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| import gradio as gr | |
| from transformers import T5Tokenizer, T5ForConditionalGeneration | |
| # xl size run out of memory on 16GB vm | |
| # All the models have input length of 512 tokens and outputs of 512 tokens | |
| # small 250M param | |
| # base | |
| # large | |
| # xl | |
| # xxl | |
| model_name = "large" | |
| tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-" + model_name) | |
| model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-" + model_name) | |
| title = "" | |
| def get_examples (): | |
| return [ | |
| ["Peter goes to the store to buy a soda. The soda costs $.25 an ounce. \ | |
| He brought $2 with him and leaves with $.50. How many ounces of soda did he buy?", | |
| "How much did Peter spend on soda? ** He spend $1.5 on soda because 2 - .5 = <<2-.5=1.5>>1.5 \ | |
| How many ounces of soda did Peter buy? ** He bought 6 ounces of soda because 1.5 / .25 = <<6=6>>6 #### 6" | |
| ], | |
| ["Krystian works in the library. He borrows an average of 40 books every day. \ | |
| Every Friday, his number of borrowed books is about 40% higher than the daily average. How many books does he borrow in a week if the library is open from Monday to Friday?" | |
| ,"How many books does Krystian borrow on Friday? ** The number of books borrowed \ | |
| on Friday is higher by 40 * 40/100 = <<40*40/100=16>>16 books. How many books does Krystian borrow in a week? ** There are 5 days from Monday to Friday inclusive, so Krystian borrows an average of 5 * 40 = <<5*40=200>>200 books during that time. How many books does Krystian borrow in a week? ** With Friday's increase in borrowings, during one week Krystian borrows 200 + 16 = <<200+16=216>>216 books."] | |
| , ["Jane had $60 but gave $30 to dave and went to movies and spend $2. How much money does Jane has left? Answer by reasoning step by step:", "$28"], | |
| ["Cat is a friend of a Dog. Are cat and Dog friends?", "Yes"] | |
| ] | |
| def text2text(input_text): | |
| input_ids = tokenizer(input_text, return_tensors="pt").input_ids | |
| input_num_tokens = input_ids.shape[1] | |
| print( "Number of input tokens: " + str(input_num_tokens)) | |
| print("Length of input: " + str(len(input_text))) | |
| # Does not seem to care if it goes over 512... humm... | |
| outputs = model.generate(input_ids, max_new_tokens=512) | |
| # Remove <pad> and </s> eof sequence tokens | |
| model_output_text = tokenizer.decode(outputs[0],skip_special_tokens=True) | |
| print("Number of output tokens: " + str(outputs.shape[1])) | |
| print("length of output: " + str(len(model_output_text))) | |
| print("Output: " + model_output_text) | |
| output_text = input_text + model_output_text | |
| return output_text | |
| with gr.Blocks() as demo: | |
| gr.Markdown( | |
| """ | |
| # Flan T5 Large Demo | |
| 780M parameter Large language model fine tuned on diverse tasks. | |
| Prompt the model in the Input box. Models output is appended to input. To get additional generation hit submit again. | |
| """) | |
| txt_in = gr.Textbox(label="Input", lines=8) | |
| correct_label = gr.Label(label="Correct") | |
| # txt_out = gr.Textbox(value="", label="Output", lines=4) | |
| btn = gr.Button(value="Submit") | |
| # Send back to inputs | |
| btn.click(text2text, inputs=[txt_in], outputs=[txt_in]) | |
| gr.Examples( | |
| examples=get_examples(), | |
| inputs=[txt_in,correct_label] | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch() |