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import gradio as gr
from paddlenlp.transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2-0.5B-Instruct")
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2-0.5B-Instruct", 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 = '''
- The underlying execution framework is based on [PaddlePaddle](https://github.com/PaddlePaddle/Paddle) and [PaddleNLP](https://github.com/PaddlePaddle/PaddleNLP).
- PaddleNLP supports a wide range of open-source LLMs. Check out the [full model list](https://github.com/PaddlePaddle/PaddleNLP?tab=readme-ov-file#%E6%A8%A1%E5%9E%8B%E6%94%AF%E6%8C%81).
- We chose QWEN2-0.5B-Instruct as the model for this use case due to limited computational resources.
- [ERNIE 4.5](https://yiyan.baidu.com/) was trained with PaddlePaddle, [give it a try](https://huggingface.co./spaces/PaddlePaddle/ernie_demo)!
'''
examples = ['请自我介绍一下。']
demo = gr.Interface(
inference,
inputs="text",
outputs="text",
title=title,
description=description,
examples=examples,
)
if __name__ == "__main__":
demo.launch()
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