handler.py

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
import os.path as op

output_dir = "./checkpoint-2"

tokenizer = AutoTokenizer.from_pretrained(output_dir)
model = AutoModelForCausalLM.from_pretrained(output_dir, device_map="auto")

# We use the tokenizer's chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating
# messages = [
#     {"role": "user", "content": "How many helicopters can a human eat in one sitting?"},
# ]

# prepare the messages for the model
# input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt").to("cuda")

# inference
# outputs = model.generate(
#         input_ids=input_ids,
#         max_new_tokens=256,
#         do_sample=True,
#         temperature=0.7,
#         top_k=50,
#         top_p=0.95
# )
# print(tokenizer.batch_decode(outputs, skip_special_tokens=True)[0])