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| import torch | |
| import gradio as gr | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| import random | |
| device = 'cpu' | |
| def ans(question ): | |
| description='' | |
| category='' | |
| seed = random.randint(1, 10000000) | |
| print(f'Seed: {seed}') | |
| torch.manual_seed(seed) | |
| inp = tokenizer.encode(f'Вопрос: {question}\nОписание: {description}\nОтвет:',return_tensors="pt").to(device) | |
| print('question',question) | |
| gen = model.generate(inp, do_sample=True, top_p=0.9, temperature=0.86, max_new_tokens=100, repetition_penalty=1.2) #, stop_token="<eos>") | |
| gen = tokenizer.decode(gen[0]) | |
| gen = gen[:gen.index('<eos>') if '<eos>' in gen else len(gen)] | |
| gen = gen.split('Ответ:')[1] | |
| return gen | |
| # Download checkpoint: | |
| checkpoint = "its5Q/rugpt3large_mailqa" | |
| tokenizer = AutoTokenizer.from_pretrained(checkpoint) | |
| model = AutoModelForCausalLM.from_pretrained(checkpoint) | |
| model = model.eval() | |
| # Gradio | |
| title = "Ответы на главные вопросы жизни, вселенной и вообще" | |
| description = "ruGPT large дообученная на датасете https://www.kaggle.com/datasets/atleast6characterss/otvetmailru-solved-questions " | |
| article = "<p style='text-align: center'><a href='https://github.com/NeuralPushkin/MailRu_Q-A'>Github with fine-tuning ruGPT3large on QA</a></p> Cозданно при поддержке <p style='text-align: center'><a href='https://t.me/lovedeathtransformers'>Love Death Transformers</a></p>" | |
| examples = [ | |
| ["Как какать?"] | |
| ] | |
| iface = gr.Interface(fn=ans, title=title, description=description, article=article, examples=examples, inputs="text", outputs="text") | |
| if __name__ == "__main__": | |
| iface.launch() |