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import torch
import time
class ModelHandler:
def __init__(self, model, tokenizer):
self.model = model
self.tokenizer = tokenizer
def generate_response(self, conversation):
inputs = self.tokenizer(conversation, return_tensors="pt", truncation=True, max_length=1024)
start_time = time.time()
output = ""
with torch.no_grad():
for _ in range(150): # Increased range for potentially longer responses
generated = self.model.generate(
**inputs,
max_new_tokens=1,
do_sample=True,
top_k=50,
top_p=0.95
)
new_token = generated[0, -1].item()
new_word = self.tokenizer.decode([new_token])
output += new_word
inputs = self.tokenizer(conversation + output, return_tensors="pt", truncation=True, max_length=1024)
if time.time() - start_time >= 0.01:
yield output
start_time = time.time()
if new_token == self.tokenizer.eos_token_id:
break
return output.strip()
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