer(prompt, return_tensors="pt")
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outputs = model.generate(**inputs, max_new_tokens=256)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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## Dataset
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- **Dataset:** [BSVGK/drugbank_dataset](https://huggingface.co/datasets/BSVGK/drugbank_dataset)
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- **Size:** 2,537 training + 254 test samples
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- **Source:** DrugBank pharmaceutical database
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- **Format:** RDF Triples → Natural Language Drug Description
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## Comparison with LoRA Adapter
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| | Merged Model v2 | LoRA Adapter v2 |
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|--|--|--|
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| Inference | ✅ Direct — no base model needed | ❌ Requires base model + PEFT |
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| Storage | Larger (full model) | Smaller (adapter only) |
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| Speed | Faster to load | Slower to load |
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| Recommended for | Production inference | Research & experimentation |
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## Intended Use
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- Pharmaceutical knowledge graph verbalisation
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- Drug information summarisation and description generation
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- Research in trustworthy and hallucination-free biomedical NLP
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- Natural language generation from biomedical knowledge graphs
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## Out of Scope
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- Non-pharmaceutical domains
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- Clinical diagnosis or medical advice
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- General purpose text generation
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## Important Notice
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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.
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## Citation
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@misc{bubathula2026drugbank_merged,
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author = {Sai Venkata Gopala Krishna Bubathula},
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title = {Gemma 1.1 2B IT Merged Model v2: KG-to-Text Generation
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for DrugBank Pharmaceutical Data},
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year = {2026},
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publisher = {HuggingFace},
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url = {https://huggingface.co/BSVGK/gemma-1.1-2b-it-drugbank-kg2text-merged-v2},
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institution = {University of East London & Depixen}
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}
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## Developer
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**Sai Venkata Gopala Krishna Bubathula**
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- 🎓 MSc Big Data Technologies, University of East London
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- 🏢 AI Engineer — UEL–Depixen Industrial Placement
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- 🔗 [GitHub](https://github.com/BSVGK1919)
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- 🔗 [LoRA Adapter](https://huggingface.co/BSVGK/gemma-1.1-2b-it-drugbank-kg2text-lora-v2)
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- 🔗 [LinkedIn](https://www.linkedin.com/in/sai-venkata-gopala-krishna-bubathula-a05a26283/)
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