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import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from typing import Optional, Dict, Any
import logging
import asyncio
# Configure logging
logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
logger = logging.getLogger(__name__)
class Charm15Chatbot:
def __init__(
self,
model_path: str,
device: Optional[str] = None,
tokenizer_kwargs: Optional[Dict[str, Any]] = None,
model_kwargs: Optional[Dict[str, Any]] = None,
):
"""
Initialize the chatbot.
Args:
model_path (str): Path or name of the pre-trained model.
device (str, optional): Device to run the model on (e.g., "cuda" or "cpu"). Defaults to "cuda" if available.
tokenizer_kwargs (dict, optional): Additional arguments for the tokenizer.
model_kwargs (dict, optional): Additional arguments for the model.
"""
self.device = device or ("cuda" if torch.cuda.is_available() else "cpu")
self.tokenizer_kwargs = tokenizer_kwargs or {}
self.model_kwargs = model_kwargs or {}
# Load tokenizer and model
logger.info(f"Loading model and tokenizer from {model_path}...")
self.tokenizer = AutoTokenizer.from_pretrained(model_path, **self.tokenizer_kwargs)
self.model = AutoModelForCausalLM.from_pretrained(model_path, **self.model_kwargs).to(self.device)
self.model.eval()
logger.info("Model and tokenizer loaded successfully.")
def generate_response(
self,
input_text: str,
max_length: int = 512,
temperature: float = 0.7,
top_p: float = 0.9,
top_k: Optional[int] = None,
repetition_penalty: float = 1.0,
**kwargs,
) -> str:
"""
Generate a response to the input text.
Args:
input_text (str): The input prompt.
max_length (int): Maximum length of the generated text.
temperature (float): Sampling temperature (higher = more random).
top_p (float): Top-p (nucleus) sampling.
top_k (int): Top-k sampling.
repetition_penalty (float): Penalty for repeating tokens.
**kwargs: Additional arguments for model.generate().
Returns:
str: The generated response.
"""
try:
inputs = self.tokenizer(
input_text,
return_tensors="pt",
truncation=True,
max_length=1024,
).to(self.device)
with torch.no_grad():
output = self.model.generate(
**inputs,
max_length=max_length,
temperature=temperature,
top_p=top_p,
top_k=top_k,
repetition_penalty=repetition_penalty,
pad_token_id=self.tokenizer.eos_token_id,
**kwargs,
)
response = self.tokenizer.decode(output[0], skip_special_tokens=True)
logger.info("Response generated successfully.")
return response
except Exception as e:
logger.error(f"Error generating response: {e}")
raise
async def async_generate(
self,
input_text: str,
max_length: int = 512,
temperature: float = 0.7,
top_p: float = 0.9,
top_k: Optional[int] = None,
repetition_penalty: float = 1.0,
**kwargs,
) -> str:
"""
Asynchronously generate a response to the input text.
Args:
input_text (str): The input prompt.
max_length (int): Maximum length of the generated text.
temperature (float): Sampling temperature (higher = more random).
top_p (float): Top-p (nucleus) sampling.
top_k (int): Top-k sampling.
repetition_penalty (float): Penalty for repeating tokens.
**kwargs: Additional arguments for model.generate().
Returns:
str: The generated response.
"""
return await asyncio.to_thread(
self.generate_response,
input_text,
max_length=max_length,
temperature=temperature,
top_p=top_p,
top_k=top_k,
repetition_penalty=repetition_penalty,
**kwargs,
) |