Create app.py

app.py

@@ -0,0 +1,78 @@
+from fastapi import FastAPI
+import torch
+
+from transformers import AutoModelForCausalLM, AutoTokenizer
+device = "cpu"
+
+tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2-0.5B-Instruct")
+
+model = AutoModelForCausalLM.from_pretrained(
+    "Qwen/Qwen2-0.5B-Instruct",
+    device_map="auto"
+)
+
+model1 = AutoModelForCausalLM.from_pretrained(
+    "Qwen/Qwen2-1.5B-Instruct",
+    device_map="auto"
+)
+
+app = FastAPI()
+
+@app.get("/")
+async def read_root():
+    return {"Hello": "World!"}
+
+
+@app.post("/model")
+async def model(data: dict):
+    prompt = data.get("prompt")
+    messages = [
+        {"role": "system", "content": "You are a helpful assistant, Sia, developed by Sushma. You will response in polity and brief."},
+        {"role": "user", "content": "Who are you?"},
+        {"role": "assistant", "content": "I am Sia, a small language model created by Sushma."},
+        {"role": "user", "content": prompt}
+    ]
+    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,
+        do_sample=True
+    )
+    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]
+
+    return response
+
+@app.post("/model1")
+async def model1(data: dict):
+    prompt = data.get("prompt")
+    messages = [
+        {"role": "system", "content": "You are a helpful assistant, Sia, developed by Sushma. You will response in polity and brief."},
+        {"role": "user", "content": "Who are you?"},
+        {"role": "assistant", "content": "I am Sia, a small language model created by Sushma."},
+        {"role": "user", "content": prompt}
+    ]
+    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,
+        do_sample=True
+    )
+    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]
+
+    return response