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Model: BSVGK/gemma-1.1-2b-it-drugbank-kg2text-merged-v2
Source: Original Platform

.gitattributes

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README.md

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+---
+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/)
+
+

chat_template.jinja

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+{{ bos_token }}{% if messages[0]['role'] == 'system' %}{{ raise_exception('System role not supported') }}{% endif %}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if (message['role'] == 'assistant') %}{% set role = 'model' %}{% else %}{% set role = message['role'] %}{% endif %}{{ '<start_of_turn>' + role + '
+' + message['content'] | trim + '<end_of_turn>
+' }}{% endfor %}{% if add_generation_prompt %}{{'<start_of_turn>model
+'}}{% endif %}

config.json

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+{
+  "architectures": [
+    "GemmaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 2,
+  "dtype": "bfloat16",
+  "eos_token_id": 1,
+  "head_dim": 256,
+  "hidden_act": "gelu_pytorch_tanh",
+  "hidden_activation": "gelu_pytorch_tanh",
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 16384,
+  "max_position_embeddings": 8192,
+  "model_type": "gemma",
+  "num_attention_heads": 8,
+  "num_hidden_layers": 18,
+  "num_key_value_heads": 1,
+  "pad_token_id": 0,
+  "rms_norm_eps": 1e-06,
+  "rope_parameters": {
+    "rope_theta": 10000.0,
+    "rope_type": "default"
+  },
+  "tie_word_embeddings": true,
+  "transformers_version": "5.5.3",
+  "use_bidirectional_attention": null,
+  "use_cache": true,
+  "vocab_size": 256000
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 2,
+  "eos_token_id": 1,
+  "pad_token_id": 0,
+  "transformers_version": "5.5.3"
+}

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tokenizer_config.json

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+{
+  "backend": "tokenizers",
+  "bos_token": "<bos>",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<eos>",
+  "extra_special_tokens": [
+    "<start_of_turn>",
+    "<end_of_turn>"
+  ],
+  "is_local": true,
+  "mask_token": "<mask>",
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "<pad>",
+  "sp_model_kwargs": {},
+  "spaces_between_special_tokens": false,
+  "tokenizer_class": "GemmaTokenizer",
+  "unk_token": "<unk>",
+  "use_default_system_prompt": false
+}