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---
library_name: transformers
tags: []
---
## INFERENCE
```Python
import time
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
finetuned_model = AutoModelForCausalLM.from_pretrained("Mr-Vicky-01/GPT-QnA")
tokenizer = AutoTokenizer.from_pretrained("Mr-Vicky-01/GPT-QnA")
alpaca_prompt = """Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
what is depresssion how to overcome
### Response:
"""
s = time.time()
prompt = alpaca_prompt
encodeds = tokenizer(prompt, return_tensors="pt",truncation=True).input_ids
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
finetuned_model.to(device)
inputs = encodeds.to(device)
# Increase max_new_tokens if needed
generated_ids = finetuned_model.generate(inputs, max_new_tokens=256, temperature=0.1, top_p=0.90, do_sample=False,pad_token_id=50259,eos_token_id=50259,num_return_sequences=1)
print(tokenizer.decode(generated_ids[0]).split('### Response:')[1].split('<eos>')[0].strip())
e = time.time()
print(f'time taken:{e-s}')
``` |