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import gradio as gr
import pandas as pd
# Define benchmark data
benchmark_data = {
'Model': [
'IlyaGusev/saiga_llama3_8b', # LLaMA3
'Vikhrmodels/Vikhr-Nemo-12B', # Vikhr
'TinyLLaMA/TinyLlama-1.1B', # TinyLLaMA
'mistralai/Mistral-Nemo-Instruct-2407', # Mistral
'Vikhrmodels/Vikhr-Qwen-2.5-0.5b-Instruct' # Qwen
],
'Creativity Score': [
37.75, # LLaMA3
46.00, # Vikhr
6.50, # TinyLLaMA
23.75, # Mistral
8.25 # Qwen
],
'Diversity Score': [
49.50, # LLaMA3
52.00, # Vikhr
14.50, # TinyLLaMA
38.50, # Mistral
15.55 # Qwen
],
'Relevance Score': [
79.25, # LLaMA3
87.50, # Vikhr
18.50, # TinyLLaMA
76.75, # Mistral
34.25 # Qwen
],
'Average Score': [
55.50, # LLaMA3
61.83, # Vikhr
13.17, # TinyLLaMA
46.33, # Mistral
19.35 # Qwen
]
}
def display_results():
df = pd.DataFrame(benchmark_data)
return df
# Create Gradio interface
with gr.Blocks() as demo:
gr.Markdown("# Russian Language Model Benchmark Results")
# Add dataframe output
output = gr.DataFrame(
headers=list(benchmark_data.keys()),
interactive=False
)
refresh_btn = gr.Button("Show Results")
refresh_btn.click(fn=display_results, outputs=output)
if __name__ == "__main__":
demo.launch() |