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| import gradio as gr | |
| import pandas as pd | |
| import random | |
| df = pd.read_csv('data.csv') | |
| df_stats = pd.read_csv('data_stats_langs.csv') | |
| map_models = df_stats[['model','model_name']].set_index('model').to_dict() | |
| df = df.rename(columns=map_models['model_name']) | |
| models = sorted(df.columns.tolist()) | |
| models.remove('hash') | |
| models.remove('message') | |
| messages = sorted(df['message'].tolist(), key=len) | |
| messages_select = [(m[:250],m) for m in messages] | |
| def out(message, model1, model2): | |
| row = df[df['message'] == message] | |
| output1 = row[model1].values[0] | |
| output2 = row[model2].values[0] | |
| return message, output1, output2 | |
| OUTPUT_DESCRIPTION='''How good are OpenSource LLMs in German? I've benchmarked a couple of models and generated outputs for about 250 prompts to compare the models. | |
| For information about the used dataset and generation see the [README.md](https://huggingface.co/spaces/floleuerer/german_llm_outputs/blob/main/README.md) | |
| Select a Prompt and the models you would like to compare -> hit "Show Outputs" | |
| ''' | |
| BENCHMARK_DESCRIPTION='''# Columns | |
| de: German Benchmark results (arc, hellaswag, mmlu) | |
| en: English Benchmark results (arc, hellaswag, mmlu) | |
| de_frac: Given a german prompt - how often does the model correctly respond in German? | |
| ''' | |
| with gr.Blocks() as iface: | |
| with gr.Tab('Model Outputs'): | |
| gr.Markdown(OUTPUT_DESCRIPTION) | |
| with gr.Row(): | |
| drop_message = gr.Dropdown(messages_select, label='Prompt', value=random.choice(messages)) | |
| with gr.Row(): | |
| drop_model1 = gr.Dropdown(models, label='Model 1', value=random.choice(models)) | |
| drop_model2 = gr.Dropdown(models, label='Model 2', value=random.choice(models)) | |
| with gr.Row(): | |
| btn = gr.Button("Show Outputs") | |
| with gr.Row(): | |
| out_message = gr.TextArea(label='Prompt') | |
| with gr.Row(): | |
| out_model1 = gr.TextArea(label='Output Model 1') | |
| out_model2 = gr.TextArea(label='Output Model 2') | |
| with gr.Tab('Benchmarks'): | |
| gr.Markdown(BENCHMARK_DESCRIPTION) | |
| gr.Dataframe(df_stats.drop('model', axis=1)) | |
| btn.click(out, | |
| inputs=[drop_message, drop_model1, drop_model2], | |
| outputs=[out_message, out_model1, out_model2]) | |
| iface.launch() |