piotr-szleg-bards-ai commited on
Commit
f829331
·
1 Parent(s): 2980f51

2024-02-14 10:47:29 Publish script update

Browse files
app.py CHANGED
@@ -3,6 +3,10 @@ import re
3
  import gradio as gr
4
  import pandas as pd
5
  import plotly
 
 
 
 
6
  from pandas.api.types import is_numeric_dtype
7
 
8
  from pipeline.config import LLMBoardConfig, QueriesConfig
@@ -61,15 +65,15 @@ model_costs_df = pd.read_csv("data/2024-02-05 12:03:45.281624_model_costs.csv")
61
  time_of_day_plots = pd.read_csv("data/time_of_day_plots.csv")
62
  output_plots = pd.read_csv("data/output_plots.csv")
63
 
64
- searched_model_name = ""
65
  collapse_languages = False
66
  collapse_output_method = False
67
 
68
 
69
  def filter_dataframes(input: str):
70
- global searched_model_name
71
  input = input.lower()
72
- searched_model_name = input
73
  return dataframes()
74
 
75
 
@@ -96,7 +100,7 @@ def collapse_output_method_toggle():
96
 
97
 
98
  def dataframes():
99
- global collapse_languages, collapse_output_method, searched_model_name, summary_df, time_of_day_comparison_df, model_costs_df
100
 
101
  summary_df_columns = summary_df.columns.to_list()
102
  group_columns = LLMBoardConfig().group_columns.copy()
@@ -108,12 +112,23 @@ def dataframes():
108
  group_columns.remove("template_name")
109
 
110
  summary_df_processed = summary_df[summary_df_columns].groupby(by=group_columns).mean().reset_index()
 
 
 
 
 
 
 
 
 
 
 
 
 
111
  return (
112
- dataframe_style(summary_df_processed[summary_df_processed.model.str.lower().str.contains(searched_model_name)]),
113
- dataframe_style(
114
- time_of_day_comparison_df[time_of_day_comparison_df.model.str.lower().str.contains(searched_model_name)]
115
- ),
116
- dataframe_style(model_costs_df[model_costs_df.model.str.lower().str.contains(searched_model_name)]),
117
  )
118
 
119
 
@@ -138,11 +153,58 @@ def snake_case_to_title(text):
138
  return " ".join(title_words)
139
 
140
 
141
- filter_textbox = gr.Textbox(label="Model name part", scale=2)
142
- filter_button = gr.Button("Filter dataframes by model name", scale=1)
143
  collapse_languages_button = gr.Button("Collapse languages")
144
  collapse_output_method_button = gr.Button("Collapse output method")
145
  last_textbox = 0
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
146
 
147
  with gr.Blocks() as demo:
148
  gr.HTML("<h1>LLM Board</h1>")
@@ -150,6 +212,9 @@ with gr.Blocks() as demo:
150
  with gr.Row():
151
  filter_textbox.render()
152
  filter_button.render()
 
 
 
153
 
154
  with gr.Tab("About this project"):
155
  gr.Markdown(
@@ -158,12 +223,7 @@ with gr.Blocks() as demo:
158
  )
159
  )
160
  with gr.Tab("General plots"):
161
- for index, row in general_plots.iterrows():
162
- plot = plotly.io.from_json(row["plot_json"])
163
- plot.update_layout(autosize=True)
164
- gr.Plot(plot, label=row["header"], scale=1)
165
- if pd.notna(row["description"]):
166
- gr.Markdown(str(row["description"]))
167
  with gr.Tab("Output characteristics"):
168
  with gr.Row():
169
  collapse_languages_button.render()
@@ -179,17 +239,10 @@ To count words we split the output string by whitespace `\w` regex character.
179
 
180
  Chunk sizes are measured in the characters count."""
181
  )
182
- for index, row in output_plots.iterrows():
183
- plot = plotly.io.from_json(row["plot_json"])
184
- plot.update_layout(autosize=True)
185
- gr.Plot(plot, label=row["header"], scale=1)
186
-
187
  with gr.Tab("Performance by time of the day"):
188
  # display only first plot for all models
189
- for index, row in time_of_day_plots[0:1].iterrows():
190
- plot = plotly.io.from_json(row["plot_json"])
191
- plot.update_layout(autosize=True)
192
- gr.Plot(plot, label=row["header"], scale=1)
193
  time_periods_explanation_ui = gr.DataFrame(
194
  dataframe_style(time_periods_explanation_df), label="Times of day ranges"
195
  )
@@ -206,10 +259,7 @@ Measurements were made during a normal work week.
206
  """
207
  )
208
  # display rest of the plots
209
- for index, row in time_of_day_plots[1:].iterrows():
210
- plot = plotly.io.from_json(row["plot_json"])
211
- plot.update_layout(autosize=True)
212
- gr.Plot(plot, label=row["header"], scale=1)
213
 
214
  with gr.Tab("Costs comparison"):
215
  models_costs_ui = gr.DataFrame(dataframe_style(model_costs_df), label="Costs comparison")
@@ -235,6 +285,18 @@ Note that pause and resume time cost was not included in the "Cost Per Token" co
235
  outputs=[summary_ui, time_of_day_comparison_ui, models_costs_ui],
236
  api_name="filter_dataframes",
237
  )
 
 
 
 
 
 
 
 
 
 
 
 
238
  collapse_languages_button.click(
239
  fn=collapse_languages_toggle,
240
  outputs=[summary_ui, collapse_languages_button],
 
3
  import gradio as gr
4
  import pandas as pd
5
  import plotly
6
+ import io
7
+ import plotly.express as px
8
+ import json
9
+
10
  from pandas.api.types import is_numeric_dtype
11
 
12
  from pipeline.config import LLMBoardConfig, QueriesConfig
 
65
  time_of_day_plots = pd.read_csv("data/time_of_day_plots.csv")
66
  output_plots = pd.read_csv("data/output_plots.csv")
67
 
68
+ searched_query = ""
69
  collapse_languages = False
70
  collapse_output_method = False
71
 
72
 
73
  def filter_dataframes(input: str):
74
+ global searched_query
75
  input = input.lower()
76
+ searched_query = input
77
  return dataframes()
78
 
79
 
 
100
 
101
 
102
  def dataframes():
103
+ global collapse_languages, collapse_output_method, searched_query, summary_df, time_of_day_comparison_df, model_costs_df
104
 
105
  summary_df_columns = summary_df.columns.to_list()
106
  group_columns = LLMBoardConfig().group_columns.copy()
 
112
  group_columns.remove("template_name")
113
 
114
  summary_df_processed = summary_df[summary_df_columns].groupby(by=group_columns).mean().reset_index()
115
+
116
+ searched_model_names = searched_query.split("|")
117
+ searched_model_names = [n.lower().strip() for n in searched_model_names]
118
+ searched_model_names = [n for n in searched_model_names if n]
119
+
120
+ def for_dataframe(df):
121
+ if not searched_model_names:
122
+ return df
123
+ return dataframe_style(pd.concat(
124
+ df[df.model.str.lower().str.contains(n)]
125
+ for n in searched_model_names
126
+ ))
127
+
128
  return (
129
+ for_dataframe(summary_df_processed),
130
+ for_dataframe(time_of_day_comparison_df),
131
+ for_dataframe(model_costs_df)
 
 
132
  )
133
 
134
 
 
153
  return " ".join(title_words)
154
 
155
 
156
+ filter_textbox = gr.Textbox(label="Model name parts *", scale=2)
157
+ filter_button = gr.Button("Filter", scale=1)
158
  collapse_languages_button = gr.Button("Collapse languages")
159
  collapse_output_method_button = gr.Button("Collapse output method")
160
  last_textbox = 0
161
+ plots = []
162
+ single_model_plots = []
163
+
164
+ def filter_plots(searched_query:str):
165
+ searched_model_names = searched_query.split("|")
166
+ searched_model_names = [n.lower().strip() for n in searched_model_names]
167
+ searched_model_names = [n for n in searched_model_names if n]
168
+
169
+ def filter_dataframe(df):
170
+ if not searched_model_names:
171
+ return df
172
+ return pd.concat(
173
+ df[df.model.str.lower().str.contains(n)]
174
+ for n in searched_model_names
175
+ )
176
+
177
+ results = []
178
+ for plot_display, plot, row in plots:
179
+ visible = True
180
+ if "df" in row and pd.notna(row["df"]):
181
+ buffer = io.StringIO(row["df"])
182
+ df = pd.read_csv(buffer)
183
+ df = filter_dataframe(df)
184
+ plot = px.bar(
185
+ df,
186
+ **json.loads(row["arguments"])
187
+ )
188
+ plot.update_layout(autosize=True)
189
+ elif "for model" in row["header"] and searched_model_names:
190
+ plot_model = row["header"].split("for model")[1].lower()
191
+ if not any(n in plot_model for n in searched_model_names):
192
+ visible = False
193
+
194
+ results.append(gr.Plot(plot, visible=visible))
195
+
196
+ return results
197
+
198
+ def display_plot(plot_df_row):
199
+ row = dict(plot_df_row)
200
+ plot = plotly.io.from_json(row["plot_json"])
201
+ plot.update_layout(autosize=True)
202
+ plots.append((
203
+ gr.Plot(plot, label=row["header"], scale=1),
204
+ plot,
205
+ row))
206
+ if "description" in row and pd.notna(row["description"]):
207
+ gr.Markdown(str(row["description"]))
208
 
209
  with gr.Blocks() as demo:
210
  gr.HTML("<h1>LLM Board</h1>")
 
212
  with gr.Row():
213
  filter_textbox.render()
214
  filter_button.render()
215
+ gr.Markdown(
216
+ "\* You can use `|` operator to display multiple models at once, for example \"gpt|mistral|zephyr\""
217
+ )
218
 
219
  with gr.Tab("About this project"):
220
  gr.Markdown(
 
223
  )
224
  )
225
  with gr.Tab("General plots"):
226
+ general_plots.apply(display_plot, axis=1)
 
 
 
 
 
227
  with gr.Tab("Output characteristics"):
228
  with gr.Row():
229
  collapse_languages_button.render()
 
239
 
240
  Chunk sizes are measured in the characters count."""
241
  )
242
+ output_plots.apply(display_plot, axis=1)
 
 
 
 
243
  with gr.Tab("Performance by time of the day"):
244
  # display only first plot for all models
245
+ time_of_day_plots[0:1].apply(display_plot, axis=1)
 
 
 
246
  time_periods_explanation_ui = gr.DataFrame(
247
  dataframe_style(time_periods_explanation_df), label="Times of day ranges"
248
  )
 
259
  """
260
  )
261
  # display rest of the plots
262
+ time_of_day_plots[1:].apply(display_plot, axis=1)
 
 
 
263
 
264
  with gr.Tab("Costs comparison"):
265
  models_costs_ui = gr.DataFrame(dataframe_style(model_costs_df), label="Costs comparison")
 
285
  outputs=[summary_ui, time_of_day_comparison_ui, models_costs_ui],
286
  api_name="filter_dataframes",
287
  )
288
+ filter_button.click(
289
+ fn=filter_plots,
290
+ inputs=filter_textbox,
291
+ outputs=[v[0] for v in plots],
292
+ api_name="filter_plots",
293
+ )
294
+ filter_textbox.submit(
295
+ fn=filter_plots,
296
+ inputs=filter_textbox,
297
+ outputs=[v[0] for v in plots],
298
+ api_name="filter_plots",
299
+ )
300
  collapse_languages_button.click(
301
  fn=collapse_languages_toggle,
302
  outputs=[summary_ui, collapse_languages_button],
data/2024-02-13 12:59:41.960562_time_of_day_comparison.csv ADDED
@@ -0,0 +1,97 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ model,time_of_day,execution_time,characters_count,words_count
2
+ Chronos Hermes (13B),night,4.168874144554138,360.0,58.5
3
+ Falcon Instruct (7B),night,1.0781378746032715,42.0,7.5
4
+ LLaMA-2 Chat (7B),night,1.686687707901001,377.5,61.5
5
+ Mistral (7B) Instruct v0.2 (Together AI),night,1.7709604501724243,311.0,48.0
6
+ Mistral-7B-Instruct-v0.2,morning,3.734026002883911,362.9,58.35
7
+ Mistral-7B-Instruct-v0.2,afternoon,3.228973722457886,222.0625,32.25
8
+ Mistral-7B-Instruct-v0.2,late afternoon,3.2048643112182615,219.8625,31.8
9
+ Mistral-7B-Instruct-v0.2,evening,3.397640073299408,261.18333333333334,40.1
10
+ Mistral-7B-Instruct-v0.2,late evening,3.389284573495388,175.79375,25.68125
11
+ Mistral-7B-Instruct-v0.2,midnight,1.9149879813194275,37.8,2.95
12
+ Mixtral-8x7B-Instruct-v0.1,early morning,4.526968242530536,285.045,41.86
13
+ Mixtral-8x7B-Instruct-v0.1,morning,3.9661054956285575,304.82,47.28
14
+ Mixtral-8x7B-Instruct-v0.1,afternoon,5.362903979589355,369.3192307692308,54.353846153846156
15
+ Mixtral-8x7B-Instruct-v0.1,late afternoon,5.80184749175942,347.9681818181818,47.27272727272727
16
+ Mixtral-8x7B-Instruct-v0.1,evening,3.6435119574237023,326.69,48.545
17
+ Mixtral-8x7B-Instruct-v0.1,late evening,5.62397656769588,395.15714285714284,49.02857142857143
18
+ Mixtral-8x7B-Instruct-v0.1,midnight,4.639010797279158,323.0394736842105,42.69210526315789
19
+ Mixtral-8x7B-Instruct-v0.1,night,4.009439338194697,301.24545454545455,42.21818181818182
20
+ OpenHermes-2.5-Mistral (7B),night,3.330963373184204,179.5,28.0
21
+ RedPajama-INCITE Chat (7B),night,1.3475391864776611,34.5,5.0
22
+ Snorkel Mistral PairRM DPO (7B),night,4.399647831916809,404.5,60.5
23
+ TinyLlama/TinyLlama-1.1B-Chat-v1.0,early morning,2.0273348593711855,372.13,62.53
24
+ TinyLlama/TinyLlama-1.1B-Chat-v1.0,morning,1.9041210174560548,372.05,62.6
25
+ TinyLlama/TinyLlama-1.1B-Chat-v1.0,afternoon,1.8381905496120452,308.795,51.08
26
+ TinyLlama/TinyLlama-1.1B-Chat-v1.0,late afternoon,1.7547113946505954,285.17857142857144,46.9
27
+ TinyLlama/TinyLlama-1.1B-Chat-v1.0,evening,1.7984187936782836,313.99,51.96
28
+ TinyLlama/TinyLlama-1.1B-Chat-v1.0,late evening,1.5875422928068372,192.78333333333333,31.261111111111113
29
+ TinyLlama/TinyLlama-1.1B-Chat-v1.0,midnight,1.6341248273849487,210.2,34.2
30
+ TinyLlama/TinyLlama-1.1B-Chat-v1.0,night,2.0128010153770446,372.05,62.6
31
+ WizardLM v1.2 (13B),night,3.350038528442383,162.0,26.0
32
+ chat-bison,midnight,3.890243631601334,398.075,49.0
33
+ chat-bison (PaLM 2),early morning,2.4665334616388592,381.9214285714286,60.892857142857146
34
+ chat-bison (PaLM 2),morning,2.488477897644043,381.73,60.88
35
+ chat-bison (PaLM 2),afternoon,2.6460144804074215,376.28076923076924,55.965384615384615
36
+ chat-bison (PaLM 2),late afternoon,3.0387172081253744,381.1363636363636,53.35454545454545
37
+ chat-bison (PaLM 2),evening,2.688272579908371,367.07,55.89
38
+ chat-bison (PaLM 2),late evening,2.7250528037548065,382.725,52.95
39
+ chat-bison (PaLM 2),midnight,2.468383938074112,381.95,60.9
40
+ chat-bison (PaLM 2),night,2.460119960308075,381.92,60.94
41
+ chat-bison-32k,midnight,4.128177767992019,389.925,48.025
42
+ chat-bison-32k (PaLM 2 32K),early morning,9.141417106560299,335.75,53.85
43
+ chat-bison-32k (PaLM 2 32K),morning,7.7035503840446475,335.75,53.85
44
+ chat-bison-32k (PaLM 2 32K),afternoon,5.014458654477046,339.3692307692308,49.323076923076925
45
+ chat-bison-32k (PaLM 2 32K),late afternoon,4.5047362284226855,348.65909090909093,48.35454545454545
46
+ chat-bison-32k (PaLM 2 32K),evening,7.7332194912433625,329.46,49.5
47
+ chat-bison-32k (PaLM 2 32K),late evening,7.796841062307358,349.95,47.805
48
+ chat-bison-32k (PaLM 2 32K),midnight,7.7498266498247785,335.75,53.85
49
+ chat-bison-32k (PaLM 2 32K),night,6.491292915344238,335.75,53.85
50
+ gemini-pro,early morning,2.7453590130460435,381.2214285714286,60.964285714285715
51
+ gemini-pro,morning,2.497767536007628,371.93,59.39
52
+ gemini-pro,afternoon,2.816922114008949,360.62307692307695,53.25769230769231
53
+ gemini-pro,late afternoon,2.9268629640903114,364.57272727272726,50.1
54
+ gemini-pro,evening,2.86901999375759,366.4,55.045
55
+ gemini-pro,late evening,3.7189874940246117,390.9142857142857,51.35
56
+ gemini-pro,midnight,3.338477972348531,369.825,55.9125
57
+ gemini-pro,night,2.8375814715210272,374.8,60.04
58
+ gpt-3.5-turbo,early morning,3.787998208734724,403.74444444444447,47.34444444444444
59
+ gpt-3.5-turbo,morning,3.126271222697364,389.9888888888889,50.93888888888889
60
+ gpt-3.5-turbo,afternoon,3.9458200880459375,381.4428571428571,43.76190476190476
61
+ gpt-3.5-turbo,late afternoon,4.384064777692159,396.4357142857143,43.28333333333333
62
+ gpt-3.5-turbo,evening,3.5153889304115657,385.18095238095236,46.01428571428571
63
+ gpt-3.5-turbo,late evening,5.110168156187617,422.73510971786834,45.358934169278996
64
+ gpt-3.5-turbo,midnight,3.822115447632102,426.71923076923076,49.05
65
+ gpt-3.5-turbo,night,6.062970260473398,418.6192307692308,44.926923076923075
66
+ gpt-4,early morning,14.348626694414351,323.5388888888889,40.544444444444444
67
+ gpt-4,morning,12.759107512468733,338.18333333333334,46.85
68
+ gpt-4,afternoon,16.002364798386893,318.3095238095238,38.77142857142857
69
+ gpt-4,late afternoon,16.80607506932254,313.3595238095238,37.49285714285714
70
+ gpt-4,evening,13.841120740345547,318.0809523809524,40.19285714285714
71
+ gpt-4,late evening,14.298642643005493,314.336,37.012
72
+ gpt-4,midnight,12.3578163115329,334.075,41.35
73
+ gpt-4,night,12.813134506115546,316.93461538461537,37.93076923076923
74
+ gpt-4-turbo,early morning,11.555620827939775,357.65555555555557,47.21666666666667
75
+ gpt-4-turbo,morning,13.686854598257277,381.8888888888889,55.02777777777778
76
+ gpt-4-turbo,afternoon,13.997754749229976,351.01190476190476,46.03333333333333
77
+ gpt-4-turbo,late afternoon,22.320911452883767,381.65714285714284,47.35476190476191
78
+ gpt-4-turbo,evening,16.550320884159632,382.31666666666666,48.45
79
+ gpt-4-turbo,late evening,14.592236209392548,413.61,44.8
80
+ gpt-4-turbo,midnight,13.770663784850727,382.7613636363636,47.61818181818182
81
+ gpt-4-turbo,night,14.254795966698573,352.54615384615386,46.37692307692308
82
+ llama-2-70b-chat,early morning,2.8660141522424265,289.6642857142857,44.614285714285714
83
+ llama-2-70b-chat,morning,2.872361832027194,283.35,43.45
84
+ llama-2-70b-chat,afternoon,4.234376892130426,375.44615384615383,55.238461538461536
85
+ llama-2-70b-chat,late afternoon,3.6833307104881365,433.6636363636364,59.445454545454545
86
+ llama-2-70b-chat,evening,2.9706250462084185,317.245,47.4
87
+ llama-2-70b-chat,late evening,4.719581684340602,572.1689655172414,79.83103448275862
88
+ llama-2-70b-chat,midnight,3.249819871626402,346.8875,52.06875
89
+ llama-2-70b-chat,night,2.8264514451677147,313.71,48.42
90
+ zephyr-7b-beta,early morning,3.937663261095683,273.03333333333336,43.45
91
+ zephyr-7b-beta,morning,4.056525647640228,386.3,63.35
92
+ zephyr-7b-beta,afternoon,3.5789777278900146,277.6,44.016666666666666
93
+ zephyr-7b-beta,late afternoon,3.4592524923459447,248.59,38.9
94
+ zephyr-7b-beta,evening,3.5673056403795878,273.3666666666667,43.5
95
+ zephyr-7b-beta,late evening,3.45343524068594,176.23125,27.21875
96
+ zephyr-7b-beta,midnight,3.7253047794103624,217.82083333333333,33.67916666666667
97
+ zephyr-7b-beta,night,3.6954557319482166,216.55833333333334,33.46666666666667
data/2024-02-14 10:19:15.390578_time_of_day_comparison.csv ADDED
@@ -0,0 +1,101 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ model,time_of_day,execution_time,characters_count,words_count
2
+ Chronos Hermes (13B),night,4.168874144554138,360.0,58.5
3
+ Falcon Instruct (7B),night,1.0781378746032715,42.0,7.5
4
+ LLaMA-2 Chat (7B),night,1.686687707901001,377.5,61.5
5
+ Mistral (7B) Instruct v0.2 (Together AI),morning,1.8144107659657795,253.75,39.0
6
+ Mistral (7B) Instruct v0.2 (Together AI),afternoon,3.4312500613076344,541.5,58.875
7
+ Mistral (7B) Instruct v0.2 (Together AI),late afternoon,3.4016824527220293,563.8333333333334,62.375
8
+ Mistral (7B) Instruct v0.2 (Together AI),evening,11.989823137010847,581.5,69.14285714285714
9
+ Mistral (7B) Instruct v0.2 (Together AI),night,1.7709604501724243,311.0,48.0
10
+ Mistral-7B-Instruct-v0.2,morning,3.734026002883911,362.9,58.35
11
+ Mistral-7B-Instruct-v0.2,afternoon,3.228973722457886,222.0625,32.25
12
+ Mistral-7B-Instruct-v0.2,late afternoon,3.2048643112182615,219.8625,31.8
13
+ Mistral-7B-Instruct-v0.2,evening,3.397640073299408,261.18333333333334,40.1
14
+ Mistral-7B-Instruct-v0.2,late evening,3.389284573495388,175.79375,25.68125
15
+ Mistral-7B-Instruct-v0.2,midnight,1.9149879813194275,37.8,2.95
16
+ Mixtral-8x7B-Instruct-v0.1,early morning,4.526968242530536,285.045,41.86
17
+ Mixtral-8x7B-Instruct-v0.1,morning,3.9661054956285575,304.82,47.28
18
+ Mixtral-8x7B-Instruct-v0.1,afternoon,5.362903979589355,369.3192307692308,54.353846153846156
19
+ Mixtral-8x7B-Instruct-v0.1,late afternoon,5.80184749175942,347.9681818181818,47.27272727272727
20
+ Mixtral-8x7B-Instruct-v0.1,evening,3.6435119574237023,326.69,48.545
21
+ Mixtral-8x7B-Instruct-v0.1,late evening,5.62397656769588,395.15714285714284,49.02857142857143
22
+ Mixtral-8x7B-Instruct-v0.1,midnight,4.639010797279158,323.0394736842105,42.69210526315789
23
+ Mixtral-8x7B-Instruct-v0.1,night,4.009439338194697,301.24545454545455,42.21818181818182
24
+ OpenHermes-2.5-Mistral (7B),night,3.330963373184204,179.5,28.0
25
+ RedPajama-INCITE Chat (7B),night,1.3475391864776611,34.5,5.0
26
+ Snorkel Mistral PairRM DPO (7B),night,4.399647831916809,404.5,60.5
27
+ TinyLlama/TinyLlama-1.1B-Chat-v1.0,early morning,2.0273348593711855,372.13,62.53
28
+ TinyLlama/TinyLlama-1.1B-Chat-v1.0,morning,1.9041210174560548,372.05,62.6
29
+ TinyLlama/TinyLlama-1.1B-Chat-v1.0,afternoon,1.8381905496120452,308.795,51.08
30
+ TinyLlama/TinyLlama-1.1B-Chat-v1.0,late afternoon,1.7547113946505954,285.17857142857144,46.9
31
+ TinyLlama/TinyLlama-1.1B-Chat-v1.0,evening,1.7984187936782836,313.99,51.96
32
+ TinyLlama/TinyLlama-1.1B-Chat-v1.0,late evening,1.5875422928068372,192.78333333333333,31.261111111111113
33
+ TinyLlama/TinyLlama-1.1B-Chat-v1.0,midnight,1.6341248273849487,210.2,34.2
34
+ TinyLlama/TinyLlama-1.1B-Chat-v1.0,night,2.0128010153770446,372.05,62.6
35
+ WizardLM v1.2 (13B),night,3.350038528442383,162.0,26.0
36
+ chat-bison,midnight,3.890243631601334,398.075,49.0
37
+ chat-bison (PaLM 2),early morning,2.4665334616388592,381.9214285714286,60.892857142857146
38
+ chat-bison (PaLM 2),morning,2.488477897644043,381.73,60.88
39
+ chat-bison (PaLM 2),afternoon,2.6460144804074215,376.28076923076924,55.965384615384615
40
+ chat-bison (PaLM 2),late afternoon,3.0387172081253744,381.1363636363636,53.35454545454545
41
+ chat-bison (PaLM 2),evening,2.688272579908371,367.07,55.89
42
+ chat-bison (PaLM 2),late evening,2.7250528037548065,382.725,52.95
43
+ chat-bison (PaLM 2),midnight,2.468383938074112,381.95,60.9
44
+ chat-bison (PaLM 2),night,2.460119960308075,381.92,60.94
45
+ chat-bison-32k,midnight,4.128177767992019,389.925,48.025
46
+ chat-bison-32k (PaLM 2 32K),early morning,9.141417106560299,335.75,53.85
47
+ chat-bison-32k (PaLM 2 32K),morning,7.7035503840446475,335.75,53.85
48
+ chat-bison-32k (PaLM 2 32K),afternoon,5.014458654477046,339.3692307692308,49.323076923076925
49
+ chat-bison-32k (PaLM 2 32K),late afternoon,4.5047362284226855,348.65909090909093,48.35454545454545
50
+ chat-bison-32k (PaLM 2 32K),evening,7.7332194912433625,329.46,49.5
51
+ chat-bison-32k (PaLM 2 32K),late evening,7.796841062307358,349.95,47.805
52
+ chat-bison-32k (PaLM 2 32K),midnight,7.7498266498247785,335.75,53.85
53
+ chat-bison-32k (PaLM 2 32K),night,6.491292915344238,335.75,53.85
54
+ gemini-pro,early morning,2.7453590130460435,381.2214285714286,60.964285714285715
55
+ gemini-pro,morning,2.497767536007628,371.93,59.39
56
+ gemini-pro,afternoon,2.816922114008949,360.62307692307695,53.25769230769231
57
+ gemini-pro,late afternoon,2.9268629640903114,364.57272727272726,50.1
58
+ gemini-pro,evening,2.86901999375759,366.4,55.045
59
+ gemini-pro,late evening,3.7189874940246117,390.9142857142857,51.35
60
+ gemini-pro,midnight,3.338477972348531,369.825,55.9125
61
+ gemini-pro,night,2.8375814715210272,374.8,60.04
62
+ gpt-3.5-turbo,early morning,3.787998208734724,403.74444444444447,47.34444444444444
63
+ gpt-3.5-turbo,morning,3.126271222697364,389.9888888888889,50.93888888888889
64
+ gpt-3.5-turbo,afternoon,3.9458200880459375,381.4428571428571,43.76190476190476
65
+ gpt-3.5-turbo,late afternoon,4.384064777692159,396.4357142857143,43.28333333333333
66
+ gpt-3.5-turbo,evening,3.5153889304115657,385.18095238095236,46.01428571428571
67
+ gpt-3.5-turbo,late evening,5.110168156187617,422.73510971786834,45.358934169278996
68
+ gpt-3.5-turbo,midnight,3.822115447632102,426.71923076923076,49.05
69
+ gpt-3.5-turbo,night,6.062970260473398,418.6192307692308,44.926923076923075
70
+ gpt-4,early morning,14.348626694414351,323.5388888888889,40.544444444444444
71
+ gpt-4,morning,12.759107512468733,338.18333333333334,46.85
72
+ gpt-4,afternoon,16.002364798386893,318.3095238095238,38.77142857142857
73
+ gpt-4,late afternoon,16.80607506932254,313.3595238095238,37.49285714285714
74
+ gpt-4,evening,13.841120740345547,318.0809523809524,40.19285714285714
75
+ gpt-4,late evening,14.298642643005493,314.336,37.012
76
+ gpt-4,midnight,12.3578163115329,334.075,41.35
77
+ gpt-4,night,12.813134506115546,316.93461538461537,37.93076923076923
78
+ gpt-4-turbo,early morning,11.555620827939775,357.65555555555557,47.21666666666667
79
+ gpt-4-turbo,morning,13.686854598257277,381.8888888888889,55.02777777777778
80
+ gpt-4-turbo,afternoon,13.997754749229976,351.01190476190476,46.03333333333333
81
+ gpt-4-turbo,late afternoon,22.320911452883767,381.65714285714284,47.35476190476191
82
+ gpt-4-turbo,evening,16.550320884159632,382.31666666666666,48.45
83
+ gpt-4-turbo,late evening,14.592236209392548,413.61,44.8
84
+ gpt-4-turbo,midnight,13.770663784850727,382.7613636363636,47.61818181818182
85
+ gpt-4-turbo,night,14.254795966698573,352.54615384615386,46.37692307692308
86
+ llama-2-70b-chat,early morning,2.8660141522424265,289.6642857142857,44.614285714285714
87
+ llama-2-70b-chat,morning,2.872361832027194,283.35,43.45
88
+ llama-2-70b-chat,afternoon,4.234376892130426,375.44615384615383,55.238461538461536
89
+ llama-2-70b-chat,late afternoon,3.6833307104881365,433.6636363636364,59.445454545454545
90
+ llama-2-70b-chat,evening,2.9706250462084185,317.245,47.4
91
+ llama-2-70b-chat,late evening,4.719581684340602,572.1689655172414,79.83103448275862
92
+ llama-2-70b-chat,midnight,3.249819871626402,346.8875,52.06875
93
+ llama-2-70b-chat,night,2.8264514451677147,313.71,48.42
94
+ zephyr-7b-beta,early morning,3.937663261095683,273.03333333333336,43.45
95
+ zephyr-7b-beta,morning,4.056525647640228,386.3,63.35
96
+ zephyr-7b-beta,afternoon,3.5789777278900146,277.6,44.016666666666666
97
+ zephyr-7b-beta,late afternoon,3.4592524923459447,248.59,38.9
98
+ zephyr-7b-beta,evening,3.5673056403795878,273.3666666666667,43.5
99
+ zephyr-7b-beta,late evening,3.45343524068594,176.23125,27.21875
100
+ zephyr-7b-beta,midnight,3.7253047794103624,217.82083333333333,33.67916666666667
101
+ zephyr-7b-beta,night,3.6954557319482166,216.55833333333334,33.46666666666667
data/general_plots.csv CHANGED
The diff for this file is too large to render. See raw diff
 
data/output_plots.csv CHANGED
@@ -1,4 +1,4 @@
1
- plot_object,header,plot_json
2
  "Figure({
3
  'data': [{'alignmentgroup': 'True',
4
  'hovertemplate': 'Model=%{x}<br>Execution time (s)=%{y}<extra></extra>',
@@ -199,7 +199,21 @@ plot_object,header,plot_json
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
- })",Plot of execution time (s),"{""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"":[3.183657696204526],""yaxis"":""y"",""type"":""bar""},{""alignmentgroup"":""True"",""hovertemplate"":""Model=%{x}\u003cbr\u003eExecution time 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(characters)=%{y}\u003cextra\u003e\u003c\u002fextra\u003e"",""legendgroup"":""zephyr-7b-beta"",""marker"":{""color"":""#00cc96"",""pattern"":{""shape"":""""}},""name"":""zephyr-7b-beta"",""offsetgroup"":""zephyr-7b-beta"",""orientation"":""v"",""showlegend"":true,""textposition"":""auto"",""x"":[""zephyr-7b-beta""],""xaxis"":""x"",""y"":[2.711003271075019],""yaxis"":""y"",""type"":""bar""}],""layout"":{""template"":{""data"":{""histogram2dcontour"":[{""type"":""histogram2dcontour"",""colorbar"":{""outlinewidth"":0,""ticks"":""""},""colorscale"":[[0.0,""#0d0887""],[0.1111111111111111,""#46039f""],[0.2222222222222222,""#7201a8""],[0.3333333333333333,""#9c179e""],[0.4444444444444444,""#bd3786""],[0.5555555555555556,""#d8576b""],[0.6666666666666666,""#ed7953""],[0.7777777777777778,""#fb9f3a""],[0.8888888888888888,""#fdca26""],[1.0,""#f0f921""]]}],""choropleth"":[{""type"":""choropleth"",""colorbar"":{""outlinewidth"":0,""ticks"":""""}}],""histogram2d"":[{""type"":""histogram2d"",""colorbar"":{""outlinewidth"":0,""ticks"":""""},""colorscale"":[[0.0,""#0d0887""],[0.1111111111111111,""#46039f""],[0.2222222222222222,""#7201a8""],[0.3333333333333333,""#9c179e""],[0.4444444444444444,""#bd3786""],[0.5555555555555556,""#d8576b""],[0.6666666666666666,""#ed7953""],[0.7777777777777778,""#fb9f3a""],[0.8888888888888888,""#fdca26""],[1.0,""#f0f921""]]}],""heatmap"":[{""type"":""heatmap"",""colorbar"":{""outlinewidth"":0,""ticks"":""""},""colorscale"":[[0.0,""#0d0887""],[0.1111111111111111,""#46039f""],[0.2222222222222222,""#7201a8""],[0.3333333333333333,""#9c179e""],[0.4444444444444444,""#bd3786""],[0.5555555555555556,""#d8576b""],[0.6666666666666666,""#ed7953""],[0.7777777777777778,""#fb9f3a""],[0.8888888888888888,""#fdca26""],[1.0,""#f0f921""]]}],""heatmapgl"":[{""type"":""heatmapgl"",""colorbar"":{""outlinewidth"":0,""ticks"":""""},""colorscale"":[[0.0,""#0d0887""],[0.1111111111111111,""#46039f""],[0.2222222222222222,""#7201a8""],[0.3333333333333333,""#9c179e""],[0.4444444444444444,""#bd3786""],[0.5555555555555556,""#d8576b""],[0.6666666666666666,""#ed7953""],[0.7777777777777778,""#fb9f3a""],[0.8888888888888888,""#fdca26""],[1.0,""#f0f921""]]}],""contourcarpet"":[{""type"":""contourcarpet"",""colorbar"":{""outlinewidth"":0,""ticks"":""""}}],""contour"":[{""type"":""contour"",""colorbar"":{""outlinewidth"":0,""ticks"":""""},""colorscale"":[[0.0,""#0d0887""],[0.1111111111111111,""#46039f""],[0.2222222222222222,""#7201a8""],[0.3333333333333333,""#9c179e""],[0.4444444444444444,""#bd3786""],[0.5555555555555556,""#d8576b""],[0.6666666666666666,""#ed7953""],[0.7777777777777778,""#fb9f3a""],[0.8888888888888888,""#fdca26""],[1.0,""#f0f921""]]}],""surface"":[{""type"":""surface"",""colorbar"":{""outlinewidth"":0,""ticks"":""""},""colorscale"":[[0.0,""#0d0887""],[0.1111111111111111,""#46039f""],[0.2222222222222222,""#7201a8""],[0.3333333333333333,""#9c179e""],[0.4444444444444444,""#bd3786""],[0.5555555555555556,""#d8576b""],[0.6666666666666666,""#ed7953""],[0.7777777777777778,""#fb9f3a""],[0.8888888888888888,""#fdca26""],[1.0,""#f0f921""]]}],""mesh3d"":[{""type"":""mesh3d"",""colorbar"":{""outlinewidth"":0,""ticks"":""""}}],""scatter"":[{""fillpattern"":{""fillmode"":""overlay"",""size"":10,""solidity"":0.2},""type"":""scatter""}],""parcoords"":[{""type"":""parcoords"",""line"":{""colorbar"":{""outlinewidth"":0,""ticks"":""""}}}],""scatterpolargl"":[{""type"":""scatterpolargl"",""marker"":{""colorbar"":{""outlinewidth"":0,""ticks"":""""}}}],""bar"":[{""error_x"":{""color"":""#2a3f5f""},""error_y"":{""color"":""#2a3f5f""},""marker"":{""line"":{""color"":""#E5ECF6"",""width"":0.5},""pattern"":{""fillmode"":""overlay"",""size"":10,""solidity"":0.2}},""type"":""bar""}],""scattergeo"":[{""type"":""scattergeo"",""marker"":{""colorbar"":{""outlinewidth"":0,""ticks"":""""}}}],""scatterpolar"":[{""type"":""scatterpolar"",""marker"":{""colorbar"":{""outlinewidth"":0,""ticks"":""""}}}],""histogram"":[{""marker"":{""pattern"":{""fillmode"":""overlay"",""size"":10,""solidity"":0.2}},""type"":""histogram""}],""scattergl"":[{""type"":""scattergl"",""marker"":{""colorbar"":{""outlinewidth"":0,""ticks"":""""}}}],""scatter3d"":[{""type"":""scatter3d"",""line"":{""colorbar"":{""outlinewidth"":0,""ticks"":""""}},""marker"":{""colorbar"":{""outlinewidth"":0,""ticks"":""""}}}],""scattermapbox"":[{""type"":""scattermapbox"",""marker"":{""colorbar"":{""outlinewidth"":0,""ticks"":""""}}}],""scatterternary"":[{""type"":""scatterternary"",""marker"":{""colorbar"":{""outlinewidth"":0,""ticks"":""""}}}],""scattercarpet"":[{""type"":""scattercarpet"",""marker"":{""colorbar"":{""outlinewidth"":0,""ticks"":""""}}}],""carpet"":[{""aaxis"":{""endlinecolor"":""#2a3f5f"",""gridcolor"":""white"",""linecolor"":""white"",""minorgridcolor"":""white"",""startlinecolor"":""#2a3f5f""},""baxis"":{""endlinecolor"":""#2a3f5f"",""gridcolor"":""white"",""linecolor"":""white"",""minorgridcolor"":""white"",""startlinecolor"":""#2a3f5f""},""type"":""carpet""}],""table"":[{""cells"":{""fill"":{""color"":""#EBF0F8""},""line"":{""color"":""white""}},""header"":{""fill"":{""color"":""#C8D4E3""},""line"":{""color"":""white""}},""type"":""table""}],""barpolar"":[{""marker"":{""line"":{""color"":""#E5ECF6"",""width"":0.5},""pattern"":{""fillmode"":""overlay"",""size"":10,""solidity"":0.2}},""type"":""barpolar""}],""pie"":[{""automargin"":true,""type"":""pie""}]},""layout"":{""autotypenumbers"":""strict"",""colorway"":[""#636efa"",""#EF553B"",""#00cc96"",""#ab63fa"",""#FFA15A"",""#19d3f3"",""#FF6692"",""#B6E880"",""#FF97FF"",""#FECB52""],""font"":{""color"":""#2a3f5f""},""hovermode"":""closest"",""hoverlabel"":{""align"":""left""},""paper_bgcolor"":""white"",""plot_bgcolor"":""#E5ECF6"",""polar"":{""bgcolor"":""#E5ECF6"",""angularaxis"":{""gridcolor"":""white"",""linecolor"":""white"",""ticks"":""""},""radialaxis"":{""gridcolor"":""white"",""linecolor"":""white"",""ticks"":""""}},""ternary"":{""bgcolor"":""#E5ECF6"",""aaxis"":{""gridcolor"":""white"",""linecolor"":""white"",""ticks"":""""},""baxis"":{""gridcolor"":""white"",""linecolor"":""white"",""ticks"":""""},""caxis"":{""gridcolor"":""white"",""linecolor"":""white"",""ticks"":""""}},""coloraxis"":{""colorbar"":{""outlinewidth"":0,""ticks"":""""}},""colorscale"":{""sequential"":[[0.0,""#0d0887""],[0.1111111111111111,""#46039f""],[0.2222222222222222,""#7201a8""],[0.3333333333333333,""#9c179e""],[0.4444444444444444,""#bd3786""],[0.5555555555555556,""#d8576b""],[0.6666666666666666,""#ed7953""],[0.7777777777777778,""#fb9f3a""],[0.8888888888888888,""#fdca26""],[1.0,""#f0f921""]],""sequentialminus"":[[0.0,""#0d0887""],[0.1111111111111111,""#46039f""],[0.2222222222222222,""#7201a8""],[0.3333333333333333,""#9c179e""],[0.4444444444444444,""#bd3786""],[0.5555555555555556,""#d8576b""],[0.6666666666666666,""#ed7953""],[0.7777777777777778,""#fb9f3a""],[0.8888888888888888,""#fdca26""],[1.0,""#f0f921""]],""diverging"":[[0,""#8e0152""],[0.1,""#c51b7d""],[0.2,""#de77ae""],[0.3,""#f1b6da""],[0.4,""#fde0ef""],[0.5,""#f7f7f7""],[0.6,""#e6f5d0""],[0.7,""#b8e186""],[0.8,""#7fbc41""],[0.9,""#4d9221""],[1,""#276419""]]},""xaxis"":{""gridcolor"":""white"",""linecolor"":""white"",""ticks"":"""",""title"":{""standoff"":15},""zerolinecolor"":""white"",""automargin"":true,""zerolinewidth"":2},""yaxis"":{""gridcolor"":""white"",""linecolor"":""white"",""ticks"":"""",""title"":{""standoff"":15},""zerolinecolor"":""white"",""automargin"":true,""zerolinewidth"":2},""scene"":{""xaxis"":{""backgroundcolor"":""#E5ECF6"",""gridcolor"":""white"",""linecolor"":""white"",""showbackground"":true,""ticks"":"""",""zerolinecolor"":""white"",""gridwidth"":2},""yaxis"":{""backgroundcolor"":""#E5ECF6"",""gridcolor"":""white"",""linecolor"":""white"",""showbackground"":true,""ticks"":"""",""zerolinecolor"":""white"",""gridwidth"":2},""zaxis"":{""backgroundcolor"":""#E5ECF6"",""gridcolor"":""white"",""linecolor"":""white"",""showbackground"":true,""ticks"":"""",""zerolinecolor"":""white"",""gridwidth"":2}},""shapedefaults"":{""line"":{""color"":""#2a3f5f""}},""annotationdefaults"":{""arrowcolor"":""#2a3f5f"",""arrowhead"":0,""arrowwidth"":1},""geo"":{""bgcolor"":""white"",""landcolor"":""#E5ECF6"",""subunitcolor"":""white"",""showland"":true,""showlakes"":true,""lakecolor"":""white""},""title"":{""x"":0.05},""mapbox"":{""style"":""light""}}},""xaxis"":{""anchor"":""y"",""domain"":[0.0,1.0],""title"":{""text"":""Model""},""categoryorder"":""array"",""categoryarray"":[""Mistral-7B-Instruct-v0.2"",""Mixtral-8x7B-Instruct-v0.1"",""TinyLlama\u002fTinyLlama-1.1B-Chat-v1.0"",""chat-bison"",""chat-bison (PaLM 2)"",""chat-bison-32k"",""chat-bison-32k (PaLM 2 32K)"",""gemini-pro"",""gpt-3.5-turbo"",""gpt-4"",""gpt-4-turbo"",""llama-2-70b-chat"",""zephyr-7b-beta""]},""yaxis"":{""anchor"":""x"",""domain"":[0.0,1.0],""title"":{""text"":""Chunks mean size (characters)""}},""legend"":{""title"":{""text"":""Model""},""tracegroupgap"":0},""title"":{""text"":""Plot of chunks mean size (characters)""},""barmode"":""relative""}}",",model,chunk_sizes_mean
633
+ 0,Mistral-7B-Instruct-v0.2,2.6941392346739055
634
+ 1,Mixtral-8x7B-Instruct-v0.1,2.9569017240855695
635
+ 2,TinyLlama/TinyLlama-1.1B-Chat-v1.0,3.146639511119026
636
+ 3,chat-bison,61.46100879376141
637
+ 4,chat-bison (PaLM 2),57.12081730998353
638
+ 5,chat-bison-32k,62.01633272616879
639
+ 6,chat-bison-32k (PaLM 2 32K),56.57639873825073
640
+ 7,gemini-pro,142.5030152620613
641
+ 8,gpt-3.5-turbo,3.0439941593593898
642
+ 9,gpt-4,3.018755174356351
643
+ 10,gpt-4-turbo,3.0126559039321035
644
+ 11,llama-2-70b-chat,3.2003241836714382
645
+ 12,zephyr-7b-beta,2.711003271075019
646
+ ","{""x"": ""model"", ""y"": ""chunk_sizes_mean"", ""color"": ""model"", ""labels"": {""model"": ""Model"", ""chunk_sizes_mean"": ""Chunks mean size (characters)""}, ""title"": ""Plot of chunks mean size (characters)""}"
data/time_of_day_plots.csv CHANGED
The diff for this file is too large to render. See raw diff
 
pipeline/models.py CHANGED
@@ -16,6 +16,7 @@ class Model(object):
16
  cost_per_million_input_tokens: int = None
17
  cost_per_million_output_tokens: int = None
18
  input_size: int = None
 
19
 
20
  def __post_init__(self):
21
  self.cost_per_million_input_tokens = self.cost_per_million_input_tokens or self.cost_per_million_tokens
@@ -144,10 +145,328 @@ MODELS = [
144
  cost_per_million_input_tokens=0.25,
145
  cost_per_million_output_tokens=0.5,
146
  ),
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
147
  ]
148
-
149
- """
150
- MODELS = [model for model in MODELS
151
- if model.model_name=="together_ai/mistralai/Mixtral-8x7B-Instruct-v0.1"
152
- or model.model_name=="huggingface/HuggingFaceH4/zephyr-7b-beta"]
153
- """
 
16
  cost_per_million_input_tokens: int = None
17
  cost_per_million_output_tokens: int = None
18
  input_size: int = None
19
+ selected: bool = False
20
 
21
  def __post_init__(self):
22
  self.cost_per_million_input_tokens = self.cost_per_million_input_tokens or self.cost_per_million_tokens
 
145
  cost_per_million_input_tokens=0.25,
146
  cost_per_million_output_tokens=0.5,
147
  ),
148
+ # new models:
149
+ Model(
150
+ "01-ai Yi Chat (34B)",
151
+ "together_ai/zero-one-ai/Yi-34B-Chat",
152
+ None,
153
+ "Together AI",
154
+ input_size=4096,
155
+ # selected=True,
156
+ ),
157
+ Model(
158
+ "Chronos Hermes (13B)",
159
+ "together_ai/Austism/chronos-hermes-13b",
160
+ None,
161
+ "Together AI",
162
+ input_size=2048,
163
+ # selected=True,
164
+ ),
165
+ Model(
166
+ "Deepseek Coder Instruct (33B)",
167
+ "together_ai/deepseek-ai/deepseek-coder-33b-instruct",
168
+ None,
169
+ "Together AI",
170
+ input_size=16384,
171
+ ),
172
+ Model(
173
+ "Platypus2 Instruct (70B)",
174
+ "together_ai/garage-bAInd/Platypus2-70B-instruct",
175
+ None,
176
+ "Together AI",
177
+ input_size=4096,
178
+ ),
179
+ Model(
180
+ "MythoMax-L2 (13B)",
181
+ "together_ai/Gryphe/MythoMax-L2-13b",
182
+ None,
183
+ "Together AI",
184
+ input_size=4096,
185
+ ),
186
+ Model(
187
+ "Vicuna v1.5 (13B)",
188
+ "together_ai/lmsys/vicuna-13b-v1.5",
189
+ None,
190
+ "Together AI",
191
+ input_size=4096,
192
+ ),
193
+ Model("Vicuna v1.5 (7B)", "together_ai/lmsys/vicuna-7b-v1.5", None, "Together AI", input_size=4096, selected=True),
194
+ Model(
195
+ "Code Llama Instruct (13B)",
196
+ "together_ai/codellama/CodeLlama-13b-Instruct-hf",
197
+ None,
198
+ "Together AI",
199
+ input_size=16384,
200
+ ),
201
+ Model(
202
+ "Code Llama Instruct (34B)",
203
+ "together_ai/codellama/CodeLlama-34b-Instruct-hf",
204
+ None,
205
+ "Together AI",
206
+ input_size=16384,
207
+ ),
208
+ Model(
209
+ "Code Llama Instruct (70B)",
210
+ "together_ai/codellama/CodeLlama-70b-Instruct-hf",
211
+ None,
212
+ "Together AI",
213
+ input_size=4096,
214
+ ),
215
+ Model(
216
+ "Code Llama Instruct (7B)",
217
+ "together_ai/codellama/CodeLlama-7b-Instruct-hf",
218
+ None,
219
+ "Together AI",
220
+ input_size=16384,
221
+ ),
222
+ Model(
223
+ "LLaMA-2 Chat (13B)",
224
+ "together_ai/togethercomputer/llama-2-13b-chat",
225
+ None,
226
+ "Together AI",
227
+ input_size=4096,
228
+ ),
229
+ Model(
230
+ "LLaMA-2 Chat (70B)",
231
+ "together_ai/togethercomputer/llama-2-70b-chat",
232
+ None,
233
+ "Together AI",
234
+ input_size=4096,
235
+ ),
236
+ Model(
237
+ "LLaMA-2 Chat (7B)",
238
+ "together_ai/togethercomputer/llama-2-7b-chat",
239
+ None,
240
+ "Together AI",
241
+ input_size=4096,
242
+ # selected=True,
243
+ ),
244
+ Model(
245
+ "Mistral (7B) Instruct",
246
+ "together_ai/mistralai/Mistral-7B-Instruct-v0.1",
247
+ None,
248
+ "Together AI",
249
+ input_size=4096,
250
+ ),
251
+ Model(
252
+ "Mistral (7B) Instruct v0.2 (Together AI)",
253
+ "together_ai/mistralai/Mistral-7B-Instruct-v0.2",
254
+ None,
255
+ "Together AI",
256
+ input_size=32768,
257
+ selected=True,
258
+ ),
259
+ Model(
260
+ "Mixtral-8x7B Instruct (46.7B)",
261
+ "together_ai/mistralai/Mixtral-8x7B-Instruct-v0.1",
262
+ None,
263
+ "Together AI",
264
+ input_size=32768,
265
+ ),
266
+ Model(
267
+ "Nous Capybara v1.9 (7B)",
268
+ "together_ai/NousResearch/Nous-Capybara-7B-V1p9",
269
+ None,
270
+ "Together AI",
271
+ input_size=8192,
272
+ ),
273
+ Model(
274
+ "Nous Hermes 2 - Mixtral 8x7B-DPO (46.7B)",
275
+ "together_ai/NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO",
276
+ None,
277
+ "Together AI",
278
+ input_size=32768,
279
+ ),
280
+ Model(
281
+ "Nous Hermes 2 - Mixtral 8x7B-SFT (46.7B)",
282
+ "together_ai/NousResearch/Nous-Hermes-2-Mixtral-8x7B-SFT",
283
+ None,
284
+ "Together AI",
285
+ input_size=32768,
286
+ ),
287
+ Model(
288
+ "Nous Hermes LLaMA-2 (7B)",
289
+ "together_ai/NousResearch/Nous-Hermes-llama-2-7b",
290
+ None,
291
+ "Together AI",
292
+ input_size=4096,
293
+ ),
294
+ Model(
295
+ "Nous Hermes Llama-2 (13B)",
296
+ "together_ai/NousResearch/Nous-Hermes-Llama2-13b",
297
+ None,
298
+ "Together AI",
299
+ input_size=4096,
300
+ ),
301
+ Model(
302
+ "Nous Hermes-2 Yi (34B)",
303
+ "together_ai/NousResearch/Nous-Hermes-2-Yi-34B",
304
+ None,
305
+ "Together AI",
306
+ input_size=4096,
307
+ ),
308
+ Model(
309
+ "OpenChat 3.5 (7B)",
310
+ "together_ai/openchat/openchat-3.5-1210",
311
+ None,
312
+ "Together AI",
313
+ input_size=8192,
314
+ ),
315
+ Model(
316
+ "OpenOrca Mistral (7B) 8K",
317
+ "together_ai/Open-Orca/Mistral-7B-OpenOrca",
318
+ None,
319
+ "Together AI",
320
+ input_size=8192,
321
+ ),
322
+ Model(
323
+ "Qwen-Chat (7B)",
324
+ "together_ai/togethercomputer/Qwen-7B-Chat",
325
+ None,
326
+ "Together AI",
327
+ input_size=8192,
328
+ ),
329
+ Model(
330
+ "Qwen 1.5 Chat (0.5B)",
331
+ "together_ai/Qwen/Qwen1.5-0.5B-Chat",
332
+ None,
333
+ "Together AI",
334
+ input_size=32768,
335
+ ),
336
+ Model(
337
+ "Qwen 1.5 Chat (1.8B)",
338
+ "together_ai/Qwen/Qwen1.5-1.8B-Chat",
339
+ None,
340
+ "Together AI",
341
+ input_size=32768,
342
+ ),
343
+ Model(
344
+ "Qwen 1.5 Chat (4B)",
345
+ "together_ai/Qwen/Qwen1.5-4B-Chat",
346
+ None,
347
+ "Together AI",
348
+ input_size=32768,
349
+ ),
350
+ Model(
351
+ "Qwen 1.5 Chat (7B)", "together_ai/Qwen/Qwen1.5-7B-Chat", None, "Together AI", input_size=32768,
352
+ # selected=True
353
+ ),
354
+ Model(
355
+ "Qwen 1.5 Chat (14B)",
356
+ "together_ai/Qwen/Qwen1.5-14B-Chat",
357
+ None,
358
+ "Together AI",
359
+ input_size=32768,
360
+ ),
361
+ Model(
362
+ "Qwen 1.5 Chat (72B)",
363
+ "together_ai/Qwen/Qwen1.5-72B-Chat",
364
+ None,
365
+ "Together AI",
366
+ input_size=4096,
367
+ ),
368
+ Model(
369
+ "Snorkel Mistral PairRM DPO (7B)",
370
+ "together_ai/snorkelai/Snorkel-Mistral-PairRM-DPO",
371
+ None,
372
+ "Together AI",
373
+ input_size=32768,
374
+ # selected=True,
375
+ ),
376
+ Model(
377
+ "Alpaca (7B)",
378
+ "together_ai/togethercomputer/alpaca-7b",
379
+ None,
380
+ "Together AI",
381
+ input_size=2048,
382
+ ),
383
+ Model(
384
+ "OpenHermes-2-Mistral (7B)",
385
+ "teknium/OpenHermes-2-Mistral-7B",
386
+ None,
387
+ "Together AI",
388
+ input_size=8192,
389
+ ),
390
+ Model(
391
+ "OpenHermes-2.5-Mistral (7B)",
392
+ "together_ai/teknium/OpenHermes-2p5-Mistral-7B",
393
+ None,
394
+ "Together AI",
395
+ input_size=8192,
396
+ # selected=True,
397
+ ),
398
+ Model(
399
+ "Falcon Instruct (40B)",
400
+ "together_ai/togethercomputer/falcon-40b-instruct",
401
+ None,
402
+ "Together AI",
403
+ input_size=2048,
404
+ ),
405
+ Model(
406
+ "Falcon Instruct (7B)",
407
+ "together_ai/togethercomputer/falcon-7b-instruct",
408
+ None,
409
+ "Together AI",
410
+ input_size=2048,
411
+ # selected=True,
412
+ ),
413
+ Model(
414
+ "LLaMA-2-7B-32K-Instruct (7B)",
415
+ "together_ai/togethercomputer/Llama-2-7B-32K-Instruct",
416
+ None,
417
+ "Together AI",
418
+ input_size=32768,
419
+ ),
420
+ Model(
421
+ "RedPajama-INCITE Chat (3B)",
422
+ "together_ai/togethercomputer/RedPajama-INCITE-Chat-3B-v1",
423
+ None,
424
+ "Together AI",
425
+ input_size=2048,
426
+ ),
427
+ Model(
428
+ "RedPajama-INCITE Chat (7B)",
429
+ "together_ai/togethercomputer/RedPajama-INCITE-7B-Chat",
430
+ None,
431
+ "Together AI",
432
+ input_size=2048,
433
+ # selected=True,
434
+ ),
435
+ Model(
436
+ "StripedHyena Nous (7B)",
437
+ "together_ai/togethercomputer/StripedHyena-Nous-7B",
438
+ None,
439
+ "Together AI",
440
+ input_size=32768,
441
+ ),
442
+ Model(
443
+ "ReMM SLERP L2 (13B)",
444
+ "together_ai/Undi95/ReMM-SLERP-L2-13B",
445
+ None,
446
+ "Together AI",
447
+ input_size=4096,
448
+ ),
449
+ Model(
450
+ "Toppy M (7B)",
451
+ "together_ai/Undi95/Toppy-M-7B",
452
+ None,
453
+ "Together AI",
454
+ input_size=4096,
455
+ ),
456
+ Model(
457
+ "WizardLM v1.2 (13B)",
458
+ "together_ai/WizardLM/WizardLM-13B-V1.2",
459
+ None,
460
+ "Together AI",
461
+ input_size=4096,
462
+ # selected=True,
463
+ ),
464
+ Model(
465
+ "Upstage SOLAR Instruct v1 (11B)",
466
+ "together_ai/upstage/SOLAR-10.7B-Instruct-v1.0",
467
+ None,
468
+ "Together AI",
469
+ input_size=4096,
470
+ # selected=True,
471
+ ),
472
  ]