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| from transformers import GPTJForCausalLM, GPT2Tokenizer | |
| import torch | |
| # Загрузка токенизатора и модели | |
| tokenizer = GPT2Tokenizer.from_pretrained("EleutherAI/gpt-j-6B") | |
| model = GPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B") | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| model = model.to(device) | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| model = model.to(device) | |
| from transformers import MarianMTModel, MarianTokenizer | |
| # Функции для перевода | |
| def translate_to_english(text): | |
| tokenizer = MarianTokenizer.from_pretrained("Helsinki-NLP/opus-mt-ru-en") | |
| model = MarianMTModel.from_pretrained("Helsinki-NLP/opus-mt-ru-en") | |
| translated = model.generate(**tokenizer.prepare_seq2seq_batch([text], return_tensors="pt")) | |
| return tokenizer.decode(translated[0]) | |
| def translate_to_russian(text): | |
| tokenizer = MarianTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-ru") | |
| model = MarianMTModel.from_pretrained("Helsinki-NLP/opus-mt-en-ru") | |
| translated = model.generate(**tokenizer.prepare_seq2seq_batch([text], return_tensors="pt")) | |
| return tokenizer.decode(translated[0]) | |
| def generate_response(input_text): | |
| # Кодирование входного текста и генерация ответа | |
| input_ids = tokenizer.encode(input_text, return_tensors='pt') | |
| output = model.generate(input_ids, max_length=1500, num_return_sequences=1, pad_token_id=50256, | |
| num_beams=5, early_stopping=True) | |
| # Декодирование и возвращение ответа | |
| response = tokenizer.decode(output[0], skip_special_tokens=True) | |
| return response | |
| # Тестирование функции | |
| input_text = "What is the capital of France?" | |
| response = generate_response(input_text) | |
| print(response) | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| model = model.to(device) | |
| user_input = "" | |
| while user_input.lower() != 'exit': | |
| user_input = input("You: ") | |
| if user_input.lower() != 'exit': | |
| user_input_english = translate_to_english(user_input) | |
| response_english = generate_response(user_input_english) | |
| response_russian = translate_to_russian(response_english) | |
| print(f"Bot: {response_russian}") | |
| import gradio as gr | |
| def predict(message, history): | |
| user_input_english = translate_to_english(message) | |
| response_english = generate_response(user_input_english) | |
| response_russian = translate_to_russian(response_english) | |
| return response_russian | |
| demo = gr.ChatInterface( | |
| predict, | |
| title='LLM for MyChatENRU' | |
| ) | |
| demo.launch() | |