all three models

app.py

@@ -9,8 +9,97 @@ login(os.environ["HF_TOKEN"])
 #generator = pipeline('text-generation', model="microsoft/DialoGPT-medium")
 tokenizer = GPT2Tokenizer.from_pretrained('microsoft/DialoGPT-medium')
 original_model = GPT2LMHeadModel.from_pretrained('microsoft/DialoGPT-medium')
-fine_tuned_model = GPT2LMHeadModel.from_pretrained('zmbfeng/FineTune-1')
+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")
+  #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)                                
+  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_untethered(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")
+  #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,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)                                
+  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,
@@ -47,7 +136,7 @@ def create_response_original(input_str,
 
   input_ids = tokenizer.encode(input_str + tokenizer.eos_token, return_tensors="pt")
   #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 = fine_tuned_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 = original_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)                                
   outputs = ""                                
   for output_id in output_ids:
     output = tokenizer.decode(output_id, skip_special_tokens=True)
@@ -64,6 +153,8 @@ def create_response_fine_tuned(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",
@@ -103,8 +194,95 @@ interface1 = gr.Interface(fn=create_response_original,
                           " 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_fine_tuned, inputs="text", outputs="text", title="Fine Tuned")
-demo = gr.TabbedInterface([interface1, interface2], ["Original", "Fine Tuned"])
+
+
+interface2 = gr.Interface(fn=create_response_untethered, 
+    title="untethered",
+    description="untethered fine tuning",
+    examples=[ 
+      ["What is death?",7,5,0.2,1.5,0.9,50,True], # The first example
+      ["One of the best teachers in all of life turns out to be what?",7,5,0.2,1.5,0.9,50,True], # The second example
+      ["what is your most meaningful relationship?",7,5,0.2,1.5,0.9,50,True], # The third example
+      ["What actually gives life meaning?",7,5,0.2,1.5,0.9,50,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)])
+
+interface3 = gr.Interface(fn=create_response_untethered_paraphrased, 
+    title="untethered paraphrased",
+    description="untethered paraphrased fine tuning",
+    examples=[ 
+      ["What is death?",7,5,0.2,1.5,0.9,50,True], # The first example
+      ["One of the best teachers in all of life turns out to be what?",7,5,0.2,1.5,0.9,50,True], # The second example
+      ["what is your most meaningful relationship?",7,5,0.2,1.5,0.9,50,True], # The third example
+      ["What actually gives life meaning?",7,5,0.2,1.5,0.9,50,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_fine_tuned, inputs="text", outputs="text", title="Fine Tuned")
+demo = gr.TabbedInterface([interface1, interface2, interface3], ["Original", "Untethered", "Untethered paraphrased"])
 # with gr.Blocks() as demo:
 #   with gr.Row():
 #