| import gradio as gr | |
| from paddlenlp.transformers import AutoTokenizer, AutoModelForCausalLM | |
| tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2-0.5B") | |
| model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2-0.5B", dtype="float32") | |
| def inference(input_text): | |
| print(input_text) | |
| print(type(input_text)) | |
| input_features = tokenizer(input_text, return_tensors="pd") | |
| outputs = model.generate(**input_features, max_new_tokens=128)#max_length=128) | |
| output_text = tokenizer.batch_decode(outputs[0], skip_special_tokens=True)[0] | |
| return output_text | |
| title = 'PaddlePaddle Meets LLM' | |
| description = 'What is special: underlying execution is using PaddlePaddle and PaddleNLP!' | |
| article = "<p style='text-align: center'> PaddleNLP <a href='https://github.com/PaddlePaddle/PaddleNLP'>Github Repo</a></p>" | |
| examples = ['请自我介绍一下。', '今天吃什么好呢？'] | |
| demo = gr.Interface( | |
| inference, | |
| inputs="text", | |
| outputs="text", | |
| title=title, | |
| description=description, | |
| article=article, | |
| examples=examples, | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch() | |