research14 commited on
Commit
971271a
·
1 Parent(s): 95980d0

added names to print

Browse files
Files changed (1) hide show
  1. app.py +2 -2
app.py CHANGED
@@ -57,7 +57,7 @@ def gpt_respond(tab_name, message, chat_history, max_convo_length = 10):
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  def vicuna_respond(tab_name, message, chat_history):
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  formatted_prompt = f'''Generate the output only for the assistant. Please output any {tab_name} in the following sentence one per line without any additional text: {message}'''
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- print('Prompt + Context:')
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  print(formatted_prompt)
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  input_ids = vicuna_tokenizer.encode(formatted_prompt, return_tensors="pt")
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  output_ids = vicuna_model.generate(input_ids, do_sample=True, max_length=149, num_beams=5, no_repeat_ngram_size=2)
@@ -70,7 +70,7 @@ def vicuna_respond(tab_name, message, chat_history):
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  def llama_respond(tab_name, message, chat_history):
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  formatted_prompt = f'''Generate the output only for the assistant. Please output any {tab_name} in the following sentence one per line without any additional text: {message}'''
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- print('Prompt + Context:')
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  print(formatted_prompt)
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  input_ids = llama_tokenizer.encode(formatted_prompt, return_tensors="pt")
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  output_ids = llama_model.generate(input_ids, do_sample=True, max_length=149, num_beams=5, no_repeat_ngram_size=2)
 
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  def vicuna_respond(tab_name, message, chat_history):
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  formatted_prompt = f'''Generate the output only for the assistant. Please output any {tab_name} in the following sentence one per line without any additional text: {message}'''
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+ print('Vicuna - Prompt + Context:')
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  print(formatted_prompt)
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  input_ids = vicuna_tokenizer.encode(formatted_prompt, return_tensors="pt")
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  output_ids = vicuna_model.generate(input_ids, do_sample=True, max_length=149, num_beams=5, no_repeat_ngram_size=2)
 
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  def llama_respond(tab_name, message, chat_history):
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  formatted_prompt = f'''Generate the output only for the assistant. Please output any {tab_name} in the following sentence one per line without any additional text: {message}'''
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+ print('Llama - Prompt + Context:')
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  print(formatted_prompt)
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  input_ids = llama_tokenizer.encode(formatted_prompt, return_tensors="pt")
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  output_ids = llama_model.generate(input_ids, do_sample=True, max_length=149, num_beams=5, no_repeat_ngram_size=2)