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Commit
·
e2a0c87
1
Parent(s):
d18a238
2024-02-08 01:30:03 Publish script update
Browse files- app.py +6 -1
- data/general_plots.csv +452 -0
- data/output_plots.csv +604 -0
- data/time_of_day_plots.csv +0 -0
app.py
CHANGED
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@@ -48,9 +48,10 @@ 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/
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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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time_of_day_plots = pd.read_csv("data/time_of_day_plots.csv")
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searched_model_name = ""
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collapse_languages = False
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@@ -167,6 +168,10 @@ Execution time refers to averaged time needed to execute one query.
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To count words we split the output string by whitespace `\w` regex character.
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Chunk sizes are measured in the characters count.""")
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with gr.Tab("Preformance by time of the day"):
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# display only first plot for all models
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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/general_plots.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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time_of_day_plots = pd.read_csv("data/time_of_day_plots.csv")
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+
output_plots = pd.read_csv("data/output_plots.csv")
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searched_model_name = ""
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collapse_languages = False
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To count words we split the output string by whitespace `\w` regex character.
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Chunk sizes are measured in the characters count.""")
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+
for index, row in output_plots.iterrows():
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plot = plotly.io.from_json(row["plot_json"])
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plot.update_layout(autosize=True)
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gr.Plot(plot, label=row["header"], scale=1)
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with gr.Tab("Preformance by time of the day"):
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# display only first plot for all models
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data/general_plots.csv
ADDED
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@@ -0,0 +1,452 @@
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| 1 |
+
path,plot_object,header,description,plot_json
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| 2 |
+
./html/plots/response_times.html,"Figure({
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| 3 |
+
'data': [{'alignmentgroup': 'True',
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| 4 |
+
'hovertemplate': 'Model=%{x}<br>Execution time (s)=%{y}<extra></extra>',
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| 5 |
+
'legendgroup': 'Mistral-7B-Instruct-v0.2',
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| 6 |
+
'marker': {'color': '#636efa', 'pattern': {'shape': ''}},
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| 7 |
+
'name': 'Mistral-7B-Instruct-v0.2',
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| 8 |
+
'offsetgroup': 'Mistral-7B-Instruct-v0.2',
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| 9 |
+
'orientation': 'v',
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| 10 |
+
'showlegend': True,
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| 11 |
+
'textposition': 'auto',
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| 12 |
+
'type': 'bar',
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| 13 |
+
'x': array(['Mistral-7B-Instruct-v0.2'], dtype=object),
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| 14 |
+
'xaxis': 'x',
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| 15 |
+
'y': array([3.27102681]),
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| 16 |
+
'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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| 21 |
+
'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,
|
| 25 |
+
'textposition': 'auto',
|
| 26 |
+
'type': 'bar',
|
| 27 |
+
'x': array(['Mixtral-8x7B-Instruct-v0.1'], dtype=object),
|
| 28 |
+
'xaxis': 'x',
|
| 29 |
+
'y': array([4.83500647]),
|
| 30 |
+
'yaxis': 'y'},
|
| 31 |
+
{'alignmentgroup': 'True',
|
| 32 |
+
'hovertemplate': 'Model=%{x}<br>Execution time (s)=%{y}<extra></extra>',
|
| 33 |
+
'legendgroup': 'TinyLlama/TinyLlama-1.1B-Chat-v1.0',
|
| 34 |
+
'marker': {'color': '#00cc96', 'pattern': {'shape': ''}},
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| 35 |
+
'name': 'TinyLlama/TinyLlama-1.1B-Chat-v1.0',
|
| 36 |
+
'offsetgroup': 'TinyLlama/TinyLlama-1.1B-Chat-v1.0',
|
| 37 |
+
'orientation': 'v',
|
| 38 |
+
'showlegend': True,
|
| 39 |
+
'textposition': 'auto',
|
| 40 |
+
'type': 'bar',
|
| 41 |
+
'x': array(['TinyLlama/TinyLlama-1.1B-Chat-v1.0'], dtype=object),
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| 42 |
+
'xaxis': 'x',
|
| 43 |
+
'y': array([1.79273835]),
|
| 44 |
+
'yaxis': 'y'},
|
| 45 |
+
{'alignmentgroup': 'True',
|
| 46 |
+
'hovertemplate': 'Model=%{x}<br>Execution time (s)=%{y}<extra></extra>',
|
| 47 |
+
'legendgroup': 'chat-bison',
|
| 48 |
+
'marker': {'color': '#ab63fa', 'pattern': {'shape': ''}},
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| 49 |
+
'name': 'chat-bison',
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| 50 |
+
'offsetgroup': 'chat-bison',
|
| 51 |
+
'orientation': 'v',
|
| 52 |
+
'showlegend': True,
|
| 53 |
+
'textposition': 'auto',
|
| 54 |
+
'type': 'bar',
|
| 55 |
+
'x': array(['chat-bison'], dtype=object),
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| 56 |
+
'xaxis': 'x',
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| 57 |
+
'y': array([3.89024363]),
|
| 58 |
+
'yaxis': 'y'},
|
| 59 |
+
{'alignmentgroup': 'True',
|
| 60 |
+
'hovertemplate': 'Model=%{x}<br>Execution time (s)=%{y}<extra></extra>',
|
| 61 |
+
'legendgroup': 'chat-bison (PaLM 2)',
|
| 62 |
+
'marker': {'color': '#FFA15A', 'pattern': {'shape': ''}},
|
| 63 |
+
'name': 'chat-bison (PaLM 2)',
|
| 64 |
+
'offsetgroup': 'chat-bison (PaLM 2)',
|
| 65 |
+
'orientation': 'v',
|
| 66 |
+
'showlegend': True,
|
| 67 |
+
'textposition': 'auto',
|
| 68 |
+
'type': 'bar',
|
| 69 |
+
'x': array(['chat-bison (PaLM 2)'], dtype=object),
|
| 70 |
+
'xaxis': 'x',
|
| 71 |
+
'y': array([2.66830386]),
|
| 72 |
+
'yaxis': 'y'},
|
| 73 |
+
{'alignmentgroup': 'True',
|
| 74 |
+
'hovertemplate': 'Model=%{x}<br>Execution time (s)=%{y}<extra></extra>',
|
| 75 |
+
'legendgroup': 'chat-bison-32k',
|
| 76 |
+
'marker': {'color': '#19d3f3', 'pattern': {'shape': ''}},
|
| 77 |
+
'name': 'chat-bison-32k',
|
| 78 |
+
'offsetgroup': 'chat-bison-32k',
|
| 79 |
+
'orientation': 'v',
|
| 80 |
+
'showlegend': True,
|
| 81 |
+
'textposition': 'auto',
|
| 82 |
+
'type': 'bar',
|
| 83 |
+
'x': array(['chat-bison-32k'], dtype=object),
|
| 84 |
+
'xaxis': 'x',
|
| 85 |
+
'y': array([4.12817777]),
|
| 86 |
+
'yaxis': 'y'},
|
| 87 |
+
{'alignmentgroup': 'True',
|
| 88 |
+
'hovertemplate': 'Model=%{x}<br>Execution time (s)=%{y}<extra></extra>',
|
| 89 |
+
'legendgroup': 'chat-bison-32k (PaLM 2 32K)',
|
| 90 |
+
'marker': {'color': '#FF6692', 'pattern': {'shape': ''}},
|
| 91 |
+
'name': 'chat-bison-32k (PaLM 2 32K)',
|
| 92 |
+
'offsetgroup': 'chat-bison-32k (PaLM 2 32K)',
|
| 93 |
+
'orientation': 'v',
|
| 94 |
+
'showlegend': True,
|
| 95 |
+
'textposition': 'auto',
|
| 96 |
+
'type': 'bar',
|
| 97 |
+
'x': array(['chat-bison-32k (PaLM 2 32K)'], dtype=object),
|
| 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',
|
| 104 |
+
'marker': {'color': '#B6E880', 'pattern': {'shape': ''}},
|
| 105 |
+
'name': 'gemini-pro',
|
| 106 |
+
'offsetgroup': 'gemini-pro',
|
| 107 |
+
'orientation': 'v',
|
| 108 |
+
'showlegend': True,
|
| 109 |
+
'textposition': 'auto',
|
| 110 |
+
'type': 'bar',
|
| 111 |
+
'x': array(['gemini-pro'], dtype=object),
|
| 112 |
+
'xaxis': 'x',
|
| 113 |
+
'y': array([3.03678889]),
|
| 114 |
+
'yaxis': 'y'},
|
| 115 |
+
{'alignmentgroup': 'True',
|
| 116 |
+
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'legendgroup': 'gpt-3.5-turbo',
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'marker': {'color': '#FF97FF', 'pattern': {'shape': ''}},
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| 119 |
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'name': 'gpt-3.5-turbo',
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| 120 |
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'offsetgroup': 'gpt-3.5-turbo',
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| 121 |
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'orientation': 'v',
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| 122 |
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'showlegend': True,
|
| 123 |
+
'textposition': 'auto',
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| 124 |
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'type': 'bar',
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| 125 |
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'x': array(['gpt-3.5-turbo'], dtype=object),
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| 126 |
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'xaxis': 'x',
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| 127 |
+
'y': array([4.29307391]),
|
| 128 |
+
'yaxis': 'y'},
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| 129 |
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{'alignmentgroup': 'True',
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| 130 |
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'hovertemplate': 'Model=%{x}<br>Execution time (s)=%{y}<extra></extra>',
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| 131 |
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'legendgroup': 'gpt-4',
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| 132 |
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'marker': {'color': '#FECB52', 'pattern': {'shape': ''}},
|
| 133 |
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'name': 'gpt-4',
|
| 134 |
+
'offsetgroup': 'gpt-4',
|
| 135 |
+
'orientation': 'v',
|
| 136 |
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'showlegend': True,
|
| 137 |
+
'textposition': 'auto',
|
| 138 |
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'type': 'bar',
|
| 139 |
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'x': array(['gpt-4'], dtype=object),
|
| 140 |
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'xaxis': 'x',
|
| 141 |
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'y': array([14.32373439]),
|
| 142 |
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'yaxis': 'y'},
|
| 143 |
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{'alignmentgroup': 'True',
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| 144 |
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'hovertemplate': 'Model=%{x}<br>Execution time (s)=%{y}<extra></extra>',
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| 145 |
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'legendgroup': 'gpt-4-turbo',
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| 146 |
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'marker': {'color': '#636efa', 'pattern': {'shape': ''}},
|
| 147 |
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'name': 'gpt-4-turbo',
|
| 148 |
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'offsetgroup': 'gpt-4-turbo',
|
| 149 |
+
'orientation': 'v',
|
| 150 |
+
'showlegend': True,
|
| 151 |
+
'textposition': 'auto',
|
| 152 |
+
'type': 'bar',
|
| 153 |
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'x': array(['gpt-4-turbo'], dtype=object),
|
| 154 |
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'xaxis': 'x',
|
| 155 |
+
'y': array([15.5354884]),
|
| 156 |
+
'yaxis': 'y'},
|
| 157 |
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{'alignmentgroup': 'True',
|
| 158 |
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'hovertemplate': 'Model=%{x}<br>Execution time (s)=%{y}<extra></extra>',
|
| 159 |
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'legendgroup': 'llama-2-70b-chat',
|
| 160 |
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'marker': {'color': '#EF553B', 'pattern': {'shape': ''}},
|
| 161 |
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'name': 'llama-2-70b-chat',
|
| 162 |
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'offsetgroup': 'llama-2-70b-chat',
|
| 163 |
+
'orientation': 'v',
|
| 164 |
+
'showlegend': True,
|
| 165 |
+
'textposition': 'auto',
|
| 166 |
+
'type': 'bar',
|
| 167 |
+
'x': array(['llama-2-70b-chat'], dtype=object),
|
| 168 |
+
'xaxis': 'x',
|
| 169 |
+
'y': array([3.6742315]),
|
| 170 |
+
'yaxis': 'y'},
|
| 171 |
+
{'alignmentgroup': 'True',
|
| 172 |
+
'hovertemplate': 'Model=%{x}<br>Execution time (s)=%{y}<extra></extra>',
|
| 173 |
+
'legendgroup': 'zephyr-7b-beta',
|
| 174 |
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'marker': {'color': '#00cc96', 'pattern': {'shape': ''}},
|
| 175 |
+
'name': 'zephyr-7b-beta',
|
| 176 |
+
'offsetgroup': 'zephyr-7b-beta',
|
| 177 |
+
'orientation': 'v',
|
| 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 |
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'template': '...',
|
| 188 |
+
'title': {'text': 'Model endpoints response times'},
|
| 189 |
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'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 |
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'domain': [0.0, 1.0],
|
| 200 |
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'title': {'text': 'Model'}},
|
| 201 |
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'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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'xaxis': 'x',
|
| 360 |
+
'y': array(['chat-bison-32k (PaLM 2 32K)'], dtype=object),
|
| 361 |
+
'yaxis': 'y'}],
|
| 362 |
+
'layout': {'barmode': 'relative',
|
| 363 |
+
'legend': {'title': {'text': 'Model'}, 'tracegroupgap': 0},
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| 364 |
+
'template': '...',
|
| 365 |
+
'title': {'text': 'Costs of execution of 800 test queries per model'},
|
| 366 |
+
'xaxis': {'anchor': 'y', 'domain': [0.0, 1.0], 'title': {'text': 'Model'}},
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| 367 |
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'yaxis': {'anchor': 'x', 'domain': [0.0, 1.0], 'title': {'text': 'Execution cost ($)'}}}
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| 368 |
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})",Costs of execution of 800 test queries per model,"Costs of experiments carried out to create this table, 800 test queries per model.
|
| 369 |
+
|
| 370 |
+
Note that pause and resume time cost was not included in the cost calculation for the models billed hourly.
|
| 371 |
+
|
| 372 |
+
Also although Gemini-Pro model is free for under 60 queries per minute we calculated the full cost for generating the tokens.","{""data"":[{""alignmentgroup"":""True"",""hovertemplate"":""Model=%{x}\u003cbr\u003eExecution cost ($)=%{y}\u003cextra\u003e\u003c\u002fextra\u003e"",""legendgroup"":""gpt-3.5-turbo"",""marker"":{""color"":""#636efa"",""pattern"":{""shape"":""""}},""name"":""gpt-3.5-turbo"",""offsetgroup"":""gpt-3.5-turbo"",""orientation"":""h"",""showlegend"":true,""textposition"":""auto"",""x"":[0.04228],""xaxis"":""x"",""y"":[""gpt-3.5-turbo""],""yaxis"":""y"",""type"":""bar""},{""alignmentgroup"":""True"",""hovertemplate"":""Model=%{x}\u003cbr\u003eExecution cost 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'legendgroup': 'TinyLlama/TinyLlama-1.1B-Chat-v1.0',
|
| 433 |
+
'marker': {'color': '#FFA15A', 'pattern': {'shape': ''}},
|
| 434 |
+
'name': 'TinyLlama/TinyLlama-1.1B-Chat-v1.0',
|
| 435 |
+
'offsetgroup': 'TinyLlama/TinyLlama-1.1B-Chat-v1.0',
|
| 436 |
+
'orientation': 'h',
|
| 437 |
+
'showlegend': True,
|
| 438 |
+
'textposition': 'auto',
|
| 439 |
+
'type': 'bar',
|
| 440 |
+
'x': array([1.1]),
|
| 441 |
+
'xaxis': 'x',
|
| 442 |
+
'y': array(['TinyLlama/TinyLlama-1.1B-Chat-v1.0'], dtype=object),
|
| 443 |
+
'yaxis': 'y'}],
|
| 444 |
+
'layout': {'barmode': 'relative',
|
| 445 |
+
'legend': {'title': {'text': 'Model'}, 'tracegroupgap': 0},
|
| 446 |
+
'template': '...',
|
| 447 |
+
'title': {'text': 'Model sizes in billions of parameters'},
|
| 448 |
+
'xaxis': {'anchor': 'y', 'domain': [0.0, 1.0], 'title': {'text': 'Model'}},
|
| 449 |
+
'yaxis': {'anchor': 'x', 'domain': [0.0, 1.0], 'title': {'text': 'Model size (B params)'}}}
|
| 450 |
+
})",Model sizes in billions of parameters,"Model sizes based on release blogs and documentation of their respective developers.
|
| 451 |
+
|
| 452 |
+
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data/output_plots.csv
ADDED
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'name': 'llama-2-70b-chat',
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'legend': {'title': {'text': 'Model'}, 'tracegroupgap': 0},
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'template': '...',
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'title': {'text': 'Chunks mean size in characters plot'},
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