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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!"}
def modelResp(promt):
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": f"{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
def modelResp1(promt):
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": f"{prompt}"}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model1.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("/modelapi")
async def modelApi(data: dict):
prompt = data.get("prompt")
response = modelResp(prompt)
return response
@app.post("/modelapi1")
async def modelApi1(data: dict):
prompt = data.get("prompt")
response = modelResp1(prompt)
return response |