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| import torch | |
| import gradio as gr | |
| from transformers import GPT2LMHeadModel, GPT2Tokenizer | |
| tokenizer = GPT2Tokenizer.from_pretrained("sberbank-ai/mGPT") | |
| model = GPT2LMHeadModel.from_pretrained("sberbank-ai/mGPT") | |
| #model.cuda() | |
| #model.eval() | |
| description = "Multilingual generation with mGPT" | |
| title = "Generate your own example" | |
| examples = [["""English: The vase with flowers is on the table.\nFinnish translation:""", "In May we celebrate "]] | |
| article = ( | |
| "<p style='text-align: center'>" | |
| "<a href='https://github.com/ai-forever/mgpt'>GitHub</a> " | |
| "</p>" | |
| ) | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| fp16 = device != 'cpu' | |
| def generate(prompt: str): | |
| input_ids = tokenizer.encode(prompt, return_tensors="pt").to(device) | |
| out = model.generate(input_ids, | |
| min_length=100, | |
| max_length=200, | |
| top_p=0.8, | |
| top_k=0, | |
| no_repeat_ngram_size=5 | |
| ) | |
| generated_text = list(map(tokenizer.decode, out))[0] | |
| return generated_text | |
| interface = gr.Interface.load("huggingface/sberbank-ai/mGPT", | |
| description=description, | |
| examples=examples, | |
| fn=generate, | |
| inputs="text", | |
| outputs='text', | |
| thumbnail = 'https://habrastorage.org/r/w1560/getpro/habr/upload_files/26a/fa1/3e1/26afa13e1d1a56f54c7b0356761af7b8.png', | |
| theme = "peach", | |
| article = article | |
| ) | |
| interface.launch(enable_queue=True) |