Create app.py

app.py

@@ -0,0 +1,77 @@
+from fastapi import FastAPI
+import time
+
+
+from transformers import AutoModelForCausalLM, AutoTokenizer
+device = "cpu" # the device to load the model onto
+
+model = AutoModelForCausalLM.from_pretrained(
+    "Qwen/Qwen2-0.5B-Instruct",
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2-0.5B-Instruct")
+
+app = FastAPI()
+
+@app.get("/")
+async def read_root():
+    return {"Hello": "World!"}
+start_time = time.time()
+messages = [
+        {"role": "system", "content": "You are a helpful assistant, Sia. You are developed by Sushma. You will response in polity, clear, brief and in short length."},
+        {"role": "user", "content": "Who are you?"},
+        {"role": "assistant", "content": "I am Sia, a small language model created by Sushma. I am here to assist you. How can I help you today?"},
+        {"role": "user", "content": "Hi, How are you?"}
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+model_inputs = tokenizer([text], return_tensors="pt").to(device)
+    
+generated_ids = model.generate(
+    model_inputs.input_ids,
+    max_new_tokens=64
+)
+generated_ids = [
+    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+]
+    
+response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+print(response)
+end_time = time.time()
+time_taken = end_time - start_time 
+print(time_taken)
+
+
+@app.get("/test")
+async def read_droot():
+    messages = [
+        {"role": "system", "content": "You are a helpful assistant, Sia. You are developed by Sushma. You will response in polity, clear, brief and in short length."},
+        {"role": "user", "content": "Who are you?"},
+        {"role": "assistant", "content": "I am Sia, a small language model created by Sushma. I am here to assist you. How can I help you today?"},
+        {"role": "user", "content": "Hi, How are you?"}
+    ]
+    text = tokenizer.apply_chat_template(
+        messages,
+        tokenize=False,
+        add_generation_prompt=True
+    )
+    model_inputs = tokenizer([text], return_tensors="pt").to(device)
+        
+    generated_ids = model.generate(
+        model_inputs.input_ids,
+        max_new_tokens=64
+    )
+    generated_ids = [
+        output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+    ]
+        
+    response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+    print(response)
+    end_time = time.time()
+    time_taken = end_time - start_time 
+    print(time_taken)
+    return {"Hello": "World!"}