Update README.md

README.md

@@ -20,17 +20,34 @@ Example of usage with vLLM library:
 ```python
 from vllm import LLM, SamplingParams
 
-prompts = [
-    "What is the mechanism of action of antibiotics?",
-    "How do statins work to lower cholesterol levels?",
-    "Tell me about Paracetamol"
-]
+tokenizer = AutoTokenizer.from_pretrained('axiong/PMC_LLaMA_13B')
+
+prompt_input = (
+    'Below is an instruction that describes a task, paired with an input that provides further context.'
+    'Write a response that appropriately completes the request.\n\n'
+    '### Instruction:\n{instruction}\n\n### Input:\n{input}\n\n### Response:'
+)
+
+example = {
+    "instruction": "You're a doctor, kindly address the medical queries according to the patient's account. Answer with the best option directly.",
+    "input": (
+        "###Question: A 23-year-old pregnant woman at 22 weeks gestation presents with burning upon urination. "
+        "She states it started 1 day ago and has been worsening despite drinking more water and taking cranberry extract. "
+        "She otherwise feels well and is followed by a doctor for her pregnancy. "
+        "Her temperature is 97.7°F (36.5°C), blood pressure is 122/77 mmHg, pulse is 80/min, respirations are 19/min, and oxygen saturation is 98% on room air."
+        "Physical exam is notable for an absence of costovertebral angle tenderness and a gravid uterus. "
+        "Which of the following is the best treatment for this patient?"
+        "###Options: A. Ampicillin B. Ceftriaxone C. Doxycycline D. Nitrofurantoin"
+    )
+}
+
+prompt_batch = [prompt_input.format_map(example)]
 
 sampling_params = SamplingParams(temperature=0.8)
 
 llm = LLM(model="disi-unibo-nlp/pmc-llama-13b-awq", quantization="awq", dtype="half")
 
-outputs = llm.generate(prompts, sampling_params)
+outputs = llm.generate(prompt_batch, sampling_params)
 
 # Print the outputs.
 for output in outputs: