| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| import torch | |
| class ChatModel: | |
| def __init__(self): | |
| self.tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1", token=True) | |
| self.model = AutoModelForCausalLM.from_pretrained( | |
| "mistralai/Mistral-7B-v0.1", | |
| torch_dtype=torch.float16, | |
| token=True | |
| ) | |
| async def generate_response(self, input_text): | |
| inputs = self.tokenizer(input_text, return_tensors="pt").to(self.model.device) | |
| outputs = self.model.generate( | |
| **inputs, | |
| max_length=100, | |
| num_return_sequences=1, | |
| temperature=0.7 | |
| ) | |
| response = self.tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| return response |