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| import gradio as gr | |
| import random | |
| import os | |
| from huggingface_hub import login | |
| from transformers import pipeline | |
| from transformers import GPT2Tokenizer, GPT2LMHeadModel | |
| login(os.environ["HF_TOKEN"]) | |
| #https://huggingface.co/facebook/opt-1.3b | |
| #generator = pipeline('text-generation', model="microsoft/DialoGPT-medium") | |
| tokenizer = GPT2Tokenizer.from_pretrained('microsoft/DialoGPT-medium') | |
| original_model = GPT2LMHeadModel.from_pretrained('microsoft/DialoGPT-medium') | |
| untethered_model = GPT2LMHeadModel.from_pretrained('zmbfeng/untethered_20240225_epochs_500') | |
| untethered_paraphrased_model = GPT2LMHeadModel.from_pretrained('zmbfeng/untethered_20240227_epochs_350') | |
| def create_response_untethered_paraphrased(input_str, | |
| # num_beams, | |
| num_return_sequences, | |
| temperature, | |
| repetition_penalty, | |
| top_p, | |
| # top_k, | |
| do_sample): | |
| print("input_str="+input_str) | |
| # num_beams = int(num_beams) | |
| # print("num_beams=" + str(num_beams)) | |
| num_return_sequences=int(num_return_sequences) | |
| print("num_return_sequences" + str(num_return_sequences)) | |
| print("top_p" + str(top_p)) | |
| # top_k=int(top_k) | |
| #print("top_k" + str(top_k)) | |
| print("repetition_penalty" + str(repetition_penalty)) | |
| print("temperature" + str(temperature)) | |
| print("do_sample" + str(do_sample)) | |
| if not do_sample: | |
| num_beams = 1 | |
| print("num_beams=" + str(num_beams)) | |
| #output_raw= generator(input_str) | |
| """print (output_raw)""" | |
| #output_str = output_raw[0]['generated_text'] | |
| #output_str = output_str.replace("\n", "") | |
| #output_str = output_str.replace(input_str, "") | |
| #output_str = tokenizer.decode(model.generate(**tokenizer("What are John West's hobbies?"+tokenizer.eos_token,return_tensors="pt",max_length=200))[0]) | |
| # output_str = tokenizer.decode(original_model.generate(**tokenizer(input_str+tokenizer.eos_token,return_tensors="pt",max_length=200), | |
| # num_beams=num_beams, | |
| # num_return_sequences=num_return_sequences)[0]) | |
| #input_ids = tokenizer.encode(input_str + tokenizer.eos_token, return_tensors="pt") | |
| encoded = tokenizer.encode_plus(input_str + tokenizer.eos_token, return_tensors="pt") | |
| input_ids = encoded["input_ids"] | |
| attention_mask = encoded["attention_mask"] | |
| #output_ids = fine_tuned_model.generate(input_ids,do_sample=True, max_length=100, temperature=0.2, top_p=0.9, repetition_penalty=1.5,num_return_sequences=6) | |
| #output_ids = untethered_paraphrased_model.generate(input_ids,do_sample=do_sample, max_length=100, temperature=temperature, top_p=top_p, top_k=top_k, repetition_penalty=repetition_penalty,num_return_sequences=num_return_sequences, num_beams = num_beams) | |
| output_ids = untethered_paraphrased_model.generate(input_ids,pad_token_id=tokenizer.eos_token_id,do_sample=True,attention_mask=attention_mask, max_length=100, temperature=temperature, top_p=top_p, repetition_penalty=repetition_penalty,num_return_sequences=num_return_sequences) | |
| outputs = "" | |
| for output_id in output_ids: | |
| output = tokenizer.decode(output_id, skip_special_tokens=True) | |
| outputs=outputs+output+"<br/>" | |
| return outputs | |
| def create_response_untethered(input_str, | |
| # num_beams, | |
| num_return_sequences, | |
| temperature, | |
| repetition_penalty, | |
| top_p, | |
| # top_k, | |
| do_sample, | |
| model_name): | |
| print("input_str="+input_str) | |
| print("model_name="+str(model_name)) | |
| # num_beams = int(num_beams) | |
| # print("num_beams=" + str(num_beams)) | |
| num_return_sequences=int(num_return_sequences) | |
| print("num_return_sequences" + str(num_return_sequences)) | |
| print("top_p" + str(top_p)) | |
| # top_k=int(top_k) | |
| # print("top_k" + str(top_k)) | |
| print("repetition_penalty" + str(repetition_penalty)) | |
| print("temperature" + str(temperature)) | |
| print("do_sample" + str(do_sample)) | |
| if not do_sample: | |
| num_beams = 1 | |
| print("num_beams=" + str(num_beams)) | |
| #output_raw= generator(input_str) | |
| """print (output_raw)""" | |
| #output_str = output_raw[0]['generated_text'] | |
| #output_str = output_str.replace("\n", "") | |
| #output_str = output_str.replace(input_str, "") | |
| #output_str = tokenizer.decode(model.generate(**tokenizer("What are John West's hobbies?"+tokenizer.eos_token,return_tensors="pt",max_length=200))[0]) | |
| # output_str = tokenizer.decode(original_model.generate(**tokenizer(input_str+tokenizer.eos_token,return_tensors="pt",max_length=200), | |
| # num_beams=num_beams, | |
| # num_return_sequences=num_return_sequences)[0]) | |
| #input_ids = tokenizer.encode(input_str + tokenizer.eos_token, return_tensors="pt") | |
| encoded = tokenizer.encode_plus(input_str + tokenizer.eos_token, return_tensors="pt") | |
| input_ids = encoded["input_ids"] | |
| attention_mask = encoded["attention_mask"] | |
| #output_ids = fine_tuned_model.generate(input_ids,do_sample=True, max_length=100, temperature=0.2, top_p=0.9, repetition_penalty=1.5,num_return_sequences=6) | |
| output_ids = untethered_model.generate(input_ids,pad_token_id=tokenizer.eos_token_id,do_sample=do_sample, attention_mask=attention_mask, max_length=100, temperature=temperature, top_p=top_p, repetition_penalty=repetition_penalty,num_return_sequences=num_return_sequences ) | |
| outputs = "" | |
| for output_id in output_ids: | |
| output = tokenizer.decode(output_id, skip_special_tokens=True) | |
| outputs=outputs+output+"\\n" | |
| return outputs | |
| def create_response_original(input_str, | |
| # num_beams, | |
| num_return_sequences, | |
| temperature, | |
| repetition_penalty, | |
| top_p, | |
| # top_k, | |
| do_sample): | |
| print("input_str="+input_str) | |
| # num_beams = int(num_beams) | |
| # print("num_beams=" + str(num_beams)) | |
| num_return_sequences=int(num_return_sequences) | |
| print("num_return_sequences" + str(num_return_sequences)) | |
| print("top_p" + str(top_p)) | |
| # top_k=int(top_k) | |
| # print("top_k" + str(top_k)) | |
| print("repetition_penalty" + str(repetition_penalty)) | |
| print("temperature" + str(temperature)) | |
| print("do_sample" + str(do_sample)) | |
| if not do_sample: | |
| num_beams = 1 | |
| print("num_beams=" + str(num_beams)) | |
| #output_raw= generator(input_str) | |
| """print (output_raw)""" | |
| #output_str = output_raw[0]['generated_text'] | |
| #output_str = output_str.replace("\n", "") | |
| #output_str = output_str.replace(input_str, "") | |
| #output_str = tokenizer.decode(model.generate(**tokenizer("What are John West's hobbies?"+tokenizer.eos_token,return_tensors="pt",max_length=200))[0]) | |
| # output_str = tokenizer.decode(original_model.generate(**tokenizer(input_str+tokenizer.eos_token,return_tensors="pt",max_length=200), | |
| # num_beams=num_beams, | |
| # num_return_sequences=num_return_sequences)[0]) | |
| #input_ids = tokenizer.encode(input_str + tokenizer.eos_token, return_tensors="pt") | |
| encoded = tokenizer.encode_plus(input_str + tokenizer.eos_token, return_tensors="pt") | |
| input_ids = encoded["input_ids"] | |
| attention_mask = encoded["attention_mask"] | |
| #output_ids = fine_tuned_model.generate(input_ids,do_sample=True, max_length=100, temperature=0.2, top_p=0.9, repetition_penalty=1.5,num_return_sequences=6) | |
| output_ids = original_model.generate(input_ids,pad_token_id=tokenizer.eos_token_id,do_sample=do_sample,attention_mask=attention_mask, max_length=100, temperature=temperature, top_p=top_p, repetition_penalty=repetition_penalty,num_return_sequences=num_return_sequences) | |
| outputs = "" | |
| for output_id in output_ids: | |
| output = tokenizer.decode(output_id, skip_special_tokens=True) | |
| outputs=outputs+output+"\\n" | |
| return outputs | |
| def create_response_fine_tuned(input_str): | |
| #output_raw= generator(input_str) | |
| """print (output_raw)""" | |
| #output_str = output_raw[0]['generated_text'] | |
| #output_str = output_str.replace("\n", "") | |
| #output_str = output_str.replace(input_str, "") | |
| #output_str = tokenizer.decode(model.generate(**tokenizer("What are John West's hobbies?"+tokenizer.eos_token,return_tensors="pt",max_length=200))[0]) | |
| output_str = tokenizer.decode(fine_tuned_model.generate(**tokenizer(input_str+tokenizer.eos_token,return_tensors="pt",max_length=200))[0]) | |
| return (output_str) | |
| interface1 = gr.Interface(fn=create_response_original, | |
| title="original", | |
| description="original language model, no fine tuning", | |
| examples=[ | |
| ["What is death?",5,0.2,1.5,0.9,True], # The first example | |
| ["One of the best teachers in all of life turns out to be what?",5,0.2,1.5,0.9,True], # The second example | |
| ["what is your most meaningful relationship?",5,0.2,1.5,0.9,True], # The third example | |
| ["What actually gives life meaning?",5,0.2,1.5,0.9,True] | |
| ], | |
| inputs=[ | |
| gr.Textbox(label="input text here", lines=3), | |
| # gr.Number(label="num_beams (integer) explores the specified number of possible outputs and selects the most " + | |
| # "likely ones (specified in num_beams)", value=7), | |
| gr.Number(label="num_return_sequences (integer) the number of outputs selected from num_beams possible output", | |
| value=5), | |
| gr.Number( | |
| label="temperature (decimal) controls the creativity or randomness of the output. A higher temperature" + | |
| " (e.g., 0.9) results in more diverse and creative output, while a lower temperature (e.g., 0.2)" + | |
| " makes the output more deterministic and focused", | |
| value=0.2), | |
| gr.Number(label="repetition_penalty (decimal) penalizes words that have already appeared in the output, " + | |
| "making them less likely to be generated again. A higher repetition_penalty (e.g., 1.5) results" + | |
| "in more varied and non-repetitive output.", | |
| value=1.5), | |
| gr.Number(label="top_p (decimal) the model will only consider the words that have a high enough probability" + | |
| " to reach a certain threshold", | |
| value=0.9), | |
| # gr.Number(label="top_k (integer) The number of highest probability vocabulary word will be considered" + | |
| # "This means that only the tokens with the highest probabilities are considered for sampling" + | |
| # "This reduces the diversity of the generated sequences, "+ | |
| # "but also makes them more likely to be coherent and fluent.", | |
| # value=50), | |
| gr.Checkbox(label="do_sample. If is set to False, num_return_sequences must be 1 because the generate function will use greedy decoding, " + | |
| "which means that it will select the word with the highest probability at each step. " + | |
| "This results in a deterministic and fluent output, but it might also lack diversity and creativity" + | |
| "If is set to True, the generate function will use stochastic sampling, which means that it will randomly" + | |
| " select a word from the probability distribution at each step. This results in a more diverse and creative" + | |
| " output, but it might also introduce errors and inconsistencies ", value=True) | |
| ], outputs=[gr.Textbox(label="output response", lines=30)]) | |
| interface2 = gr.Interface(fn=create_response_untethered, | |
| title="untethered", | |
| description="untethered fine tuning", | |
| examples=[ | |
| ["What is death?",5,0.2,1.5,0.9,True], # The first example | |
| ["One of the best teachers in all of life turns out to be what?",5,0.2,1.5,0.9,True], # The second example | |
| ["what is your most meaningful relationship?",5,0.2,1.5,0.9,True], # The third example | |
| ["What actually gives life meaning?",5,0.2,1.5,0.9,True] | |
| ], | |
| inputs=[ | |
| gr.Textbox(label="input text here", lines=3), | |
| # gr.Number(label="num_beams (integer) explores the specified number of possible outputs and selects the most " + | |
| # "likely ones (specified in num_beams)", value=7), | |
| gr.Number(label="num_return_sequences (integer) the number of outputs selected from num_beams possible output", | |
| value=5), | |
| gr.Number( | |
| label="temperature (decimal) controls the creativity or randomness of the output. A higher temperature" + | |
| " (e.g., 0.9) results in more diverse and creative output, while a lower temperature (e.g., 0.2)" + | |
| " makes the output more deterministic and focused", | |
| value=0.2), | |
| gr.Number(label="repetition_penalty (decimal) penalizes words that have already appeared in the output, " + | |
| "making them less likely to be generated again. A higher repetition_penalty (e.g., 1.5) results" + | |
| "in more varied and non-repetitive output.", | |
| value=1.5), | |
| gr.Number(label="top_p (decimal) the model will only consider the words that have a high enough probability" + | |
| " to reach a certain threshold", | |
| value=0.9), | |
| # gr.Number(label="top_k (integer) The number of highest probability vocabulary word will be considered" + | |
| # "This means that only the tokens with the highest probabilities are considered for sampling" + | |
| # "This reduces the diversity of the generated sequences, "+ | |
| # "but also makes them more likely to be coherent and fluent.", | |
| # value=50), | |
| gr.Checkbox(label="do_sample. If is set to False, num_return_sequences must be 1 because the generate function will use greedy decoding, " + | |
| "which means that it will select the word with the highest probability at each step. " + | |
| "This results in a deterministic and fluent output, but it might also lack diversity and creativity" + | |
| "If is set to True, the generate function will use stochastic sampling, which means that it will randomly" + | |
| " select a word from the probability distribution at each step. This results in a more diverse and creative" + | |
| " output, but it might also introduce errors and inconsistencies ", value=True), | |
| gr.Textbox(label="model", lines=3, value="untethered_model_name") | |
| #,visible=False | |
| ], outputs=[gr.Textbox(label="output response", lines=30)]) | |
| interface3 = gr.Interface(fn=create_response_untethered_paraphrased, | |
| title="untethered paraphrased", | |
| description="untethered paraphrased fine tuning", | |
| examples=[ | |
| ["What is death?",5,0.2,1.5,0.9,True], # The first example | |
| ["One of the best teachers in all of life turns out to be what?",5,0.2,1.5,0.9,True], # The second example | |
| ["what is your most meaningful relationship?",5,0.2,1.5,0.9,True], # The third example | |
| ["What actually gives life meaning?",5,0.2,1.5,0.9,True] | |
| ], | |
| inputs=[ | |
| gr.Textbox(label="input text here", lines=3), | |
| # gr.Number(label="num_beams (integer) explores the specified number of possible outputs and selects the most " + | |
| # "likely ones (specified in num_beams)", value=7), | |
| gr.Number(label="num_return_sequences (integer) the number of outputs selected from num_beams possible output", | |
| value=5), | |
| gr.Number( | |
| label="temperature (decimal) controls the creativity or randomness of the output. A higher temperature" + | |
| " (e.g., 0.9) results in more diverse and creative output, while a lower temperature (e.g., 0.2)" + | |
| " makes the output more deterministic and focused", | |
| value=0.2), | |
| gr.Number(label="repetition_penalty (decimal) penalizes words that have already appeared in the output, " + | |
| "making them less likely to be generated again. A higher repetition_penalty (e.g., 1.5) results" + | |
| "in more varied and non-repetitive output.", | |
| value=1.5), | |
| gr.Number(label="top_p (decimal) the model will only consider the words that have a high enough probability" + | |
| " to reach a certain threshold", | |
| value=0.9), | |
| # gr.Number(label="top_k (integer) The number of highest probability vocabulary word will be considered" + | |
| # "This means that only the tokens with the highest probabilities are considered for sampling" + | |
| # "This reduces the diversity of the generated sequences, "+ | |
| # "but also makes them more likely to be coherent and fluent.", | |
| # value=50), | |
| gr.Checkbox(label="do_sample. If is set to False, num_return_sequences must be 1 because the generate function will use greedy decoding, " + | |
| "which means that it will select the word with the highest probability at each step. " + | |
| "This results in a deterministic and fluent output, but it might also lack diversity and creativity" + | |
| "If is set to True, the generate function will use stochastic sampling, which means that it will randomly" + | |
| " select a word from the probability distribution at each step. This results in a more diverse and creative" + | |
| " output, but it might also introduce errors and inconsistencies ", value=True) | |
| ], outputs="html") | |
| #[gr.Textbox(label="output response", lines=30)] | |
| #interface2 = gr.Interface(fn=create_response_fine_tuned, inputs="text", outputs="text", title="Fine Tuned") | |
| demo = gr.TabbedInterface([interface1, interface2, interface3], ["Original", "Untethered", "Untethered paraphrased"]) | |
| #demo = gr.TabbedInterface([interface1, interface3], ["Original", "Untethered paraphrased"]) | |
| # with gr.Blocks() as demo: | |
| # with gr.Row(): | |
| # | |
| demo.launch() |