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4a4a0a9
1
Parent(s):
2966484
2024-02-06 13:57:07 Publish script update
Browse files- app.py +9 -2
- data/2024-02-06 13:51:35.085414_general_plot.csv +284 -0
app.py
CHANGED
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@@ -48,7 +48,7 @@ output_types_df = pd.DataFrame({
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summary_df: pd.DataFrame = pd.read_csv("data/2024-02-05 23:33:22.947120_summary.csv")
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time_of_day_comparison_df = pd.read_csv("data/2024-02-06 09:49:19.637072_time_of_day_comparison.csv")
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-
general_plots = pd.read_csv("data/2024-02-06 13:
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model_costs_df = pd.read_csv("data/2024-02-05 12:03:45.281624_model_costs.csv")
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@@ -182,7 +182,14 @@ Measurements were made during a normal work week.
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with gr.Tab("Costs comparison"):
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models_costs_ui = gr.DataFrame(dataframe_style(model_costs_df), label="Costs comparison")
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gr.Markdown(
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-
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)
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filter_button.click(
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fn=filter_dataframes,
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summary_df: pd.DataFrame = pd.read_csv("data/2024-02-05 23:33:22.947120_summary.csv")
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time_of_day_comparison_df = pd.read_csv("data/2024-02-06 09:49:19.637072_time_of_day_comparison.csv")
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+
general_plots = pd.read_csv("data/2024-02-06 13:51:35.085414_general_plot.csv")
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model_costs_df = pd.read_csv("data/2024-02-05 12:03:45.281624_model_costs.csv")
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with gr.Tab("Costs comparison"):
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models_costs_ui = gr.DataFrame(dataframe_style(model_costs_df), label="Costs comparison")
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gr.Markdown(
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+
"""\
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+
Provider pricing column contains pricing from the website of the provider.
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+
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+
Hugging Face Inference Endpoints are charged by hour so to compare different providers together,
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for models hosted this way we calculated "Cost Per Token" column using data collected during the experiment.
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+
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Note that pause and resume time cost was not included in the "Cost Per Token" column calculation.
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"""
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)
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filter_button.click(
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fn=filter_dataframes,
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data/2024-02-06 13:51:35.085414_general_plot.csv
ADDED
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| 1 |
+
path,plot_object,header,description,plot_json
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+
./html/plots/response_times.html,"Figure({
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+
'data': [{'alignmentgroup': 'True',
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+
'hovertemplate': 'Model=%{x}<br>Execution time (s)=%{y}<extra></extra>',
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+
'legendgroup': 'Mistral-7B-Instruct-v0.2',
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+
'marker': {'color': '#636efa', 'pattern': {'shape': ''}},
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+
'name': 'Mistral-7B-Instruct-v0.2',
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'offsetgroup': 'Mistral-7B-Instruct-v0.2',
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+
'orientation': 'v',
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'showlegend': True,
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+
'textposition': 'auto',
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| 12 |
+
'type': 'bar',
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+
'x': array(['Mistral-7B-Instruct-v0.2'], dtype=object),
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| 14 |
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'xaxis': 'x',
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| 15 |
+
'y': array([3.27102681]),
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'yaxis': 'y'},
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| 17 |
+
{'alignmentgroup': 'True',
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| 18 |
+
'hovertemplate': 'Model=%{x}<br>Execution time (s)=%{y}<extra></extra>',
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| 19 |
+
'legendgroup': 'Mixtral-8x7B-Instruct-v0.1',
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| 20 |
+
'marker': {'color': '#EF553B', 'pattern': {'shape': ''}},
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+
'name': 'Mixtral-8x7B-Instruct-v0.1',
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| 22 |
+
'offsetgroup': 'Mixtral-8x7B-Instruct-v0.1',
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| 23 |
+
'orientation': 'v',
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| 24 |
+
'showlegend': True,
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| 25 |
+
'textposition': 'auto',
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| 26 |
+
'type': 'bar',
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| 27 |
+
'x': array(['Mixtral-8x7B-Instruct-v0.1'], dtype=object),
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| 28 |
+
'xaxis': 'x',
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| 29 |
+
'y': array([4.83500647]),
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| 30 |
+
'yaxis': 'y'},
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| 31 |
+
{'alignmentgroup': 'True',
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| 32 |
+
'hovertemplate': 'Model=%{x}<br>Execution time (s)=%{y}<extra></extra>',
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| 33 |
+
'legendgroup': 'TinyLlama/TinyLlama-1.1B-Chat-v1.0',
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| 34 |
+
'marker': {'color': '#00cc96', 'pattern': {'shape': ''}},
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| 35 |
+
'name': 'TinyLlama/TinyLlama-1.1B-Chat-v1.0',
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| 36 |
+
'offsetgroup': 'TinyLlama/TinyLlama-1.1B-Chat-v1.0',
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| 37 |
+
'orientation': 'v',
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+
'showlegend': True,
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| 39 |
+
'textposition': 'auto',
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| 40 |
+
'type': 'bar',
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| 41 |
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'x': array(['TinyLlama/TinyLlama-1.1B-Chat-v1.0'], dtype=object),
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| 42 |
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'xaxis': 'x',
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| 43 |
+
'y': array([1.79273835]),
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| 44 |
+
'yaxis': 'y'},
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| 45 |
+
{'alignmentgroup': 'True',
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| 46 |
+
'hovertemplate': 'Model=%{x}<br>Execution time (s)=%{y}<extra></extra>',
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+
'legendgroup': 'chat-bison',
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| 48 |
+
'marker': {'color': '#ab63fa', 'pattern': {'shape': ''}},
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| 49 |
+
'name': 'chat-bison',
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+
'offsetgroup': 'chat-bison',
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| 51 |
+
'orientation': 'v',
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+
'showlegend': True,
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| 53 |
+
'textposition': 'auto',
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+
'type': 'bar',
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| 55 |
+
'x': array(['chat-bison'], dtype=object),
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| 56 |
+
'xaxis': 'x',
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| 57 |
+
'y': array([3.89024363]),
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| 58 |
+
'yaxis': 'y'},
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| 59 |
+
{'alignmentgroup': 'True',
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+
'hovertemplate': 'Model=%{x}<br>Execution time (s)=%{y}<extra></extra>',
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+
'legendgroup': 'chat-bison (PaLM 2)',
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+
'marker': {'color': '#FFA15A', 'pattern': {'shape': ''}},
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+
'name': 'chat-bison (PaLM 2)',
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+
'offsetgroup': 'chat-bison (PaLM 2)',
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+
'orientation': 'v',
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| 66 |
+
'showlegend': True,
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+
'textposition': 'auto',
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| 68 |
+
'type': 'bar',
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+
'x': array(['chat-bison (PaLM 2)'], dtype=object),
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| 70 |
+
'xaxis': 'x',
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+
'y': array([2.66830386]),
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| 72 |
+
'yaxis': 'y'},
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| 73 |
+
{'alignmentgroup': 'True',
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| 74 |
+
'hovertemplate': 'Model=%{x}<br>Execution time (s)=%{y}<extra></extra>',
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+
'legendgroup': 'chat-bison-32k',
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| 76 |
+
'marker': {'color': '#19d3f3', 'pattern': {'shape': ''}},
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| 77 |
+
'name': 'chat-bison-32k',
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+
'offsetgroup': 'chat-bison-32k',
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'orientation': 'v',
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| 80 |
+
'showlegend': True,
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+
'textposition': 'auto',
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+
'type': 'bar',
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+
'x': array(['chat-bison-32k'], dtype=object),
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+
'xaxis': 'x',
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| 85 |
+
'y': array([4.12817777]),
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| 86 |
+
'yaxis': 'y'},
|
| 87 |
+
{'alignmentgroup': 'True',
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| 88 |
+
'hovertemplate': 'Model=%{x}<br>Execution time (s)=%{y}<extra></extra>',
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| 89 |
+
'legendgroup': 'chat-bison-32k (PaLM 2 32K)',
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| 90 |
+
'marker': {'color': '#FF6692', 'pattern': {'shape': ''}},
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| 91 |
+
'name': 'chat-bison-32k (PaLM 2 32K)',
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| 92 |
+
'offsetgroup': 'chat-bison-32k (PaLM 2 32K)',
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| 93 |
+
'orientation': 'v',
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| 94 |
+
'showlegend': True,
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| 95 |
+
'textposition': 'auto',
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| 96 |
+
'type': 'bar',
|
| 97 |
+
'x': array(['chat-bison-32k (PaLM 2 32K)'], dtype=object),
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| 98 |
+
'xaxis': 'x',
|
| 99 |
+
'y': array([6.73886213]),
|
| 100 |
+
'yaxis': 'y'},
|
| 101 |
+
{'alignmentgroup': 'True',
|
| 102 |
+
'hovertemplate': 'Model=%{x}<br>Execution time (s)=%{y}<extra></extra>',
|
| 103 |
+
'legendgroup': 'gemini-pro',
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| 104 |
+
'marker': {'color': '#B6E880', 'pattern': {'shape': ''}},
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| 105 |
+
'name': 'gemini-pro',
|
| 106 |
+
'offsetgroup': 'gemini-pro',
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| 107 |
+
'orientation': 'v',
|
| 108 |
+
'showlegend': True,
|
| 109 |
+
'textposition': 'auto',
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| 110 |
+
'type': 'bar',
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| 111 |
+
'x': array(['gemini-pro'], dtype=object),
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| 112 |
+
'xaxis': 'x',
|
| 113 |
+
'y': array([3.03678889]),
|
| 114 |
+
'yaxis': 'y'},
|
| 115 |
+
{'alignmentgroup': 'True',
|
| 116 |
+
'hovertemplate': 'Model=%{x}<br>Execution time (s)=%{y}<extra></extra>',
|
| 117 |
+
'legendgroup': 'gpt-3.5-turbo',
|
| 118 |
+
'marker': {'color': '#FF97FF', 'pattern': {'shape': ''}},
|
| 119 |
+
'name': 'gpt-3.5-turbo',
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| 120 |
+
'offsetgroup': 'gpt-3.5-turbo',
|
| 121 |
+
'orientation': 'v',
|
| 122 |
+
'showlegend': True,
|
| 123 |
+
'textposition': 'auto',
|
| 124 |
+
'type': 'bar',
|
| 125 |
+
'x': array(['gpt-3.5-turbo'], dtype=object),
|
| 126 |
+
'xaxis': 'x',
|
| 127 |
+
'y': array([4.29307391]),
|
| 128 |
+
'yaxis': 'y'},
|
| 129 |
+
{'alignmentgroup': 'True',
|
| 130 |
+
'hovertemplate': 'Model=%{x}<br>Execution time (s)=%{y}<extra></extra>',
|
| 131 |
+
'legendgroup': 'gpt-4',
|
| 132 |
+
'marker': {'color': '#FECB52', 'pattern': {'shape': ''}},
|
| 133 |
+
'name': 'gpt-4',
|
| 134 |
+
'offsetgroup': 'gpt-4',
|
| 135 |
+
'orientation': 'v',
|
| 136 |
+
'showlegend': True,
|
| 137 |
+
'textposition': 'auto',
|
| 138 |
+
'type': 'bar',
|
| 139 |
+
'x': array(['gpt-4'], dtype=object),
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| 140 |
+
'xaxis': 'x',
|
| 141 |
+
'y': array([14.32373439]),
|
| 142 |
+
'yaxis': 'y'},
|
| 143 |
+
{'alignmentgroup': 'True',
|
| 144 |
+
'hovertemplate': 'Model=%{x}<br>Execution time (s)=%{y}<extra></extra>',
|
| 145 |
+
'legendgroup': 'gpt-4-turbo',
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| 146 |
+
'marker': {'color': '#636efa', 'pattern': {'shape': ''}},
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| 147 |
+
'name': 'gpt-4-turbo',
|
| 148 |
+
'offsetgroup': 'gpt-4-turbo',
|
| 149 |
+
'orientation': 'v',
|
| 150 |
+
'showlegend': True,
|
| 151 |
+
'textposition': 'auto',
|
| 152 |
+
'type': 'bar',
|
| 153 |
+
'x': array(['gpt-4-turbo'], dtype=object),
|
| 154 |
+
'xaxis': 'x',
|
| 155 |
+
'y': array([15.5354884]),
|
| 156 |
+
'yaxis': 'y'},
|
| 157 |
+
{'alignmentgroup': 'True',
|
| 158 |
+
'hovertemplate': 'Model=%{x}<br>Execution time (s)=%{y}<extra></extra>',
|
| 159 |
+
'legendgroup': 'llama-2-70b-chat',
|
| 160 |
+
'marker': {'color': '#EF553B', 'pattern': {'shape': ''}},
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| 161 |
+
'name': 'llama-2-70b-chat',
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| 162 |
+
'offsetgroup': 'llama-2-70b-chat',
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| 163 |
+
'orientation': 'v',
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| 164 |
+
'showlegend': True,
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| 165 |
+
'textposition': 'auto',
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| 166 |
+
'type': 'bar',
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| 167 |
+
'x': array(['llama-2-70b-chat'], dtype=object),
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| 168 |
+
'xaxis': 'x',
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| 169 |
+
'y': array([3.6742315]),
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| 170 |
+
'yaxis': 'y'},
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| 171 |
+
{'alignmentgroup': 'True',
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| 172 |
+
'hovertemplate': 'Model=%{x}<br>Execution time (s)=%{y}<extra></extra>',
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| 173 |
+
'legendgroup': 'zephyr-7b-beta',
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+
'marker': {'color': '#00cc96', 'pattern': {'shape': ''}},
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| 175 |
+
'name': 'zephyr-7b-beta',
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+
'offsetgroup': 'zephyr-7b-beta',
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+
'orientation': 'v',
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| 178 |
+
'showlegend': True,
|
| 179 |
+
'textposition': 'auto',
|
| 180 |
+
'type': 'bar',
|
| 181 |
+
'x': array(['zephyr-7b-beta'], dtype=object),
|
| 182 |
+
'xaxis': 'x',
|
| 183 |
+
'y': array([3.6370095]),
|
| 184 |
+
'yaxis': 'y'}],
|
| 185 |
+
'layout': {'barmode': 'relative',
|
| 186 |
+
'legend': {'title': {'text': 'Model'}, 'tracegroupgap': 0},
|
| 187 |
+
'template': '...',
|
| 188 |
+
'title': {'text': 'Model endpoints response times'},
|
| 189 |
+
'xaxis': {'anchor': 'y',
|
| 190 |
+
'categoryarray': [Mistral-7B-Instruct-v0.2,
|
| 191 |
+
Mixtral-8x7B-Instruct-v0.1,
|
| 192 |
+
TinyLlama/TinyLlama-1.1B-Chat-v1.0,
|
| 193 |
+
chat-bison, chat-bison (PaLM 2), chat-
|
| 194 |
+
bison-32k, chat-bison-32k (PaLM 2 32K),
|
| 195 |
+
gemini-pro, gpt-3.5-turbo, gpt-4,
|
| 196 |
+
gpt-4-turbo, llama-2-70b-chat,
|
| 197 |
+
zephyr-7b-beta],
|
| 198 |
+
'categoryorder': 'array',
|
| 199 |
+
'domain': [0.0, 1.0],
|
| 200 |
+
'title': {'text': 'Model'}},
|
| 201 |
+
'yaxis': {'anchor': 'x', 'domain': [0.0, 1.0], 'title': {'text': 'Execution time (s)'}}}
|
| 202 |
+
})",Model endpoints response times,"This plot shows averaged response time for one query for each model.
|
| 203 |
+
|
| 204 |
+
We used `time.time()` result saved to variable before making the call to API and compared it to `time.time()` result after receiving the results.
|
| 205 |
+
|
| 206 |
+
It is also important to note that we used litellm python library for all of the models which adds some overhead compared to pure curl calls.","{""data"":[{""alignmentgroup"":""True"",""hovertemplate"":""Model=%{x}\u003cbr\u003eExecution time (s)=%{y}\u003cextra\u003e\u003c\u002fextra\u003e"",""legendgroup"":""Mistral-7B-Instruct-v0.2"",""marker"":{""color"":""#636efa"",""pattern"":{""shape"":""""}},""name"":""Mistral-7B-Instruct-v0.2"",""offsetgroup"":""Mistral-7B-Instruct-v0.2"",""orientation"":""v"",""showlegend"":true,""textposition"":""auto"",""x"":[""Mistral-7B-Instruct-v0.2""],""xaxis"":""x"",""y"":[3.271026806036631],""yaxis"":""y"",""type"":""bar""},{""alignmentgroup"":""True"",""hovertemplate"":""Model=%{x}\u003cbr\u003eExecution time 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'textposition': 'auto',
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|
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'template': '...',
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| 239 |
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'title': {'text': 'Costs of execution of 800 test queries per model'},
|
| 240 |
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'xaxis': {'anchor': 'y', 'domain': [0.0, 1.0], 'title': {'text': 'Model'}},
|
| 241 |
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'yaxis': {'anchor': 'x', 'domain': [0.0, 1.0], 'title': {'text': 'Execution cost ($)'}}}
|
| 242 |
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})",Costs of execution of 800 test queries per model,"Costs of experiments carried out to create this table, 800 test queries per model.
|
| 243 |
+
|
| 244 |
+
Note that pause and resume time cost was not included in the cost calculation for the models billed hourly.
|
| 245 |
+
|
| 246 |
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})",Model sizes in billions of parameters,"Model sizes based on release blogs and documentation of their respective developers.
|
| 283 |
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|
| 284 |
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