PharmaQA‑270M is a compact, instruction-tuned language model for pharmacology and pharmacy domains. Based on Gemma3 (270M parameters), it was fine-tuned using LoRA and merged into a single model checkpoint for easy deployment. This model is optimized for educational and research use cases, especially where compute constraints are present.

Model Details

Property	Value
Base Model	Google Gemma3 (270M parameters)
Fine-tuning Method	LoRA using Unsloth
Dataset Used	25,000 Q&A pairs from MIRIAD-4.4M
Epochs	3
Final Format	Merged (base + LoRA weights)
Model Size	270M
License	ODC-BY v1.0 dataset license (non-commercial)
Author	Mohamed Yasser

⚠️ Caution & Intended Use

Do not use this model for real-world medical diagnosis, treatment, or care decisions.
The model was trained on MIRIAD Q&A pairs generated via LLMs from biomedical literature.
MIRIAD and this model must be used for educational, research, and academic exploration only.
This model inherits all OpenAI and ODC-BY v1.0 usage limitations associated with the dataset.

Performance Summary

From evaluation on 50 unseen pharma questions:

Metric	Value
Average Answer Length	40.3 words
Longest Answer	95 words
Shortest Answer	12 words
Empty / Short Responses	0
Clinical Accuracy	✅ Consistent terminology
Depth in Short Responses	⚠️ Limited
Best Use Case	Lightweight educational deployment (MCQs, tutoring)

Sample Inference Code

python

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "yasserrmd/PharmaQA-270M"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")
model.eval()

question = "What is the mechanism of action of metformin?"
messages = [{"role": "user", "content": f"Q: {question} A:"}]

inputs = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    return_tensors="pt",
    tokenize=True,
    return_dict=True
).to(model.device)

if "token_type_ids" in inputs:
    del inputs["token_type_ids"]

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=128,
        temperature=0.7,
        top_p=0.95,
        repetition_penalty=1.05
    )

response = tokenizer.decode(outputs[0], skip_special_tokens=True).strip()
answer = response.split("A:")[-1].strip()

print("💊 Question:", question)
print("🧠 Answer:", answer)

License

Model: Open for academic and non-commercial use
Dataset: MIRIAD-4.4M under ODC-BY v1.0

Acknowledgements

MIRIAD creators for making the dataset openly accessible.
Unsloth team for enabling fast LoRA tuning on small GPUs.
Hugging Face and Google for Gemma3 base model.