Update README.md

README.md

@@ -68,9 +68,54 @@ library_name: peft
 Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
 
 ## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+from peft import PeftModel
+
+# Load the base model and tokenizer
+base_model_id = "deepseek-ai/deepseek-r1-distill-qwen-7b"
+tokenizer = AutoTokenizer.from_pretrained(base_model_id, trust_remote_code=True)
+tokenizer.pad_token = tokenizer.eos_token
+
+# Load the base model
+base_model = AutoModelForCausalLM.from_pretrained(
+    base_model_id,
+    device_map="auto",
+    trust_remote_code=True,
+    torch_dtype=torch.float16
+)
+
+# Load the fine-tuned LoRA adapter
+adapter_path = "your-username/deepseek-medical-reasoner"  # Replace with actual model path
+model = PeftModel.from_pretrained(
+    base_model, 
+    adapter_path,
+    torch_dtype=torch.float16,
+    device_map="auto"
+)
+
+# Create a text generation pipeline
+pipe = pipeline(
+    "text-generation",
+    model=model,
+    tokenizer=tokenizer,
+    max_new_tokens=512,
+    temperature=0.6,
+    top_p=0.95,
+    repetition_penalty=1.15
+)
+
+# Example usage
+prompt = """Please reason step by step:
+
+A 45-year-old patient presents with sudden onset chest pain, shortness of breath, and anxiety. 
+The pain is described as sharp and worsens with deep breathing. 
+What is the most likely diagnosis and what immediate tests should be ordered?"""
+
+result = pipe(prompt)
+print(result[0]["generated_text"])
+```
 [More Information Needed]
 
 ## Training Details