benchmark under cookbook

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ishaan-jaff 2023-09-07 21:46:13 -07:00
parent 39f94c2147
commit 6d2eea7eac
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from litellm import completion, completion_cost
import time
import click
from tqdm import tqdm
from tabulate import tabulate
from termcolor import colored
# Define the list of models to benchmark
models = ['gpt-3.5-turbo', 'togethercomputer/llama-2-70b-chat', 'claude-2']
# List of questions to benchmark (replace with your questions)
questions = [
"When will BerriAI IPO?",
"When will LiteLLM hit $100M ARR?"
]
@click.command()
@click.option('--system-prompt', default="You are a helpful assistant that can answer questions.", help="System prompt for the conversation.")
def main(system_prompt):
for question in questions:
data = [] # Data for the current question
with tqdm(total=len(models)) as pbar:
for model in models:
colored_description = colored(f"Running question: {question} for model: {model}", 'green')
pbar.set_description(colored_description)
start_time = time.time()
response = completion(
model=model,
max_tokens=500,
messages=[
{"role": "system", "content": system_prompt},
{"role": "user", "content": question}
],
)
end = time.time()
total_time = end - start_time
cost = completion_cost(response)
raw_response = response['choices'][0]['message']['content']
data.append({
'Model': colored(model, 'light_blue'),
'Response': raw_response, # Colorize the response
'ResponseTime': colored(f"{total_time:.2f} seconds", "red"),
'Cost': colored(f"${cost:.6f}", 'green'), # Colorize the cost
})
pbar.update(1)
# Separate headers from the data
headers = ['Model', 'Response', 'Response Time (seconds)', 'Cost ($)']
colwidths = [15, 80, 15, 10]
# Create a nicely formatted table for the current question
table = tabulate([list(d.values()) for d in data], headers, tablefmt="grid", maxcolwidths=colwidths)
# Print the table for the current question
colored_question = colored(question, 'green')
click.echo(f"\nBenchmark Results for '{colored_question}':")
click.echo(table) # Display the formatted table
if __name__ == '__main__':
main()

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