codehance
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distilgpt2-medical-pharma

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 ---
+language: en
+license: mit
+tags:
+- medical
+- pharmaceutical
+- autocomplete
+- distillation
+- gpt2
+datasets:
+- medmcqa
+metrics:
+- perplexity
+model-index:
+- name: codehance/distilgpt2-medical-pharma
+  results:
+  - task:
+      type: text-generation
+    dataset:
+      name: Medical Q&A
+      type: medmcqa
+    metrics:
+    - name: Perplexity
+      type: perplexity
+      value: 44.07
 ---
+# DistilGPT-2 Medical Pharmaceutical Autocomplete
+## Model Description
+This is a distilled GPT-2 model fine-tuned for pharmaceutical autocomplete. It suggests drug names and medical terminology based on clinical context.
+**Key Features:**
+- 34% smaller than base fine-tuned model (81,912,576 parameters)
+- 45% faster inference (347.9ms per generation)
+- Specialized in pharmaceutical vocabulary
+## Training Process
+### Stage 1: Fine-Tuning
+- Base model: GPT-2 (124M parameters)
+- Dataset: Medical Q&A (medmcqa) - 4,500 training examples
+- Training: 3 epochs
+- Final perplexity: 23.61
+### Stage 2: Knowledge Distillation
+- Teacher: Fine-tuned GPT-2
+- Student: DistilGPT-2
+- Training: 2 epochs
+- Compression: 34.2% size reduction
+## Performance
+| Metric | Value |
+|--------|-------|
+| Parameters | 81,912,576 |
+| Perplexity | 44.07 |
+| Inference Speed | 347.9ms |
+| Quality Retained | 53.6% |
+## Usage
+```python
+from transformers import GPT2LMHeadModel, GPT2Tokenizer
+# Load model and tokenizer
+model = GPT2LMHeadModel.from_pretrained("codehance/distilgpt2-medical-pharma")
+tokenizer = GPT2Tokenizer.from_pretrained("codehance/distilgpt2-medical-pharma")
+# Generate pharmaceutical suggestions
+prompt = "The patient should take"
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model.generate(**inputs, max_length=30, num_return_sequences=3)
+for output in outputs:
+    print(tokenizer.decode(output, skip_special_tokens=True))
+```
+## Intended Use
+**Primary Use Cases:**
+- Pharmaceutical autocomplete systems
+- Medical documentation assistance
+- Clinical note-taking tools
+- Drug name suggestion
+**Limitations:**
+- Not a substitute for medical advice
+- May suggest incorrect drugs - always verify with qualified professionals
+- Trained on medical exam questions, not real prescriptions
+- English language only
+## Training Data
+- **Source:** MedMCQA dataset (Indian medical entrance exam questions)
+- **Size:** 4,500 training examples
+- **Content:** Medical questions with pharmaceutical terminology
+## Ethical Considerations
+⚠️ **Important:** This model is for autocomplete assistance only. It should NOT be used as the sole basis for medical decisions. Always verify suggestions with qualified healthcare professionals.
+## Model Card Authors
+Created as part of a pharmaceutical autocomplete system tutorial demonstrating transfer learning, fine-tuning, and knowledge distillation.
+## Citation
+```bibtex
+@misc{distilgpt2-medical-pharma,
+  author = {codehance},
+  title = {DistilGPT-2 Medical Pharmaceutical Autocomplete},
+  year = {2025},
+  publisher = {Hugging Face},
+  howpublished = {\url{https://huggingface.co/codehance/distilgpt2-medical-pharma}}
+}
+```