GLM-5.2 is live. #1 throughput on OpenRouter, pay-per-token on FriendliAI. Try it today ➜
BSVGK
Available on FriendliAI
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Run this model inference with full control and performance in your environment.
Model Details
Model Provider
BSVGK
Model Tree
google/gemma-1.1-2b-it
Input Modalities
Output Modalities
Supported Functionality
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
| Metric | Score |
|---|---|
| BLEU Score | 0.9737 |
| BERTScore F1 | 0.9896 |
| Fact F1 | 0.9966 |
| Hallucination Rate | 0.54% |
| Test Samples | 254 unseen DrugBank entries |
from transformers import AutoTokenizer, AutoModelForCausalLM from peft import PeftModel
base_model = AutoModelForCausalLM.from_pretrained( "google/gemma-1.1-2b-it" ) tokenizer = AutoTokenizer.from_pretrained( "google/gemma-1.1-2b-it" )
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:
Description:"""
inputs = tokenizer(prompt, return_tensors="pt") outputs = model.generate(**inputs, max_new_tokens=256) print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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.
@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} }
Sai Venkata Gopala Krishna Bubathula