| import gradio as gr | |
| import torch | |
| from transformers import AutoTokenizer, AutoModelForCausalLM | |
| tokenizer = AutoTokenizer.from_pretrained( | |
| 'kakaobrain/kogpt', revision='KoGPT6B-ryan1.5b', | |
| bos_token='[BOS]', eos_token='[EOS]', unk_token='[UNK]', pad_token='[PAD]', mask_token='[MASK]' | |
| ) | |
| model = AutoModelForCausalLM.from_pretrained( | |
| 'kakaobrain/kogpt', revision='KoGPT6B-ryan1.5b', | |
| pad_token_id=tokenizer.eos_token_id, | |
| torch_dtype=torch.float16, low_cpu_mem_usage=True | |
| ).to(device='cpu', non_blocking=True) | |
| _ = model.eval() | |
| title = "KoGPT" | |
| description = "Gradio demo for KoGPT(Korean Generative Pre-trained Transformer). To use it, simply add your text, or click one of the examples to load them. Read more at the links below." | |
| article = "<p style='text-align: center'><a href='https://github.com/kakaobrain/kogpt' target='_blank'>KoGPT: KakaoBrain Korean(hangul) Generative Pre-trained Transformer</a> | <a href='https://huggingface.co/kakaobrain/kogpt' target='_blank'>Huggingface Model</a></p>" | |
| examples=[['μΈκ°μ²λΌ μκ°νκ³ , νλνλ \'μ§λ₯\'μ ν΅ν΄ μΈλ₯κ° μ΄μ κΉμ§ νμ§ λͺ»νλ']] | |
| def greet(text): | |
| prompt = text | |
| with torch.no_grad(): | |
| tokens = tokenizer.encode(prompt, return_tensors='pt').to(device='cpu', non_blocking=True) | |
| gen_tokens = model.generate(tokens, do_sample=True, temperature=0.8, max_length=64) | |
| generated = tokenizer.batch_decode(gen_tokens)[0] | |
| return generated | |
| iface = gr.Interface(fn=greet, inputs="text", outputs="text", title=title, description=description, article=article, examples=examples,enable_queue=True) | |
| iface.launch() |