User trying to import a module that's not installed. Instead of bumping him and telling her to use a different workspace on which the module *is* installed, use compute resources to try and install.. nuts.. Starting with : from transformers import TFAutoModelForSeq2SeqLM, AutoTokenizer import gradio as gr model = TFAutoModelForSeq2SeqLM.from_pretrained("google/flan-t5-small") tokenizer = AutoTokenizer.from_pretrained("google/flan-t5-small") def gen_text(input_string, max_length): inputs = tokenizer(input_string, return_tensors="pt") outputs = model.generate(**inputs, max_length=max_length) final_text = tokenizer.batch_decode(outputs[0], skip_special_tokens=True) return (final_text) demo = gr.Interface( fn=gen_text, ...
User trying to import a module that's not installed. Instead of bumping him and telling her to use a different workspace on which the module *is* installed, use compute resources to try and install.. nuts.. Starting with : from transformers import TFAutoModelForSeq2SeqLM, AutoTokenizer import gradio as gr model = TFAutoModelForSeq2SeqLM.from_pretrained("google/flan-t5-small") tokenizer = AutoTokenizer.from_pretrained("google/flan-t5-small") def gen_text(input_string, max_length): inputs = tokenizer(input_string, return_tensors="pt") outputs = model.generate(**inputs, max_length=max_length) final_text = tokenizer.batch_decode(outputs[0], skip_special_tokens=True) return (final_text) demo = gr.Interface( fn=gen_text, ...