Update README.md

README.md

@@ -40,7 +40,16 @@ Large language models, such as GPT-4, obtain reasonable scores on medical questi
 In this work, we introduce a method to improve the proficiency of a small language model in the medical domain by employing a two-fold approach. 
 We first fine-tune the model on a corpus of medical textbooks. Then, we use GPT-4 to generate questions similar to the downstream task, prompted with textbook knowledge, and use them to fine-tune the model. 
 We show the benefits of our training strategy on a medical answering question dataset.
-The study's findings highlight the potential of small language models in the medical domain when appropriately fine-tuned. 
+
+
+### Using the model
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("raidium/MQG")
+model = AutoModelForCausalLM.from_pretrained("raidium/MQG") 
+```
 
 
 - **Developed by:** Raidium