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Replaced text-davinci-002 with gpt-3.5-turbo-instruct because text-davinci-002 is deprecated. #20

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Replaced text-davinci-002 with gpt-3.5-turbo-instruct because text-davinci-002 is deprecated. #20

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2bb6ac6
Update ape.py
faridaelhusseiny278 Jan 31, 2024
addb3c1
Update demo.py
faridaelhusseiny278 Jan 31, 2024
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10 changes: 5 additions & 5 deletions automatic_prompt_engineer/ape.py
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Original file line number Diff line number Diff line change
Expand Up @@ -18,8 +18,8 @@ def simple_ape(dataset,
eval_template='Instruction: [PROMPT]\nInput: [INPUT]\nOutput: [OUTPUT]',
prompt_gen_template=None,
demos_template='Input: [INPUT]\nOutput: [OUTPUT]',
eval_model='text-davinci-002',
prompt_gen_model='text-davinci-002',
eval_model='gpt-3.5-turbo-instruct',
prompt_gen_model='gpt-3.5-turbo-instruct',
prompt_gen_mode='forward',
num_prompts=50,
eval_rounds=20,
Expand Down Expand Up @@ -60,7 +60,7 @@ def simple_eval(dataset,
prompts,
eval_template='Instruction: [PROMPT]\nInput: [INPUT]\nOutput: [OUTPUT]',
demos_template='Input: [INPUT]\nOutput: [OUTPUT]',
eval_model='text-davinci-002',
eval_model='gpt-3.5-turbo-instruct',
num_samples=50):
"""
Function that wraps the evaluate_prompts function to make it easier to use.
Expand All @@ -87,8 +87,8 @@ def simple_estimate_cost(dataset,
eval_template='Instruction: [PROMPT]\nInput: [INPUT]\nOutput: [OUTPUT]',
prompt_gen_template=None,
demos_template='Input: [INPUT]\nOutput: [OUTPUT]',
eval_model='text-davinci-002',
prompt_gen_model='text-davinci-002',
eval_model='gpt-3.5-turbo-instruct',
prompt_gen_model='gpt-3.5-turbo-instruct',
prompt_gen_mode='forward',
num_prompts=50,
eval_rounds=20,
Expand Down
36 changes: 18 additions & 18 deletions demo.py
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Original file line number Diff line number Diff line change
Expand Up @@ -7,7 +7,7 @@
from automatic_prompt_engineer.ape import get_simple_prompt_gen_template
from automatic_prompt_engineer import ape, evaluate, config, template, llm

model_types = ['text-ada-001', 'text-babbage-001', 'text-curie-001', 'text-davinci-002']
model_types = ['text-ada-001', 'text-babbage-001', 'text-curie-001', 'gpt-3.5-turbo-instruct']
mode_types = ['forward', 'insert']
eval_types = ['likelihood', 'bandits']
task_types = ['antonyms', 'cause_and_effect', 'common_concept', 'diff', 'first_word_letter',
Expand Down Expand Up @@ -59,8 +59,8 @@ def run_ape(prompt_gen_data, eval_data,
eval_template='Instruction: [PROMPT]\nInput: [INPUT]\nOutput: [OUTPUT]',
prompt_gen_template=None,
demos_template='Input: [INPUT]\nOutput: [OUTPUT]',
eval_model='text-davinci-002',
prompt_gen_model='text-davinci-002',
eval_model='gpt-3.5-turbo-instruct',
prompt_gen_model='gpt-3.5-turbo-instruct',
prompt_gen_mode='forward',
num_prompts=50, eval_rounds=10, prompt_gen_batch_size=200, eval_batch_size=500, # basic
num_subsamples=None, num_demos=None, # advanced
Expand Down Expand Up @@ -141,8 +141,8 @@ def run_ape(prompt_gen_data, eval_data,

def basic_ape(prompt_gen_data, eval_data,
eval_template='Instruction: [PROMPT]\nInput: [INPUT]\nOutput: [OUTPUT]',
eval_model='text-davinci-002',
prompt_gen_model='text-davinci-002',
eval_model='gpt-3.5-turbo-instruct',
prompt_gen_model='gpt-3.5-turbo-instruct',
prompt_gen_mode='forward',
num_prompts=50, eval_rounds=10, prompt_gen_batch_size=200, eval_batch_size=500):
return run_ape(prompt_gen_data, eval_data, eval_template,
Expand All @@ -153,8 +153,8 @@ def basic_ape(prompt_gen_data, eval_data,

def advance_ape(prompt_gen_data, eval_data,
eval_template, prompt_gen_template, demos_template,
eval_model='text-davinci-002',
prompt_gen_model='text-davinci-002',
eval_model='gpt-3.5-turbo-instruct',
prompt_gen_model='gpt-3.5-turbo-instruct',
prompt_gen_mode='forward',
num_prompts=50, eval_rounds=10, prompt_gen_batch_size=200, eval_batch_size=500, # basic
num_subsamples=None, num_demos=None, # advanced
Expand All @@ -175,8 +175,8 @@ def estimate_cost(prompt_gen_data, eval_data,
eval_template='Instruction: [PROMPT]\nInput: [INPUT]\nOutput: [OUTPUT]',
prompt_gen_template=None,
demos_template='Input: [INPUT]\nOutput: [OUTPUT]',
eval_model='text-davinci-002',
prompt_gen_model='text-davinci-002',
eval_model='gpt-3.5-turbo-instruct',
prompt_gen_model='gpt-3.5-turbo-instruct',
prompt_gen_mode='forward',
num_prompts=50, eval_rounds=10, prompt_gen_batch_size=200, eval_batch_size=500, # basic
num_subsamples=None, num_demos=None, # advanced
Expand Down Expand Up @@ -251,8 +251,8 @@ def estimate_cost(prompt_gen_data, eval_data,
def basic_estimate_cost(prompt_gen_data,
eval_data,
eval_template='Instruction: [PROMPT]\nInput: [INPUT]\nOutput: [OUTPUT]',
eval_model='text-davinci-002',
prompt_gen_model='text-davinci-002',
eval_model='gpt-3.5-turbo-instruct',
prompt_gen_model='gpt-3.5-turbo-instruct',
prompt_gen_mode='forward',
num_prompts=50, eval_rounds=10, prompt_gen_batch_size=200, eval_batch_size=500):
return estimate_cost(prompt_gen_data, eval_data, eval_template,
Expand All @@ -263,8 +263,8 @@ def basic_estimate_cost(prompt_gen_data,

def advance_estimate_cost(prompt_gen_data, eval_data,
eval_template, prompt_gen_template, demos_template,
eval_model='text-davinci-002',
prompt_gen_model='text-davinci-002',
eval_model='gpt-3.5-turbo-instruct',
prompt_gen_model='gpt-3.5-turbo-instruct',
num_prompts=50, eval_rounds=10, prompt_gen_batch_size=200, eval_batch_size=500, # basic
num_subsamples=None, num_demos=None, # advanced
num_samples=None, num_few_shot=None # advanced
Expand All @@ -285,7 +285,7 @@ def compute_score(prompt,
eval_data,
eval_template,
demos_template,
eval_model='text-davinci-002',
eval_model='gpt-3.5-turbo-instruct',
num_few_shot=None # advanced
):
eval_data = parse_data(eval_data)
Expand All @@ -302,8 +302,8 @@ def compute_score(prompt,

def run_prompt(prompt, inputs,
eval_template='Instruction: [PROMPT]\nInput: [INPUT]\nOutput: [OUTPUT]',
eval_model='text-davinci-002',
prompt_gen_model='text-davinci-002',
eval_model='gpt-3.5-turbo-instruct',
prompt_gen_model='gpt-3.5-turbo-instruct',
prompt_gen_mode='forward',
num_prompts=50, eval_rounds=10, prompt_gen_batch_size=200, eval_batch_size=500):
conf = config.simple_config(
Expand Down Expand Up @@ -381,10 +381,10 @@ def get_demo():
with gr.Tab("Basic"):
with gr.Row():
prompt_gen_model = gr.Dropdown(label="Prompt Generation Model", choices=model_types,
value="text-davinci-002")
value="gpt-3.5-turbo-instruct")

eval_model = gr.Dropdown(label="Evaluation Model", choices=model_types,
value="text-davinci-002")
value="gpt-3.5-turbo-instruct")

with gr.Row():
num_prompts = gr.Slider(label="Number of Prompts", minimum=1, maximum=250, step=10, value=50)
Expand Down