import torch from transformers import AutoTokenizer, AutoModel, BloomTokenizerFast,BloomForCausalLM import gradio as gr modelo = 'bigscience/bloom-1b7' tokenizer = AutoTokenizer.from_pretrained(modelo) model = BloomForCausalLM.from_pretrained(modelo) def generator(prompt,max_length, temp): input_ids = tokenizer(prompt, return_tensors="pt").input_ids gen_tokens = model.generate( input_ids, do_sample=True, temperature=temp, max_length=max_length, ) gen_text = tokenizer.batch_decode(gen_tokens)[0] return gen_text def run(prompt, max_len, temp): min_len = 1 output = generator(prompt,max_len, temp) print(output) return (output,"") if __name__ == "__main__": demo = gr.Blocks() with demo: gr.Markdown(modelo) with gr.Row(): with gr.Column(): text = gr.Textbox( label="Input", value=" ", # should be set to " " when plugged into a real API ) tokens = gr.Slider(1, 250, value=50, step=1, label="Tokens to generate") temp = gr.Slider(0.1, 1.0, value=0.7, step=0.1, label="Temperature") with gr.Row(): submit = gr.Button("Submit") with gr.Column(): text_error = gr.Markdown(label="Log information") text_out = gr.Textbox(label="Output") submit.click( run, inputs=[text, tokens, temp], outputs=[text_out, text_error], ) demo.launch()