| import gradio as gr | |
| from transformers import AutoTokenizer, AutoModelForCausalLM | |
| from transformers import pipeline | |
| # tokenizer = AutoTokenizer.from_pretrained("cerebras/Cerebras-GPT-13B") | |
| # model = AutoModelForCausalLM.from_pretrained("cerebras/Cerebras-GPT-13B") | |
| # tokenizer = AutoTokenizer.from_pretrained("cerebras/Cerebras-GPT-2.7B") | |
| model = AutoModelForCausalLM.from_pretrained("cerebras/Cerebras-GPT-2.7B") | |
| text = "Generative AI is " | |
| # pipe = pipeline("text-generation", model=model, tokenizer=tokenizer) | |
| pipe = pipeline("text-generation", model=model) | |
| def greet(speech): | |
| generated_text = pipe(speech, max_length=50, do_sample=False, no_repeat_ngram_size=2)[0] | |
| return generated_text['generated_text'] | |
| # def greet(speech): | |
| # inputs = tokenizer(speech, return_tensors="pt") | |
| # outputs = model.generate(**inputs, num_beams=5, | |
| # max_new_tokens=50, early_stopping=True, | |
| # no_repeat_ngram_size=2) | |
| # text_output = tokenizer.batch_decode(outputs, skip_special_tokens=True) | |
| # return text_output[0] | |
| iface = gr.Interface(fn=greet, inputs="text", outputs="text") | |
| iface.launch(share=True) |