diff --git a/data/output_plots.csv b/data/output_plots.csv
index 7e08543dfb60ad7bdd1c694a7777c6b5ca9503c0..d7721a303cc797ae8aae31fe20a0e9f0843f3e8e 100644
--- a/data/output_plots.csv
+++ b/data/output_plots.csv
@@ -12,7 +12,7 @@ plot_object,header,plot_json,df,arguments,description
'type': 'bar',
'x': array(['Snorkel Mistral PairRM DPO (7B)'], dtype=object),
'xaxis': 'x',
- 'y': array([24.99005381]),
+ 'y': array([19.9867597]),
'yaxis': 'y'},
{'alignmentgroup': 'True',
'hovertemplate': 'Model=%{x}
Execution time (s)=%{y}',
@@ -26,7 +26,7 @@ plot_object,header,plot_json,df,arguments,description
'type': 'bar',
'x': array(['gpt-4'], dtype=object),
'xaxis': 'x',
- 'y': array([17.68367981]),
+ 'y': array([15.98077287]),
'yaxis': 'y'},
{'alignmentgroup': 'True',
'hovertemplate': 'Model=%{x}
Execution time (s)=%{y}',
@@ -40,7 +40,7 @@ plot_object,header,plot_json,df,arguments,description
'type': 'bar',
'x': array(['gpt-4-turbo'], dtype=object),
'xaxis': 'x',
- 'y': array([15.85205264]),
+ 'y': array([14.24463232]),
'yaxis': 'y'},
{'alignmentgroup': 'True',
'hovertemplate': 'Model=%{x}
Execution time (s)=%{y}',
@@ -54,7 +54,7 @@ plot_object,header,plot_json,df,arguments,description
'type': 'bar',
'x': array(['01-ai Yi Chat (34B)'], dtype=object),
'xaxis': 'x',
- 'y': array([10.34810258]),
+ 'y': array([10.99151355]),
'yaxis': 'y'},
{'alignmentgroup': 'True',
'hovertemplate': 'Model=%{x}
Execution time (s)=%{y}',
@@ -68,63 +68,63 @@ plot_object,header,plot_json,df,arguments,description
'type': 'bar',
'x': array(['Chronos Hermes (13B)'], dtype=object),
'xaxis': 'x',
- 'y': array([10.29452815]),
+ 'y': array([8.58972701]),
'yaxis': 'y'},
{'alignmentgroup': 'True',
'hovertemplate': 'Model=%{x}
Execution time (s)=%{y}',
- 'legendgroup': 'Mixtral-8x7B-Instruct-v0.1',
+ 'legendgroup': 'WizardLM v1.2 (13B)',
'marker': {'color': '#19d3f3', 'pattern': {'shape': ''}},
- 'name': 'Mixtral-8x7B-Instruct-v0.1',
- 'offsetgroup': 'Mixtral-8x7B-Instruct-v0.1',
+ 'name': 'WizardLM v1.2 (13B)',
+ 'offsetgroup': 'WizardLM v1.2 (13B)',
'orientation': 'v',
'showlegend': True,
'textposition': 'auto',
'type': 'bar',
- 'x': array(['Mixtral-8x7B-Instruct-v0.1'], dtype=object),
+ 'x': array(['WizardLM v1.2 (13B)'], dtype=object),
'xaxis': 'x',
- 'y': array([6.55972525]),
+ 'y': array([5.86888533]),
'yaxis': 'y'},
{'alignmentgroup': 'True',
'hovertemplate': 'Model=%{x}
Execution time (s)=%{y}',
- 'legendgroup': 'llama-2-70b-chat',
+ 'legendgroup': 'Mixtral-8x7B-Instruct-v0.1',
'marker': {'color': '#FF6692', 'pattern': {'shape': ''}},
- 'name': 'llama-2-70b-chat',
- 'offsetgroup': 'llama-2-70b-chat',
+ 'name': 'Mixtral-8x7B-Instruct-v0.1',
+ 'offsetgroup': 'Mixtral-8x7B-Instruct-v0.1',
'orientation': 'v',
'showlegend': True,
'textposition': 'auto',
'type': 'bar',
- 'x': array(['llama-2-70b-chat'], dtype=object),
+ 'x': array(['Mixtral-8x7B-Instruct-v0.1'], dtype=object),
'xaxis': 'x',
- 'y': array([6.06536523]),
+ 'y': array([5.52183281]),
'yaxis': 'y'},
{'alignmentgroup': 'True',
'hovertemplate': 'Model=%{x}
Execution time (s)=%{y}',
- 'legendgroup': 'WizardLM v1.2 (13B)',
+ 'legendgroup': 'chat-bison-32k (PaLM 2 32K)',
'marker': {'color': '#B6E880', 'pattern': {'shape': ''}},
- 'name': 'WizardLM v1.2 (13B)',
- 'offsetgroup': 'WizardLM v1.2 (13B)',
+ 'name': 'chat-bison-32k (PaLM 2 32K)',
+ 'offsetgroup': 'chat-bison-32k (PaLM 2 32K)',
'orientation': 'v',
'showlegend': True,
'textposition': 'auto',
'type': 'bar',
- 'x': array(['WizardLM v1.2 (13B)'], dtype=object),
+ 'x': array(['chat-bison-32k (PaLM 2 32K)'], dtype=object),
'xaxis': 'x',
- 'y': array([5.50723492]),
+ 'y': array([5.46443628]),
'yaxis': 'y'},
{'alignmentgroup': 'True',
'hovertemplate': 'Model=%{x}
Execution time (s)=%{y}',
- 'legendgroup': 'chat-bison-32k (PaLM 2 32K)',
+ 'legendgroup': 'llama-2-70b-chat',
'marker': {'color': '#FF97FF', 'pattern': {'shape': ''}},
- 'name': 'chat-bison-32k (PaLM 2 32K)',
- 'offsetgroup': 'chat-bison-32k (PaLM 2 32K)',
+ 'name': 'llama-2-70b-chat',
+ 'offsetgroup': 'llama-2-70b-chat',
'orientation': 'v',
'showlegend': True,
'textposition': 'auto',
'type': 'bar',
- 'x': array(['chat-bison-32k (PaLM 2 32K)'], dtype=object),
+ 'x': array(['llama-2-70b-chat'], dtype=object),
'xaxis': 'x',
- 'y': array([5.46443628]),
+ 'y': array([4.80907998]),
'yaxis': 'y'},
{'alignmentgroup': 'True',
'hovertemplate': 'Model=%{x}
Execution time (s)=%{y}',
@@ -138,26 +138,12 @@ plot_object,header,plot_json,df,arguments,description
'type': 'bar',
'x': array(['gpt-3.5-turbo'], dtype=object),
'xaxis': 'x',
- 'y': array([4.59921461]),
- 'yaxis': 'y'},
- {'alignmentgroup': 'True',
- 'hovertemplate': 'Model=%{x}
Execution time (s)=%{y}',
- 'legendgroup': 'gemini-pro',
- 'marker': {'color': '#636efa', 'pattern': {'shape': ''}},
- 'name': 'gemini-pro',
- 'offsetgroup': 'gemini-pro',
- 'orientation': 'v',
- 'showlegend': True,
- 'textposition': 'auto',
- 'type': 'bar',
- 'x': array(['gemini-pro'], dtype=object),
- 'xaxis': 'x',
- 'y': array([4.11187035]),
+ 'y': array([4.63821494]),
'yaxis': 'y'},
{'alignmentgroup': 'True',
'hovertemplate': 'Model=%{x}
Execution time (s)=%{y}',
'legendgroup': 'Mistral (7B) Instruct v0.2 (Together AI)',
- 'marker': {'color': '#EF553B', 'pattern': {'shape': ''}},
+ 'marker': {'color': '#636efa', 'pattern': {'shape': ''}},
'name': 'Mistral (7B) Instruct v0.2 (Together AI)',
'offsetgroup': 'Mistral (7B) Instruct v0.2 (Together AI)',
'orientation': 'v',
@@ -166,26 +152,26 @@ plot_object,header,plot_json,df,arguments,description
'type': 'bar',
'x': array(['Mistral (7B) Instruct v0.2 (Together AI)'], dtype=object),
'xaxis': 'x',
- 'y': array([3.9566073]),
+ 'y': array([3.66314562]),
'yaxis': 'y'},
{'alignmentgroup': 'True',
'hovertemplate': 'Model=%{x}
Execution time (s)=%{y}',
- 'legendgroup': 'Vicuna v1.5 (7B)',
- 'marker': {'color': '#00cc96', 'pattern': {'shape': ''}},
- 'name': 'Vicuna v1.5 (7B)',
- 'offsetgroup': 'Vicuna v1.5 (7B)',
+ 'legendgroup': 'gemini-pro',
+ 'marker': {'color': '#EF553B', 'pattern': {'shape': ''}},
+ 'name': 'gemini-pro',
+ 'offsetgroup': 'gemini-pro',
'orientation': 'v',
'showlegend': True,
'textposition': 'auto',
'type': 'bar',
- 'x': array(['Vicuna v1.5 (7B)'], dtype=object),
+ 'x': array(['gemini-pro'], dtype=object),
'xaxis': 'x',
- 'y': array([3.85673946]),
+ 'y': array([3.64548177]),
'yaxis': 'y'},
{'alignmentgroup': 'True',
'hovertemplate': 'Model=%{x}
Execution time (s)=%{y}',
'legendgroup': 'zephyr-7b-beta',
- 'marker': {'color': '#ab63fa', 'pattern': {'shape': ''}},
+ 'marker': {'color': '#00cc96', 'pattern': {'shape': ''}},
'name': 'zephyr-7b-beta',
'offsetgroup': 'zephyr-7b-beta',
'orientation': 'v',
@@ -199,7 +185,7 @@ plot_object,header,plot_json,df,arguments,description
{'alignmentgroup': 'True',
'hovertemplate': 'Model=%{x}
Execution time (s)=%{y}',
'legendgroup': 'Mistral-7B-Instruct-v0.2',
- 'marker': {'color': '#FFA15A', 'pattern': {'shape': ''}},
+ 'marker': {'color': '#ab63fa', 'pattern': {'shape': ''}},
'name': 'Mistral-7B-Instruct-v0.2',
'offsetgroup': 'Mistral-7B-Instruct-v0.2',
'orientation': 'v',
@@ -212,22 +198,22 @@ plot_object,header,plot_json,df,arguments,description
'yaxis': 'y'},
{'alignmentgroup': 'True',
'hovertemplate': 'Model=%{x}
Execution time (s)=%{y}',
- 'legendgroup': 'LLaMA-2 Chat (7B)',
- 'marker': {'color': '#19d3f3', 'pattern': {'shape': ''}},
- 'name': 'LLaMA-2 Chat (7B)',
- 'offsetgroup': 'LLaMA-2 Chat (7B)',
+ 'legendgroup': 'Vicuna v1.5 (7B)',
+ 'marker': {'color': '#FFA15A', 'pattern': {'shape': ''}},
+ 'name': 'Vicuna v1.5 (7B)',
+ 'offsetgroup': 'Vicuna v1.5 (7B)',
'orientation': 'v',
'showlegend': True,
'textposition': 'auto',
'type': 'bar',
- 'x': array(['LLaMA-2 Chat (7B)'], dtype=object),
+ 'x': array(['Vicuna v1.5 (7B)'], dtype=object),
'xaxis': 'x',
- 'y': array([3.35083223]),
+ 'y': array([3.35936635]),
'yaxis': 'y'},
{'alignmentgroup': 'True',
'hovertemplate': 'Model=%{x}
Execution time (s)=%{y}',
'legendgroup': 'chat-bison (PaLM 2)',
- 'marker': {'color': '#FF6692', 'pattern': {'shape': ''}},
+ 'marker': {'color': '#19d3f3', 'pattern': {'shape': ''}},
'name': 'chat-bison (PaLM 2)',
'offsetgroup': 'chat-bison (PaLM 2)',
'orientation': 'v',
@@ -238,6 +224,20 @@ plot_object,header,plot_json,df,arguments,description
'xaxis': 'x',
'y': array([3.30885241]),
'yaxis': 'y'},
+ {'alignmentgroup': 'True',
+ 'hovertemplate': 'Model=%{x}
Execution time (s)=%{y}',
+ 'legendgroup': 'LLaMA-2 Chat (7B)',
+ 'marker': {'color': '#FF6692', 'pattern': {'shape': ''}},
+ 'name': 'LLaMA-2 Chat (7B)',
+ 'offsetgroup': 'LLaMA-2 Chat (7B)',
+ 'orientation': 'v',
+ 'showlegend': True,
+ 'textposition': 'auto',
+ 'type': 'bar',
+ 'x': array(['LLaMA-2 Chat (7B)'], dtype=object),
+ 'xaxis': 'x',
+ 'y': array([2.94143569]),
+ 'yaxis': 'y'},
{'alignmentgroup': 'True',
'hovertemplate': 'Model=%{x}
Execution time (s)=%{y}',
'legendgroup': 'OpenHermes-2.5-Mistral (7B)',
@@ -250,7 +250,7 @@ plot_object,header,plot_json,df,arguments,description
'type': 'bar',
'x': array(['OpenHermes-2.5-Mistral (7B)'], dtype=object),
'xaxis': 'x',
- 'y': array([2.88827331]),
+ 'y': array([2.81202571]),
'yaxis': 'y'},
{'alignmentgroup': 'True',
'hovertemplate': 'Model=%{x}
Execution time (s)=%{y}',
@@ -264,7 +264,7 @@ plot_object,header,plot_json,df,arguments,description
'type': 'bar',
'x': array(['Qwen 1.5 Chat (7B)'], dtype=object),
'xaxis': 'x',
- 'y': array([2.20103282]),
+ 'y': array([2.2973882]),
'yaxis': 'y'},
{'alignmentgroup': 'True',
'hovertemplate': 'Model=%{x}
Execution time (s)=%{y}',
@@ -278,7 +278,7 @@ plot_object,header,plot_json,df,arguments,description
'type': 'bar',
'x': array(['RedPajama-INCITE Chat (7B)'], dtype=object),
'xaxis': 'x',
- 'y': array([1.98214534]),
+ 'y': array([1.7621016]),
'yaxis': 'y'},
{'alignmentgroup': 'True',
'hovertemplate': 'Model=%{x}
Execution time (s)=%{y}',
@@ -292,7 +292,7 @@ plot_object,header,plot_json,df,arguments,description
'type': 'bar',
'x': array(['TinyLlama/TinyLlama-1.1B-Chat-v1.0'], dtype=object),
'xaxis': 'x',
- 'y': array([1.59549656]),
+ 'y': array([1.65637505]),
'yaxis': 'y'},
{'alignmentgroup': 'True',
'hovertemplate': 'Model=%{x}
Execution time (s)=%{y}',
@@ -315,45 +315,45 @@ plot_object,header,plot_json,df,arguments,description
'xaxis': {'anchor': 'y',
'categoryarray': [Snorkel Mistral PairRM DPO (7B), gpt-4,
gpt-4-turbo, 01-ai Yi Chat (34B),
- Chronos Hermes (13B),
- Mixtral-8x7B-Instruct-v0.1,
- llama-2-70b-chat, WizardLM v1.2 (13B),
- chat-bison-32k (PaLM 2 32K),
- gpt-3.5-turbo, gemini-pro, Mistral (7B)
- Instruct v0.2 (Together AI), Vicuna v1.5
- (7B), zephyr-7b-beta,
- Mistral-7B-Instruct-v0.2, LLaMA-2 Chat
- (7B), chat-bison (PaLM 2),
- OpenHermes-2.5-Mistral (7B), Qwen 1.5
- Chat (7B), RedPajama-INCITE Chat (7B),
+ Chronos Hermes (13B), WizardLM v1.2
+ (13B), Mixtral-8x7B-Instruct-v0.1, chat-
+ bison-32k (PaLM 2 32K),
+ llama-2-70b-chat, gpt-3.5-turbo, Mistral
+ (7B) Instruct v0.2 (Together AI),
+ gemini-pro, zephyr-7b-beta,
+ Mistral-7B-Instruct-v0.2, Vicuna v1.5
+ (7B), chat-bison (PaLM 2), LLaMA-2 Chat
+ (7B), OpenHermes-2.5-Mistral (7B), Qwen
+ 1.5 Chat (7B), RedPajama-INCITE Chat
+ (7B),
TinyLlama/TinyLlama-1.1B-Chat-v1.0,
Falcon Instruct (7B)],
'categoryorder': 'array',
'domain': [0.0, 1.0],
'title': {'text': 'Model'}},
'yaxis': {'anchor': 'x', 'domain': [0.0, 1.0], 'title': {'text': 'Execution time (s)'}}}
-})",Plot of execution time given the same input for summarization task,"{""data"":[{""alignmentgroup"":""True"",""hovertemplate"":""Model=%{x}\u003cbr\u003eExecution time (s)=%{y}\u003cextra\u003e\u003c\u002fextra\u003e"",""legendgroup"":""Snorkel Mistral PairRM DPO (7B)"",""marker"":{""color"":""#636efa"",""pattern"":{""shape"":""""}},""name"":""Snorkel Mistral PairRM DPO (7B)"",""offsetgroup"":""Snorkel Mistral PairRM DPO (7B)"",""orientation"":""v"",""showlegend"":true,""textposition"":""auto"",""x"":[""Snorkel Mistral PairRM DPO (7B)""],""xaxis"":""x"",""y"":[24.990053808560074],""yaxis"":""y"",""type"":""bar""},{""alignmentgroup"":""True"",""hovertemplate"":""Model=%{x}\u003cbr\u003eExecution time 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Mistral PairRM DPO (7B)"",""gpt-4"",""gpt-4-turbo"",""01-ai Yi Chat (34B)"",""Chronos Hermes (13B)"",""WizardLM v1.2 (13B)"",""Mixtral-8x7B-Instruct-v0.1"",""chat-bison-32k (PaLM 2 32K)"",""llama-2-70b-chat"",""gpt-3.5-turbo"",""Mistral (7B) Instruct v0.2 (Together AI)"",""gemini-pro"",""zephyr-7b-beta"",""Mistral-7B-Instruct-v0.2"",""Vicuna v1.5 (7B)"",""chat-bison (PaLM 2)"",""LLaMA-2 Chat (7B)"",""OpenHermes-2.5-Mistral (7B)"",""Qwen 1.5 Chat (7B)"",""RedPajama-INCITE Chat (7B)"",""TinyLlama\u002fTinyLlama-1.1B-Chat-v1.0"",""Falcon Instruct (7B)""]},""yaxis"":{""anchor"":""x"",""domain"":[0.0,1.0],""title"":{""text"":""Execution time (s)""}},""legend"":{""title"":{""text"":""Model""},""tracegroupgap"":0},""title"":{""text"":""Plot of execution time given the same input for summarization task""},""barmode"":""relative""}}",",model,execution_time
+10,Snorkel Mistral PairRM DPO (7B),19.986759704265395
+18,gpt-4,15.98077287161498
+19,gpt-4-turbo,14.244632319505602
+0,01-ai Yi Chat (34B),10.991513552391007
+1,Chronos Hermes (13B),8.589727006841095
+13,WizardLM v1.2 (13B),5.868885332229091
+6,Mixtral-8x7B-Instruct-v0.1,5.5218328136697465
15,chat-bison-32k (PaLM 2 32K),5.464436282749687
-17,gpt-3.5-turbo,4.599214611078473
-16,gemini-pro,4.111870346541831
-4,Mistral (7B) Instruct v0.2 (Together AI),3.95660729618315
-12,Vicuna v1.5 (7B),3.8567394597322227
+20,llama-2-70b-chat,4.8090799786202965
+17,gpt-3.5-turbo,4.638214940246723
+4,Mistral (7B) Instruct v0.2 (Together AI),3.6631456230179738
+16,gemini-pro,3.645481765456671
21,zephyr-7b-beta,3.5767457232334547
5,Mistral-7B-Instruct-v0.2,3.386921099240709
-3,LLaMA-2 Chat (7B),3.3508322324998163
+12,Vicuna v1.5 (7B),3.35936635421319
14,chat-bison (PaLM 2),3.3088524077087644
-7,OpenHermes-2.5-Mistral (7B),2.888273307162782
-8,Qwen 1.5 Chat (7B),2.201032818057145
-9,RedPajama-INCITE Chat (7B),1.9821453389905843
-11,TinyLlama/TinyLlama-1.1B-Chat-v1.0,1.5954965559732053
+3,LLaMA-2 Chat (7B),2.941435688511993
+7,OpenHermes-2.5-Mistral (7B),2.812025713575059
+8,Qwen 1.5 Chat (7B),2.2973882044010723
+9,RedPajama-INCITE Chat (7B),1.7621016023810012
+11,TinyLlama/TinyLlama-1.1B-Chat-v1.0,1.6563750527772214
2,Falcon Instruct (7B),1.3390633107409045
","{""x"": ""model"", ""y"": ""execution_time"", ""color"": ""model"", ""labels"": {""model"": ""Model"", ""execution_time"": ""Execution time (s)""}, ""title"": ""Plot of execution time given the same input for summarization task""}","This plot shows averaged response time for one query for each model.
@@ -373,7 +373,7 @@ It is also important to note that we used litellm python library for all of the
'type': 'bar',
'x': array(['Snorkel Mistral PairRM DPO (7B)'], dtype=object),
'xaxis': 'x',
- 'y': array([216.81729501]),
+ 'y': array([231.74486334]),
'yaxis': 'y'},
{'alignmentgroup': 'True',
'hovertemplate': 'Model=%{x}
Output words count=%{y}',
@@ -387,7 +387,7 @@ It is also important to note that we used litellm python library for all of the
'type': 'bar',
'x': array(['llama-2-70b-chat'], dtype=object),
'xaxis': 'x',
- 'y': array([95.35066814]),
+ 'y': array([84.28377876]),
'yaxis': 'y'},
{'alignmentgroup': 'True',
'hovertemplate': 'Model=%{x}
Output words count=%{y}',
@@ -401,7 +401,7 @@ It is also important to note that we used litellm python library for all of the
'type': 'bar',
'x': array(['Chronos Hermes (13B)'], dtype=object),
'xaxis': 'x',
- 'y': array([71.56112097]),
+ 'y': array([74.84074732]),
'yaxis': 'y'},
{'alignmentgroup': 'True',
'hovertemplate': 'Model=%{x}
Output words count=%{y}',
@@ -415,7 +415,7 @@ It is also important to note that we used litellm python library for all of the
'type': 'bar',
'x': array(['Mistral (7B) Instruct v0.2 (Together AI)'], dtype=object),
'xaxis': 'x',
- 'y': array([68.1734857]),
+ 'y': array([73.7573238]),
'yaxis': 'y'},
{'alignmentgroup': 'True',
'hovertemplate': 'Model=%{x}
Output words count=%{y}',
@@ -429,49 +429,49 @@ It is also important to note that we used litellm python library for all of the
'type': 'bar',
'x': array(['LLaMA-2 Chat (7B)'], dtype=object),
'xaxis': 'x',
- 'y': array([62.66994559]),
+ 'y': array([66.83829706]),
'yaxis': 'y'},
{'alignmentgroup': 'True',
'hovertemplate': 'Model=%{x}
Output words count=%{y}',
- 'legendgroup': 'Mixtral-8x7B-Instruct-v0.1',
+ 'legendgroup': '01-ai Yi Chat (34B)',
'marker': {'color': '#19d3f3', 'pattern': {'shape': ''}},
- 'name': 'Mixtral-8x7B-Instruct-v0.1',
- 'offsetgroup': 'Mixtral-8x7B-Instruct-v0.1',
+ 'name': '01-ai Yi Chat (34B)',
+ 'offsetgroup': '01-ai Yi Chat (34B)',
'orientation': 'v',
'showlegend': True,
'textposition': 'auto',
'type': 'bar',
- 'x': array(['Mixtral-8x7B-Instruct-v0.1'], dtype=object),
+ 'x': array(['01-ai Yi Chat (34B)'], dtype=object),
'xaxis': 'x',
- 'y': array([54.16817689]),
+ 'y': array([64.86385624]),
'yaxis': 'y'},
{'alignmentgroup': 'True',
'hovertemplate': 'Model=%{x}
Output words count=%{y}',
- 'legendgroup': '01-ai Yi Chat (34B)',
+ 'legendgroup': 'Mixtral-8x7B-Instruct-v0.1',
'marker': {'color': '#FF6692', 'pattern': {'shape': ''}},
- 'name': '01-ai Yi Chat (34B)',
- 'offsetgroup': '01-ai Yi Chat (34B)',
+ 'name': 'Mixtral-8x7B-Instruct-v0.1',
+ 'offsetgroup': 'Mixtral-8x7B-Instruct-v0.1',
'orientation': 'v',
'showlegend': True,
'textposition': 'auto',
'type': 'bar',
- 'x': array(['01-ai Yi Chat (34B)'], dtype=object),
+ 'x': array(['Mixtral-8x7B-Instruct-v0.1'], dtype=object),
'xaxis': 'x',
- 'y': array([44.25828436]),
+ 'y': array([62.02878459]),
'yaxis': 'y'},
{'alignmentgroup': 'True',
'hovertemplate': 'Model=%{x}
Output words count=%{y}',
- 'legendgroup': 'chat-bison (PaLM 2)',
+ 'legendgroup': 'WizardLM v1.2 (13B)',
'marker': {'color': '#B6E880', 'pattern': {'shape': ''}},
- 'name': 'chat-bison (PaLM 2)',
- 'offsetgroup': 'chat-bison (PaLM 2)',
+ 'name': 'WizardLM v1.2 (13B)',
+ 'offsetgroup': 'WizardLM v1.2 (13B)',
'orientation': 'v',
'showlegend': True,
'textposition': 'auto',
'type': 'bar',
- 'x': array(['chat-bison (PaLM 2)'], dtype=object),
+ 'x': array(['WizardLM v1.2 (13B)'], dtype=object),
'xaxis': 'x',
- 'y': array([40.9371875]),
+ 'y': array([54.62341982]),
'yaxis': 'y'},
{'alignmentgroup': 'True',
'hovertemplate': 'Model=%{x}
Output words count=%{y}',
@@ -485,77 +485,77 @@ It is also important to note that we used litellm python library for all of the
'type': 'bar',
'x': array(['gemini-pro'], dtype=object),
'xaxis': 'x',
- 'y': array([39.69346182]),
+ 'y': array([49.23743043]),
'yaxis': 'y'},
{'alignmentgroup': 'True',
'hovertemplate': 'Model=%{x}
Output words count=%{y}',
- 'legendgroup': 'gpt-3.5-turbo',
+ 'legendgroup': 'Qwen 1.5 Chat (7B)',
'marker': {'color': '#FECB52', 'pattern': {'shape': ''}},
- 'name': 'gpt-3.5-turbo',
- 'offsetgroup': 'gpt-3.5-turbo',
+ 'name': 'Qwen 1.5 Chat (7B)',
+ 'offsetgroup': 'Qwen 1.5 Chat (7B)',
'orientation': 'v',
'showlegend': True,
'textposition': 'auto',
'type': 'bar',
- 'x': array(['gpt-3.5-turbo'], dtype=object),
+ 'x': array(['Qwen 1.5 Chat (7B)'], dtype=object),
'xaxis': 'x',
- 'y': array([39.25851284]),
+ 'y': array([45.20889867]),
'yaxis': 'y'},
{'alignmentgroup': 'True',
'hovertemplate': 'Model=%{x}
Output words count=%{y}',
- 'legendgroup': 'chat-bison-32k (PaLM 2 32K)',
+ 'legendgroup': 'Vicuna v1.5 (7B)',
'marker': {'color': '#636efa', 'pattern': {'shape': ''}},
- 'name': 'chat-bison-32k (PaLM 2 32K)',
- 'offsetgroup': 'chat-bison-32k (PaLM 2 32K)',
+ 'name': 'Vicuna v1.5 (7B)',
+ 'offsetgroup': 'Vicuna v1.5 (7B)',
'orientation': 'v',
'showlegend': True,
'textposition': 'auto',
'type': 'bar',
- 'x': array(['chat-bison-32k (PaLM 2 32K)'], dtype=object),
+ 'x': array(['Vicuna v1.5 (7B)'], dtype=object),
'xaxis': 'x',
- 'y': array([38.72477679]),
+ 'y': array([44.23524076]),
'yaxis': 'y'},
{'alignmentgroup': 'True',
'hovertemplate': 'Model=%{x}
Output words count=%{y}',
- 'legendgroup': 'gpt-4-turbo',
+ 'legendgroup': 'gpt-3.5-turbo',
'marker': {'color': '#EF553B', 'pattern': {'shape': ''}},
- 'name': 'gpt-4-turbo',
- 'offsetgroup': 'gpt-4-turbo',
+ 'name': 'gpt-3.5-turbo',
+ 'offsetgroup': 'gpt-3.5-turbo',
'orientation': 'v',
'showlegend': True,
'textposition': 'auto',
'type': 'bar',
- 'x': array(['gpt-4-turbo'], dtype=object),
+ 'x': array(['gpt-3.5-turbo'], dtype=object),
'xaxis': 'x',
- 'y': array([38.36012473]),
+ 'y': array([43.083453]),
'yaxis': 'y'},
{'alignmentgroup': 'True',
'hovertemplate': 'Model=%{x}
Output words count=%{y}',
- 'legendgroup': 'Vicuna v1.5 (7B)',
+ 'legendgroup': 'OpenHermes-2.5-Mistral (7B)',
'marker': {'color': '#00cc96', 'pattern': {'shape': ''}},
- 'name': 'Vicuna v1.5 (7B)',
- 'offsetgroup': 'Vicuna v1.5 (7B)',
+ 'name': 'OpenHermes-2.5-Mistral (7B)',
+ 'offsetgroup': 'OpenHermes-2.5-Mistral (7B)',
'orientation': 'v',
'showlegend': True,
'textposition': 'auto',
'type': 'bar',
- 'x': array(['Vicuna v1.5 (7B)'], dtype=object),
+ 'x': array(['OpenHermes-2.5-Mistral (7B)'], dtype=object),
'xaxis': 'x',
- 'y': array([36.52786114]),
+ 'y': array([42.84559302]),
'yaxis': 'y'},
{'alignmentgroup': 'True',
'hovertemplate': 'Model=%{x}
Output words count=%{y}',
- 'legendgroup': 'WizardLM v1.2 (13B)',
+ 'legendgroup': 'chat-bison (PaLM 2)',
'marker': {'color': '#ab63fa', 'pattern': {'shape': ''}},
- 'name': 'WizardLM v1.2 (13B)',
- 'offsetgroup': 'WizardLM v1.2 (13B)',
+ 'name': 'chat-bison (PaLM 2)',
+ 'offsetgroup': 'chat-bison (PaLM 2)',
'orientation': 'v',
'showlegend': True,
'textposition': 'auto',
'type': 'bar',
- 'x': array(['WizardLM v1.2 (13B)'], dtype=object),
+ 'x': array(['chat-bison (PaLM 2)'], dtype=object),
'xaxis': 'x',
- 'y': array([33.48512973]),
+ 'y': array([40.9371875]),
'yaxis': 'y'},
{'alignmentgroup': 'True',
'hovertemplate': 'Model=%{x}
Output words count=%{y}',
@@ -569,63 +569,63 @@ It is also important to note that we used litellm python library for all of the
'type': 'bar',
'x': array(['gpt-4'], dtype=object),
'xaxis': 'x',
- 'y': array([32.90737496]),
+ 'y': array([40.80491664]),
'yaxis': 'y'},
{'alignmentgroup': 'True',
'hovertemplate': 'Model=%{x}
Output words count=%{y}',
- 'legendgroup': 'OpenHermes-2.5-Mistral (7B)',
+ 'legendgroup': 'gpt-4-turbo',
'marker': {'color': '#19d3f3', 'pattern': {'shape': ''}},
- 'name': 'OpenHermes-2.5-Mistral (7B)',
- 'offsetgroup': 'OpenHermes-2.5-Mistral (7B)',
+ 'name': 'gpt-4-turbo',
+ 'offsetgroup': 'gpt-4-turbo',
'orientation': 'v',
'showlegend': True,
'textposition': 'auto',
'type': 'bar',
- 'x': array(['OpenHermes-2.5-Mistral (7B)'], dtype=object),
+ 'x': array(['gpt-4-turbo'], dtype=object),
'xaxis': 'x',
- 'y': array([32.78088953]),
+ 'y': array([40.57897205]),
'yaxis': 'y'},
{'alignmentgroup': 'True',
'hovertemplate': 'Model=%{x}
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- 'legendgroup': 'TinyLlama/TinyLlama-1.1B-Chat-v1.0',
+ 'legendgroup': 'chat-bison-32k (PaLM 2 32K)',
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- 'name': 'TinyLlama/TinyLlama-1.1B-Chat-v1.0',
- 'offsetgroup': 'TinyLlama/TinyLlama-1.1B-Chat-v1.0',
+ 'name': 'chat-bison-32k (PaLM 2 32K)',
+ 'offsetgroup': 'chat-bison-32k (PaLM 2 32K)',
'orientation': 'v',
'showlegend': True,
'textposition': 'auto',
'type': 'bar',
- 'x': array(['TinyLlama/TinyLlama-1.1B-Chat-v1.0'], dtype=object),
+ 'x': array(['chat-bison-32k (PaLM 2 32K)'], dtype=object),
'xaxis': 'x',
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+ 'y': array([38.72477679]),
'yaxis': 'y'},
{'alignmentgroup': 'True',
'hovertemplate': 'Model=%{x}
Output words count=%{y}',
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+ 'legendgroup': 'TinyLlama/TinyLlama-1.1B-Chat-v1.0',
'marker': {'color': '#B6E880', 'pattern': {'shape': ''}},
- 'name': 'zephyr-7b-beta',
- 'offsetgroup': 'zephyr-7b-beta',
+ 'name': 'TinyLlama/TinyLlama-1.1B-Chat-v1.0',
+ 'offsetgroup': 'TinyLlama/TinyLlama-1.1B-Chat-v1.0',
'orientation': 'v',
'showlegend': True,
'textposition': 'auto',
'type': 'bar',
- 'x': array(['zephyr-7b-beta'], dtype=object),
+ 'x': array(['TinyLlama/TinyLlama-1.1B-Chat-v1.0'], dtype=object),
'xaxis': 'x',
- 'y': array([32.25400169]),
+ 'y': array([36.08376437]),
'yaxis': 'y'},
{'alignmentgroup': 'True',
'hovertemplate': 'Model=%{x}
Output words count=%{y}',
- 'legendgroup': 'Qwen 1.5 Chat (7B)',
+ 'legendgroup': 'zephyr-7b-beta',
'marker': {'color': '#FF97FF', 'pattern': {'shape': ''}},
- 'name': 'Qwen 1.5 Chat (7B)',
- 'offsetgroup': 'Qwen 1.5 Chat (7B)',
+ 'name': 'zephyr-7b-beta',
+ 'offsetgroup': 'zephyr-7b-beta',
'orientation': 'v',
'showlegend': True,
'textposition': 'auto',
'type': 'bar',
- 'x': array(['Qwen 1.5 Chat (7B)'], dtype=object),
+ 'x': array(['zephyr-7b-beta'], dtype=object),
'xaxis': 'x',
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+ 'y': array([32.25400169]),
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@@ -667,7 +667,7 @@ It is also important to note that we used litellm python library for all of the
'type': 'bar',
'x': array(['RedPajama-INCITE Chat (7B)'], dtype=object),
'xaxis': 'x',
- 'y': array([5.2095088]),
+ 'y': array([5.3563392]),
'yaxis': 'y'}],
'layout': {'barmode': 'relative',
'legend': {'title': {'text': 'Model'}, 'tracegroupgap': 0},
@@ -677,15 +677,16 @@ It is also important to note that we used litellm python library for all of the
'categoryarray': [Snorkel Mistral PairRM DPO (7B),
llama-2-70b-chat, Chronos Hermes (13B),
Mistral (7B) Instruct v0.2 (Together
- AI), LLaMA-2 Chat (7B),
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- Chat (34B), chat-bison (PaLM 2), gemini-
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- 2 32K), gpt-4-turbo, Vicuna v1.5 (7B),
- WizardLM v1.2 (13B), gpt-4,
- OpenHermes-2.5-Mistral (7B),
+ AI), LLaMA-2 Chat (7B), 01-ai Yi Chat
+ (34B), Mixtral-8x7B-Instruct-v0.1,
+ WizardLM v1.2 (13B), gemini-pro, Qwen
+ 1.5 Chat (7B), Vicuna v1.5 (7B),
+ gpt-3.5-turbo, OpenHermes-2.5-Mistral
+ (7B), chat-bison (PaLM 2), gpt-4,
+ gpt-4-turbo, chat-bison-32k (PaLM 2
+ 32K),
TinyLlama/TinyLlama-1.1B-Chat-v1.0,
- zephyr-7b-beta, Qwen 1.5 Chat (7B),
+ zephyr-7b-beta,
Mistral-7B-Instruct-v0.2, Falcon
Instruct (7B), RedPajama-INCITE Chat
(7B)],
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'domain': [0.0, 1.0],
'title': {'text': 'Model'}},
'yaxis': {'anchor': 'x', 'domain': [0.0, 1.0], 'title': {'text': 'Output words count'}}}
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'type': 'bar',
- 'x': array(['llama-2-70b-chat'], dtype=object),
+ 'x': array(['TinyLlama/TinyLlama-1.1B-Chat-v1.0'], dtype=object),
'xaxis': 'x',
- 'y': array([3.20032418]),
+ 'y': array([3.2040858]),
'yaxis': 'y'},
{'alignmentgroup': 'True',
'hovertemplate': 'Model=%{x}
Chunks mean size (characters)=%{y}',
@@ -800,189 +801,189 @@ It is also important to note that we used litellm python library for all of the
'type': 'bar',
'x': array(['RedPajama-INCITE Chat (7B)'], dtype=object),
'xaxis': 'x',
- 'y': array([3.15892476]),
+ 'y': array([3.17327871]),
'yaxis': 'y'},
{'alignmentgroup': 'True',
'hovertemplate': 'Model=%{x}
Chunks mean size (characters)=%{y}',
- 'legendgroup': 'TinyLlama/TinyLlama-1.1B-Chat-v1.0',
+ 'legendgroup': 'llama-2-70b-chat',
'marker': {'color': '#FF6692', 'pattern': {'shape': ''}},
- 'name': 'TinyLlama/TinyLlama-1.1B-Chat-v1.0',
- 'offsetgroup': 'TinyLlama/TinyLlama-1.1B-Chat-v1.0',
+ 'name': 'llama-2-70b-chat',
+ 'offsetgroup': 'llama-2-70b-chat',
'orientation': 'v',
'showlegend': True,
'textposition': 'auto',
'type': 'bar',
- 'x': array(['TinyLlama/TinyLlama-1.1B-Chat-v1.0'], dtype=object),
+ 'x': array(['llama-2-70b-chat'], dtype=object),
'xaxis': 'x',
- 'y': array([3.14663951]),
+ 'y': array([2.99479736]),
'yaxis': 'y'},
{'alignmentgroup': 'True',
'hovertemplate': 'Model=%{x}
Chunks mean size (characters)=%{y}',
- 'legendgroup': 'gpt-3.5-turbo',
+ 'legendgroup': 'Mixtral-8x7B-Instruct-v0.1',
'marker': {'color': '#B6E880', 'pattern': {'shape': ''}},
- 'name': 'gpt-3.5-turbo',
- 'offsetgroup': 'gpt-3.5-turbo',
+ 'name': 'Mixtral-8x7B-Instruct-v0.1',
+ 'offsetgroup': 'Mixtral-8x7B-Instruct-v0.1',
'orientation': 'v',
'showlegend': True,
'textposition': 'auto',
'type': 'bar',
- 'x': array(['gpt-3.5-turbo'], dtype=object),
+ 'x': array(['Mixtral-8x7B-Instruct-v0.1'], dtype=object),
'xaxis': 'x',
- 'y': array([3.04399416]),
+ 'y': array([2.81103164]),
'yaxis': 'y'},
{'alignmentgroup': 'True',
'hovertemplate': 'Model=%{x}
Chunks mean size (characters)=%{y}',
- 'legendgroup': 'gpt-4',
+ 'legendgroup': 'LLaMA-2 Chat (7B)',
'marker': {'color': '#FF97FF', 'pattern': {'shape': ''}},
- 'name': 'gpt-4',
- 'offsetgroup': 'gpt-4',
+ 'name': 'LLaMA-2 Chat (7B)',
+ 'offsetgroup': 'LLaMA-2 Chat (7B)',
'orientation': 'v',
'showlegend': True,
'textposition': 'auto',
'type': 'bar',
- 'x': array(['gpt-4'], dtype=object),
+ 'x': array(['LLaMA-2 Chat (7B)'], dtype=object),
'xaxis': 'x',
- 'y': array([3.01875517]),
+ 'y': array([2.7218178]),
'yaxis': 'y'},
{'alignmentgroup': 'True',
'hovertemplate': 'Model=%{x}
Chunks mean size (characters)=%{y}',
- 'legendgroup': 'gpt-4-turbo',
+ 'legendgroup': 'zephyr-7b-beta',
'marker': {'color': '#FECB52', 'pattern': {'shape': ''}},
- 'name': 'gpt-4-turbo',
- 'offsetgroup': 'gpt-4-turbo',
+ 'name': 'zephyr-7b-beta',
+ 'offsetgroup': 'zephyr-7b-beta',
'orientation': 'v',
'showlegend': True,
'textposition': 'auto',
'type': 'bar',
- 'x': array(['gpt-4-turbo'], dtype=object),
+ 'x': array(['zephyr-7b-beta'], dtype=object),
'xaxis': 'x',
- 'y': array([3.0126559]),
+ 'y': array([2.70854999]),
'yaxis': 'y'},
{'alignmentgroup': 'True',
'hovertemplate': 'Model=%{x}
Chunks mean size (characters)=%{y}',
- 'legendgroup': 'Mixtral-8x7B-Instruct-v0.1',
+ 'legendgroup': 'Mistral-7B-Instruct-v0.2',
'marker': {'color': '#636efa', 'pattern': {'shape': ''}},
- 'name': 'Mixtral-8x7B-Instruct-v0.1',
- 'offsetgroup': 'Mixtral-8x7B-Instruct-v0.1',
+ 'name': 'Mistral-7B-Instruct-v0.2',
+ 'offsetgroup': 'Mistral-7B-Instruct-v0.2',
'orientation': 'v',
'showlegend': True,
'textposition': 'auto',
'type': 'bar',
- 'x': array(['Mixtral-8x7B-Instruct-v0.1'], dtype=object),
+ 'x': array(['Mistral-7B-Instruct-v0.2'], dtype=object),
'xaxis': 'x',
- 'y': array([2.95880337]),
+ 'y': array([2.64769775]),
'yaxis': 'y'},
{'alignmentgroup': 'True',
'hovertemplate': 'Model=%{x}
Chunks mean size (characters)=%{y}',
- 'legendgroup': 'LLaMA-2 Chat (7B)',
+ 'legendgroup': 'Chronos Hermes (13B)',
'marker': {'color': '#EF553B', 'pattern': {'shape': ''}},
- 'name': 'LLaMA-2 Chat (7B)',
- 'offsetgroup': 'LLaMA-2 Chat (7B)',
+ 'name': 'Chronos Hermes (13B)',
+ 'offsetgroup': 'Chronos Hermes (13B)',
'orientation': 'v',
'showlegend': True,
'textposition': 'auto',
'type': 'bar',
- 'x': array(['LLaMA-2 Chat (7B)'], dtype=object),
+ 'x': array(['Chronos Hermes (13B)'], dtype=object),
'xaxis': 'x',
- 'y': array([2.77311537]),
+ 'y': array([2.64238254]),
'yaxis': 'y'},
{'alignmentgroup': 'True',
'hovertemplate': 'Model=%{x}
Chunks mean size (characters)=%{y}',
- 'legendgroup': 'Qwen 1.5 Chat (7B)',
+ 'legendgroup': 'gpt-3.5-turbo',
'marker': {'color': '#00cc96', 'pattern': {'shape': ''}},
- 'name': 'Qwen 1.5 Chat (7B)',
- 'offsetgroup': 'Qwen 1.5 Chat (7B)',
+ 'name': 'gpt-3.5-turbo',
+ 'offsetgroup': 'gpt-3.5-turbo',
'orientation': 'v',
'showlegend': True,
'textposition': 'auto',
'type': 'bar',
- 'x': array(['Qwen 1.5 Chat (7B)'], dtype=object),
+ 'x': array(['gpt-3.5-turbo'], dtype=object),
'xaxis': 'x',
- 'y': array([2.75041217]),
+ 'y': array([2.62098816]),
'yaxis': 'y'},
{'alignmentgroup': 'True',
'hovertemplate': 'Model=%{x}
Chunks mean size (characters)=%{y}',
- 'legendgroup': 'Snorkel Mistral PairRM DPO (7B)',
+ 'legendgroup': 'gpt-4-turbo',
'marker': {'color': '#ab63fa', 'pattern': {'shape': ''}},
- 'name': 'Snorkel Mistral PairRM DPO (7B)',
- 'offsetgroup': 'Snorkel Mistral PairRM DPO (7B)',
+ 'name': 'gpt-4-turbo',
+ 'offsetgroup': 'gpt-4-turbo',
'orientation': 'v',
'showlegend': True,
'textposition': 'auto',
'type': 'bar',
- 'x': array(['Snorkel Mistral PairRM DPO (7B)'], dtype=object),
+ 'x': array(['gpt-4-turbo'], dtype=object),
'xaxis': 'x',
- 'y': array([2.71229258]),
+ 'y': array([2.59991471]),
'yaxis': 'y'},
{'alignmentgroup': 'True',
'hovertemplate': 'Model=%{x}
Chunks mean size (characters)=%{y}',
- 'legendgroup': 'zephyr-7b-beta',
+ 'legendgroup': 'gpt-4',
'marker': {'color': '#FFA15A', 'pattern': {'shape': ''}},
- 'name': 'zephyr-7b-beta',
- 'offsetgroup': 'zephyr-7b-beta',
+ 'name': 'gpt-4',
+ 'offsetgroup': 'gpt-4',
'orientation': 'v',
'showlegend': True,
'textposition': 'auto',
'type': 'bar',
- 'x': array(['zephyr-7b-beta'], dtype=object),
+ 'x': array(['gpt-4'], dtype=object),
'xaxis': 'x',
- 'y': array([2.70854999]),
+ 'y': array([2.59580904]),
'yaxis': 'y'},
{'alignmentgroup': 'True',
'hovertemplate': 'Model=%{x}
Chunks mean size (characters)=%{y}',
- 'legendgroup': 'Chronos Hermes (13B)',
+ 'legendgroup': 'Snorkel Mistral PairRM DPO (7B)',
'marker': {'color': '#19d3f3', 'pattern': {'shape': ''}},
- 'name': 'Chronos Hermes (13B)',
- 'offsetgroup': 'Chronos Hermes (13B)',
+ 'name': 'Snorkel Mistral PairRM DPO (7B)',
+ 'offsetgroup': 'Snorkel Mistral PairRM DPO (7B)',
'orientation': 'v',
'showlegend': True,
'textposition': 'auto',
'type': 'bar',
- 'x': array(['Chronos Hermes (13B)'], dtype=object),
+ 'x': array(['Snorkel Mistral PairRM DPO (7B)'], dtype=object),
'xaxis': 'x',
- 'y': array([2.69286913]),
+ 'y': array([2.57228422]),
'yaxis': 'y'},
{'alignmentgroup': 'True',
'hovertemplate': 'Model=%{x}
Chunks mean size (characters)=%{y}',
- 'legendgroup': 'Mistral-7B-Instruct-v0.2',
+ 'legendgroup': 'Mistral (7B) Instruct v0.2 (Together AI)',
'marker': {'color': '#FF6692', 'pattern': {'shape': ''}},
- 'name': 'Mistral-7B-Instruct-v0.2',
- 'offsetgroup': 'Mistral-7B-Instruct-v0.2',
+ 'name': 'Mistral (7B) Instruct v0.2 (Together AI)',
+ 'offsetgroup': 'Mistral (7B) Instruct v0.2 (Together AI)',
'orientation': 'v',
'showlegend': True,
'textposition': 'auto',
'type': 'bar',
- 'x': array(['Mistral-7B-Instruct-v0.2'], dtype=object),
+ 'x': array(['Mistral (7B) Instruct v0.2 (Together AI)'], dtype=object),
'xaxis': 'x',
- 'y': array([2.64769775]),
+ 'y': array([2.55559184]),
'yaxis': 'y'},
{'alignmentgroup': 'True',
'hovertemplate': 'Model=%{x}
Chunks mean size (characters)=%{y}',
- 'legendgroup': 'Mistral (7B) Instruct v0.2 (Together AI)',
+ 'legendgroup': 'Qwen 1.5 Chat (7B)',
'marker': {'color': '#B6E880', 'pattern': {'shape': ''}},
- 'name': 'Mistral (7B) Instruct v0.2 (Together AI)',
- 'offsetgroup': 'Mistral (7B) Instruct v0.2 (Together AI)',
+ 'name': 'Qwen 1.5 Chat (7B)',
+ 'offsetgroup': 'Qwen 1.5 Chat (7B)',
'orientation': 'v',
'showlegend': True,
'textposition': 'auto',
'type': 'bar',
- 'x': array(['Mistral (7B) Instruct v0.2 (Together AI)'], dtype=object),
+ 'x': array(['Qwen 1.5 Chat (7B)'], dtype=object),
'xaxis': 'x',
- 'y': array([2.63474314]),
+ 'y': array([2.49843277]),
'yaxis': 'y'},
{'alignmentgroup': 'True',
'hovertemplate': 'Model=%{x}
Chunks mean size (characters)=%{y}',
- 'legendgroup': '01-ai Yi Chat (34B)',
+ 'legendgroup': 'Vicuna v1.5 (7B)',
'marker': {'color': '#FF97FF', 'pattern': {'shape': ''}},
- 'name': '01-ai Yi Chat (34B)',
- 'offsetgroup': '01-ai Yi Chat (34B)',
+ 'name': 'Vicuna v1.5 (7B)',
+ 'offsetgroup': 'Vicuna v1.5 (7B)',
'orientation': 'v',
'showlegend': True,
'textposition': 'auto',
'type': 'bar',
- 'x': array(['01-ai Yi Chat (34B)'], dtype=object),
+ 'x': array(['Vicuna v1.5 (7B)'], dtype=object),
'xaxis': 'x',
- 'y': array([2.5995706]),
+ 'y': array([2.42986939]),
'yaxis': 'y'},
{'alignmentgroup': 'True',
'hovertemplate': 'Model=%{x}
Chunks mean size (characters)=%{y}',
@@ -996,21 +997,21 @@ It is also important to note that we used litellm python library for all of the
'type': 'bar',
'x': array(['OpenHermes-2.5-Mistral (7B)'], dtype=object),
'xaxis': 'x',
- 'y': array([2.4002878]),
+ 'y': array([2.3836649]),
'yaxis': 'y'},
{'alignmentgroup': 'True',
'hovertemplate': 'Model=%{x}
Chunks mean size (characters)=%{y}',
- 'legendgroup': 'Vicuna v1.5 (7B)',
+ 'legendgroup': '01-ai Yi Chat (34B)',
'marker': {'color': '#636efa', 'pattern': {'shape': ''}},
- 'name': 'Vicuna v1.5 (7B)',
- 'offsetgroup': 'Vicuna v1.5 (7B)',
+ 'name': '01-ai Yi Chat (34B)',
+ 'offsetgroup': '01-ai Yi Chat (34B)',
'orientation': 'v',
'showlegend': True,
'textposition': 'auto',
'type': 'bar',
- 'x': array(['Vicuna v1.5 (7B)'], dtype=object),
+ 'x': array(['01-ai Yi Chat (34B)'], dtype=object),
'xaxis': 'x',
- 'y': array([2.36590999]),
+ 'y': array([2.35026191]),
'yaxis': 'y'},
{'alignmentgroup': 'True',
'hovertemplate': 'Model=%{x}
Chunks mean size (characters)=%{y}',
@@ -1024,7 +1025,7 @@ It is also important to note that we used litellm python library for all of the
'type': 'bar',
'x': array(['WizardLM v1.2 (13B)'], dtype=object),
'xaxis': 'x',
- 'y': array([2.32204331]),
+ 'y': array([2.27685339]),
'yaxis': 'y'}],
'layout': {'barmode': 'relative',
'legend': {'title': {'text': 'Model'}, 'tracegroupgap': 0},
@@ -1033,43 +1034,44 @@ It is also important to note that we used litellm python library for all of the
'xaxis': {'anchor': 'y',
'categoryarray': [gemini-pro, chat-bison (PaLM 2), chat-
bison-32k (PaLM 2 32K), Falcon Instruct
- (7B), llama-2-70b-chat, RedPajama-INCITE
- Chat (7B),
+ (7B),
TinyLlama/TinyLlama-1.1B-Chat-v1.0,
- gpt-3.5-turbo, gpt-4, gpt-4-turbo,
+ RedPajama-INCITE Chat (7B),
+ llama-2-70b-chat,
Mixtral-8x7B-Instruct-v0.1, LLaMA-2 Chat
- (7B), Qwen 1.5 Chat (7B), Snorkel
- Mistral PairRM DPO (7B), zephyr-7b-beta,
- Chronos Hermes (13B),
- Mistral-7B-Instruct-v0.2, Mistral (7B)
- Instruct v0.2 (Together AI), 01-ai Yi
- Chat (34B), OpenHermes-2.5-Mistral (7B),
- Vicuna v1.5 (7B), WizardLM v1.2 (13B)],
+ (7B), zephyr-7b-beta,
+ Mistral-7B-Instruct-v0.2, Chronos Hermes
+ (13B), gpt-3.5-turbo, gpt-4-turbo,
+ gpt-4, Snorkel Mistral PairRM DPO (7B),
+ Mistral (7B) Instruct v0.2 (Together
+ AI), Qwen 1.5 Chat (7B), Vicuna v1.5
+ (7B), OpenHermes-2.5-Mistral (7B), 01-ai
+ Yi Chat (34B), WizardLM v1.2 (13B)],
'categoryorder': 'array',
'domain': [0.0, 1.0],
'title': {'text': 'Model'}},
'yaxis': {'anchor': 'x', 'domain': [0.0, 1.0], 'title': {'text': 'Chunks mean size (characters)'}}}
-})",Plot of chunks mean size given the same input for summarization task,"{""data"":[{""alignmentgroup"":""True"",""hovertemplate"":""Model=%{x}\u003cbr\u003eChunks mean size (characters)=%{y}\u003cextra\u003e\u003c\u002fextra\u003e"",""legendgroup"":""gemini-pro"",""marker"":{""color"":""#636efa"",""pattern"":{""shape"":""""}},""name"":""gemini-pro"",""offsetgroup"":""gemini-pro"",""orientation"":""v"",""showlegend"":true,""textposition"":""auto"",""x"":[""gemini-pro""],""xaxis"":""x"",""y"":[142.5030152620613],""yaxis"":""y"",""type"":""bar""},{""alignmentgroup"":""True"",""hovertemplate"":""Model=%{x}\u003cbr\u003eChunks mean size (characters)=%{y}\u003cextra\u003e\u003c\u002fextra\u003e"",""legendgroup"":""chat-bison (PaLM 2)"",""marker"":{""color"":""#EF553B"",""pattern"":{""shape"":""""}},""name"":""chat-bison (PaLM 2)"",""offsetgroup"":""chat-bison (PaLM 2)"",""orientation"":""v"",""showlegend"":true,""textposition"":""auto"",""x"":[""chat-bison (PaLM 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14,chat-bison (PaLM 2),57.12081730998353
15,chat-bison-32k (PaLM 2 32K),56.57639873825073
2,Falcon Instruct (7B),3.988623278882008
-20,llama-2-70b-chat,3.2003241836714382
-9,RedPajama-INCITE Chat (7B),3.158924757153785
-11,TinyLlama/TinyLlama-1.1B-Chat-v1.0,3.146639511119026
-17,gpt-3.5-turbo,3.0439941593593898
-18,gpt-4,3.018755174356351
-19,gpt-4-turbo,3.0126559039321035
-6,Mixtral-8x7B-Instruct-v0.1,2.9588033651203722
-3,LLaMA-2 Chat (7B),2.773115366110363
-8,Qwen 1.5 Chat (7B),2.7504121744515917
-10,Snorkel Mistral PairRM DPO (7B),2.7122925840807723
+11,TinyLlama/TinyLlama-1.1B-Chat-v1.0,3.2040858024436085
+9,RedPajama-INCITE Chat (7B),3.1732787071501423
+20,llama-2-70b-chat,2.994797357612382
+6,Mixtral-8x7B-Instruct-v0.1,2.811031636552529
+3,LLaMA-2 Chat (7B),2.721817799235493
21,zephyr-7b-beta,2.7085499882907946
-1,Chronos Hermes (13B),2.6928691347727396
5,Mistral-7B-Instruct-v0.2,2.6476977468756706
-4,Mistral (7B) Instruct v0.2 (Together AI),2.634743137389986
-0,01-ai Yi Chat (34B),2.599570602274316
-7,OpenHermes-2.5-Mistral (7B),2.4002877998617014
-12,Vicuna v1.5 (7B),2.365909992104287
-13,WizardLM v1.2 (13B),2.322043305064012
+1,Chronos Hermes (13B),2.6423825352546655
+17,gpt-3.5-turbo,2.6209881584269534
+19,gpt-4-turbo,2.599914709314438
+18,gpt-4,2.5958090443843065
+10,Snorkel Mistral PairRM DPO (7B),2.572284219027933
+4,Mistral (7B) Instruct v0.2 (Together AI),2.555591838590856
+8,Qwen 1.5 Chat (7B),2.498432766773607
+12,Vicuna v1.5 (7B),2.4298693880128615
+7,OpenHermes-2.5-Mistral (7B),2.3836649031289374
+0,01-ai Yi Chat (34B),2.350261905969674
+13,WizardLM v1.2 (13B),2.276853394780161
","{""x"": ""model"", ""y"": ""chunk_sizes_mean"", ""color"": ""model"", ""labels"": {""model"": ""Model"", ""chunk_sizes_mean"": ""Chunks mean size (characters)""}, ""title"": ""Plot of chunks mean size given the same input for summarization task""}",
diff --git a/data/summary.csv b/data/summary.csv
index 47c9d5eb9051282b40d7cf36dee5fbd09e49ee93..1e43d7a356981b61f75749c0f26e50f19b5e4c03 100644
--- a/data/summary.csv
+++ b/data/summary.csv
@@ -3,10 +3,14 @@ model,language,template_name,execution_time,characters_count,words_count,chunk_s
01-ai Yi Chat (34B),english,markdown,4.875209599125142,537.8265306122449,90.21428571428571,4.23145472061657,1.0,15.0,0.029477664979513287,0.000396728515625,0.6902577877044678
01-ai Yi Chat (34B),japanese,json,12.8705559865928,388.04938271604937,11.691358024691358,1.2000610873549176,1.0,13.0,0.036714292821464704,0.00048804283142089844,0.7141768932342529
01-ai Yi Chat (34B),japanese,markdown,19.812452404686574,573.056179775281,16.9438202247191,1.1449288376060702,1.0,13.0,0.03677184357071728,0.0004608631134033203,0.6928038597106934
+01-ai Yi Chat (34B),ukrainian,json,8.188566315174103,496.25,69.6,1.828481945467944,1.0,12.0,0.02802372422390504,0.0011577606201171875,0.16095876693725586
+01-ai Yi Chat (34B),ukrainian,markdown,16.368104696273804,1032.55,142.55,1.874807081252837,1.0,15.0,0.028322039070423764,0.0008616447448730469,0.6243219375610352
Chronos Hermes (13B),english,json,4.721294949054718,365.9,62.69,3.490080122090805,1.0,15.0,0.021018946593792918,0.00027179718017578125,0.6065487861633301
Chronos Hermes (13B),english,markdown,4.458954448602637,358.2857142857143,61.48979591836735,3.6168108776267,1.0,14.0,0.020784828659925238,0.0004761219024658203,0.3964853286743164
Chronos Hermes (13B),japanese,json,18.112667706277634,683.2083333333334,21.72222222222222,1.150182379349046,1.0,14.0,0.02361172727552015,0.0003821849822998047,0.5440409183502197
Chronos Hermes (13B),japanese,markdown,13.88519547736808,1072.5479452054794,140.34246575342465,2.514403160024407,1.0,16.0,0.024182286664176957,0.0004260540008544922,0.39719629287719727
+Chronos Hermes (13B),ukrainian,json,4.837301111221313,536.05,76.0,2.569750719079578,1.0,12.0,0.020274785655342607,0.0009684562683105469,0.1987767219543457
+Chronos Hermes (13B),ukrainian,markdown,5.522948348522187,625.0,86.8,2.5130679533574587,1.0,10.0,0.02013855738768292,0.0009472370147705078,0.2056293487548828
Falcon Instruct (7B),english,json,1.8377627313953557,82.93150684931507,15.849315068493151,4.230607966457023,1.0,13.0,0.012668974994363525,0.0004930496215820312,0.09698057174682617
Falcon Instruct (7B),english,markdown,1.8538796330841494,90.4225352112676,15.408450704225352,4.648805213613324,1.0,13.0,0.012733512128461192,0.0003917217254638672,0.09382963180541992
Falcon Instruct (7B),japanese,json,1.0451691810120927,35.819148936170215,7.127659574468085,3.7163355408388523,1.0,10.0,0.01360485859001446,0.00037479400634765625,0.08153510093688965
@@ -15,10 +19,14 @@ LLaMA-2 Chat (7B),english,json,2.5491372994754626,344.3695652173913,59.836956521
LLaMA-2 Chat (7B),english,markdown,2.7005234983834354,394.3068181818182,68.39772727272727,3.7214714714714714,1.0,16.0,0.007627917532755141,0.00035452842712402344,0.2196364402770996
LLaMA-2 Chat (7B),japanese,json,3.9193407914724694,474.2048192771084,53.433734939759034,1.960304811236179,1.0,14.0,0.009285861508448958,0.00035572052001953125,0.1930844783782959
LLaMA-2 Chat (7B),japanese,markdown,4.234327340667898,712.875,69.01136363636364,1.9195557051497811,1.0,16.0,0.008488639047908132,0.0003447532653808594,0.2040574550628662
+LLaMA-2 Chat (7B),ukrainian,json,2.0112603068351746,463.4,65.35,2.618079096045198,1.0,14.0,0.007828741625877423,0.0006103515625,0.09551024436950684
+LLaMA-2 Chat (7B),ukrainian,markdown,2.2340248942375185,586.7,85.0,2.6203662349263066,1.0,10.0,0.007689223325694443,0.0005850791931152344,0.2752835750579834
Mistral (7B) Instruct v0.2 (Together AI),english,json,2.0483637296531096,342.8995983935743,56.7710843373494,3.79526159043428,1.0,15.0,0.007936270601427141,0.0002071857452392578,0.30161213874816895
Mistral (7B) Instruct v0.2 (Together AI),english,markdown,2.544825687053356,450.6170212765957,72.89893617021276,3.7039174536551243,1.0,15.0,0.00792093470239189,0.0002067089080810547,0.4635443687438965
Mistral (7B) Instruct v0.2 (Together AI),japanese,json,3.7119850810751855,269.34939759036143,5.162650602409639,1.0478310796559724,1.0,13.0,0.009880354483175478,0.00023865699768066406,2.616291046142578
Mistral (7B) Instruct v0.2 (Together AI),japanese,markdown,7.5212546869509485,1171.8265895953757,137.8612716763006,1.9919624258145658,1.0,109.0,0.010850601641771877,0.00021409988403320312,8.214757204055786
+Mistral (7B) Instruct v0.2 (Together AI),ukrainian,json,2.769386518001556,502.45,69.7,2.338064215914379,1.0,10.0,0.010534280618993334,0.0005958080291748047,0.46826767921447754
+Mistral (7B) Instruct v0.2 (Together AI),ukrainian,markdown,3.3830580353736877,714.6,100.15,2.4565142660708146,1.0,13.0,0.010020564986734138,0.0005855560302734375,0.29975008964538574
Mistral-7B-Instruct-v0.2,english,json,4.296516989732718,339.22727272727275,54.77272727272727,4.079097368626532,1.0,15.0,0.04079543511947759,0.00024437904357910156,60.7178213596344
Mistral-7B-Instruct-v0.2,english,markdown,3.8693686013526105,365.531914893617,57.56382978723404,4.128319115703472,1.0,16.0,0.03467739830076673,0.020945072174072266,0.25538039207458496
Mistral-7B-Instruct-v0.2,japanese,json,2.613244720867702,56.298701298701296,3.4675324675324677,1.2642169728783903,1.0,11.0,0.04004790950495914,0.00047779083251953125,3.370748996734619
@@ -27,34 +35,49 @@ Mixtral-8x7B-Instruct-v0.1,english,json,3.665129848137623,403.2542016806723,62.4
Mixtral-8x7B-Instruct-v0.1,english,markdown,5.775103669417532,522.9172932330827,83.45864661654136,4.350284606242572,1.0,15.0,0.015317759848210679,0.00020313262939453125,1.7103569507598877
Mixtral-8x7B-Instruct-v0.1,japanese,json,7.287098480350196,556.9218106995885,63.477366255144034,2.2494971825601304,1.0,109.0,0.02290278010061531,0.00020551681518554688,76.45081758499146
Mixtral-8x7B-Instruct-v0.1,japanese,markdown,9.511569020294008,432.14285714285717,7.2976190476190474,1.0973066110456153,1.0,12.0,0.017938276822029118,0.00019669532775878906,3.0317282676696777
+Mixtral-8x7B-Instruct-v0.1,ukrainian,json,2.900975728034973,476.7,67.9,2.643926788685524,1.0,14.0,0.012538410462872955,0.0006000995635986328,0.6836926937103271
+Mixtral-8x7B-Instruct-v0.1,ukrainian,markdown,3.9911201357841493,652.5,87.6,2.3870495701481618,1.0,14.0,0.012435001452138791,0.0005846023559570312,0.6808013916015625
OpenHermes-2.5-Mistral (7B),english,json,2.299964639605308,354.92857142857144,58.66326530612245,3.597745138601572,1.0,15.0,0.009893205301989491,0.0003464221954345703,0.34207844734191895
OpenHermes-2.5-Mistral (7B),english,markdown,2.595633898092353,380.0978260869565,63.68478260869565,3.918973439426202,1.0,15.0,0.01048721514193851,0.00033664703369140625,0.3534977436065674
OpenHermes-2.5-Mistral (7B),japanese,json,2.513281902488397,143.57142857142858,4.051020408163265,1.0734721904325932,1.0,8.0,0.014181530556562719,0.0003523826599121094,0.48614931106567383
OpenHermes-2.5-Mistral (7B),japanese,markdown,4.1442127884650715,333.18367346938777,4.724489795918367,1.0109604309864388,1.0,9.0,0.010979779298567272,0.0003566741943359375,0.6548421382904053
+OpenHermes-2.5-Mistral (7B),ukrainian,json,2.4432021379470825,399.15,55.95,2.3653333333333335,1.0,10.0,0.011227967085661712,0.0005941390991210938,0.34734392166137695
+OpenHermes-2.5-Mistral (7B),ukrainian,markdown,2.8758589148521425,501.9,70.0,2.3355048859934855,1.0,10.0,0.011170440188337337,0.0005991458892822266,0.4472694396972656
Qwen 1.5 Chat (7B),english,json,2.1808673976570048,314.01075268817203,52.655913978494624,3.838459516298633,1.0,15.0,0.006817500015914628,0.00034117698669433594,0.13403892517089844
Qwen 1.5 Chat (7B),english,markdown,2.3712507900438813,424.5684210526316,70.2421052631579,4.211108790979328,1.0,15.0,0.006809696296879881,0.0003638267517089844,0.14255690574645996
Qwen 1.5 Chat (7B),japanese,json,2.2431268441049674,207.58947368421053,4.11578947368421,1.4861341371514696,1.0,17.0,0.007540746784425806,0.00038743019104003906,0.21312594413757324
Qwen 1.5 Chat (7B),japanese,markdown,2.0088862404227257,214.79166666666666,1.7395833333333333,1.4659462533769374,1.0,17.0,0.007425861000658759,0.00035572052001953125,0.13323616981506348
+Qwen 1.5 Chat (7B),ukrainian,json,1.8380907773971558,367.15,51.65,2.0073810825587755,1.0,12.0,0.00695894590944068,0.0006153583526611328,0.13977646827697754
+Qwen 1.5 Chat (7B),ukrainian,markdown,3.1421071767807005,666.5,90.85,1.9815668202764978,1.0,12.0,0.00806936215411612,0.0006673336029052734,0.2673149108886719
RedPajama-INCITE Chat (7B),english,json,2.0967678966976346,55.61904761904762,5.416666666666667,2.916354556803995,1.0,15.0,0.026109205798412232,0.0004024505615234375,0.131911039352417
RedPajama-INCITE Chat (7B),english,markdown,2.0654409464667824,54.84705882352941,5.176470588235294,2.9119300437226734,1.0,70.0,0.02642942978991783,0.0004153251647949219,0.939018964767456
RedPajama-INCITE Chat (7B),japanese,json,1.8026243034674196,53.40816326530612,5.877551020408164,2.8079399141630903,1.0,13.0,0.027359759986656417,0.0003719329833984375,0.1054222583770752
RedPajama-INCITE Chat (7B),japanese,markdown,1.9637482093305003,77.66326530612245,4.36734693877551,3.999474513925381,1.0,70.0,0.027386420911948303,0.0007987022399902344,0.4024965763092041
+RedPajama-INCITE Chat (7B),ukrainian,json,1.3467459321022033,56.25,5.15,2.9296875,1.0,11.0,0.02616042209168275,0.0007944107055664062,0.06468510627746582
+RedPajama-INCITE Chat (7B),ukrainian,markdown,1.297282326221466,60.8,6.15,3.474285714285714,1.0,70.0,0.025852158410208565,0.0007431507110595703,0.08039665222167969
Snorkel Mistral PairRM DPO (7B),english,json,3.0900819142659506,951.3444444444444,151.1888888888889,3.789042793291145,1.0,15.0,0.0072367200916011905,0.00022792816162109375,0.1967298984527588
Snorkel Mistral PairRM DPO (7B),english,markdown,2.628829130373503,499.4736842105263,77.45263157894736,3.989071038251366,1.0,15.0,0.008036611600301004,0.0003504753112792969,0.27209019660949707
Snorkel Mistral PairRM DPO (7B),japanese,json,7.3780752953062665,1579.6914893617022,191.4468085106383,1.9991249091251784,1.0,15.0,0.008245711491711964,0.0003476142883300781,0.49362945556640625
Snorkel Mistral PairRM DPO (7B),japanese,markdown,86.86322889429458,16876.14893617021,447.1808510638298,1.0719315956553996,1.0,14.0,0.0054663013888370925,0.00032520294189453125,1.1380209922790527
+Snorkel Mistral PairRM DPO (7B),ukrainian,json,6.105635261535644,1655.45,237.05,2.9667562724014336,1.0,15.0,0.010083894127158708,0.0005767345428466797,0.26311397552490234
+Snorkel Mistral PairRM DPO (7B),ukrainian,markdown,13.854707729816436,3487.85,286.15,1.6177787054430761,1.0,16.0,0.0062002094289766,0.000553131103515625,0.3519413471221924
TinyLlama/TinyLlama-1.1B-Chat-v1.0,english,json,1.9458302193674548,366.25,61.58965517241379,4.169774654522613,1.0,13.0,0.012296600931852907,0.00024318695068359375,0.17363858222961426
TinyLlama/TinyLlama-1.1B-Chat-v1.0,english,markdown,1.6830505162477494,282.55,47.1125,3.9572829131652663,1.0,14.0,0.012217252319600401,0.0003170967102050781,0.15227961540222168
TinyLlama/TinyLlama-1.1B-Chat-v1.0,japanese,json,1.4167885013989039,72.81428571428572,8.8,2.0274463007159906,1.0,13.0,0.014790566363975749,0.00031280517578125,0.3310587406158447
TinyLlama/TinyLlama-1.1B-Chat-v1.0,japanese,markdown,1.336316986878713,89.78333333333333,12.716666666666667,2.432054176072235,1.0,13.0,0.013910464400900675,0.0005300045013427734,0.176621675491333
+TinyLlama/TinyLlama-1.1B-Chat-v1.0,ukrainian,markdown,1.8998890399932862,319.35,50.2,3.4338709677419357,1.0,15.0,0.011969600441635296,0.0006313323974609375,0.15036249160766602
Vicuna v1.5 (7B),english,json,2.811121812619661,371.14736842105265,63.88421052631579,3.485468564650059,1.0,15.0,0.011213418027071577,0.0003688335418701172,0.11755537986755371
Vicuna v1.5 (7B),english,markdown,2.038096696772474,380.3085106382979,64.2872340425532,3.6980448950036204,1.0,15.0,0.011199472534307541,0.00037550926208496094,0.1981511116027832
Vicuna v1.5 (7B),japanese,json,4.888940976460774,293.85333333333335,15.04,1.2507235684694398,1.0,15.0,0.015249164155471149,0.0003762245178222656,0.319460391998291
Vicuna v1.5 (7B),japanese,markdown,5.688798353075981,300.2125,2.9,1.0294029402940295,1.0,11.0,0.015179881321561766,0.0003802776336669922,0.19443845748901367
+Vicuna v1.5 (7B),ukrainian,json,2.355931317806244,411.65,57.75,2.5544523735650015,1.0,10.0,0.011324495104873473,0.0006866455078125,0.20106196403503418
+Vicuna v1.5 (7B),ukrainian,markdown,2.3733089685440065,442.05,61.55,2.5611239860950175,1.0,10.0,0.011319451647036873,0.0007417201995849609,0.09550309181213379
WizardLM v1.2 (13B),english,json,3.0704635977745056,284.04545454545456,48.71590909090909,3.286785009861933,1.0,15.0,0.021288358060095672,0.00036406517028808594,0.4714198112487793
WizardLM v1.2 (13B),english,markdown,3.646563207401949,367.45882352941175,62.84705882352941,3.7540865384615385,1.0,14.0,0.021508242619725376,0.0005571842193603516,0.5012450218200684
WizardLM v1.2 (13B),japanese,json,6.563389152896647,218.3877551020408,4.642857142857143,1.0465013935748864,1.0,7.0,0.027866148073150618,0.00034737586975097656,0.5480227470397949
WizardLM v1.2 (13B),japanese,markdown,8.748523741352315,352.1530612244898,17.73469387755102,1.2008002783576897,1.0,11.0,0.02774835801738455,0.000453948974609375,0.8807919025421143
+WizardLM v1.2 (13B),ukrainian,json,4.173892283439637,438.45,61.4,2.558051341890315,1.0,10.0,0.020677012420729966,0.0007526874542236328,0.3674149513244629
+WizardLM v1.2 (13B),ukrainian,markdown,9.010480010509491,705.45,132.4,1.8148958065346026,1.0,12.0,0.02172789176202906,0.0006875991821289062,0.6248154640197754
chat-bison (PaLM 2),english,json,2.5406186148524283,381.7267857142857,60.88125,78.31727422604872,1.0,142.0,0.3495202942369709,0.0003688335418701172,7.334931373596191
chat-bison (PaLM 2),english,markdown,2.9635989278554917,490.4875,78.5375,84.20386266094421,1.0,138.0,0.3641034907025841,0.0004639625549316406,4.600815534591675
chat-bison (PaLM 2),japanese,json,3.0616454005241396,232.68,10.48,34.21764705882353,3.0,124.0,0.326771635167739,0.0005857944488525391,2.7724690437316895
@@ -67,28 +90,41 @@ gemini-pro,english,json,2.7415290858475547,388.5206321334504,62.01755926251097,2
gemini-pro,english,markdown,3.0089986324310303,487.1458333333333,80.67708333333333,183.39607843137256,12.0,472.0,1.1130499073103362,0.0006177425384521484,4.875006198883057
gemini-pro,japanese,json,4.5300196591070145,276.6694915254237,6.983050847457627,91.1927374301676,4.0,333.0,1.4752027082709627,0.0006062984466552734,8.291066646575928
gemini-pro,japanese,markdown,6.166934008781727,422.7307692307692,9.096153846153847,90.83471074380165,9.0,266.0,1.3123543331445742,0.0006639957427978516,13.754565954208374
+gemini-pro,ukrainian,json,2.6666214764118195,489.125,68.0625,122.28125,35.0,288.0,0.63798962906003,0.0015087127685546875,2.747244358062744
+gemini-pro,ukrainian,markdown,2.7587877301608814,496.8235294117647,68.58823529411765,115.6986301369863,30.0,284.0,0.6183678385329573,0.0013196468353271484,2.6417908668518066
gpt-3.5-turbo,english,call,2.908525016600803,456.85911016949154,72.26059322033899,,,,,,
gpt-3.5-turbo,english,json,2.566863525494622,398.56960556844547,65.32366589327147,4.806746320443226,1.0,16.0,0.015585779219764062,0.0005395412445068359,1.4500916004180908
gpt-3.5-turbo,english,markdown,2.3388922008184285,506.90384615384613,81.99038461538461,5.050584403142365,1.0,16.0,0.01715806382989308,0.0005617141723632812,1.127073049545288
gpt-3.5-turbo,japanese,call,7.255881716940138,382.95,6.351111111111111,,,,,,
gpt-3.5-turbo,japanese,json,5.226112197780009,237.1069182389937,3.559748427672956,1.1291481969569905,1.0,7.0,0.02129129201220198,0.0005452632904052734,13.75982117652893
gpt-3.5-turbo,japanese,markdown,7.29901300883684,358.72131147540983,6.065573770491803,1.1894977168949772,1.0,15.0,0.021750167313542772,0.0005612373352050781,13.71114993095398
+gpt-3.5-turbo,ukrainian,call,4.756740653514862,345.65,47.05,,,,,,
+gpt-3.5-turbo,ukrainian,json,4.362081623077392,335.45,47.1,1.7857332978440246,1.0,12.0,0.01937515550958117,0.0007231235504150391,0.16832637786865234
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gpt-4,english,call,9.15708539162022,374.16815144766144,59.661469933184854,,,,,,
gpt-4,english,json,7.963626848197565,383.3707317073171,61.69512195121951,4.7834568389659005,1.0,16.0,0.08687007305138472,0.000537872314453125,3.531284809112549
gpt-4,english,markdown,10.02310108244419,428.35,67.825,4.990243192078054,1.0,16.0,0.10377185442257747,0.0005447864532470703,2.087376117706299
gpt-4,japanese,call,24.259297592319243,214.9076023391813,1.1695906432748537,,,,,,
gpt-4,japanese,json,22.703774025460252,211.63025210084032,4.168067226890757,1.1685226429101707,1.0,7.0,0.11835955503744466,0.0005507469177246094,3.5608017444610596
gpt-4,japanese,markdown,31.99519391655922,275.1,2.925,1.1327980234712787,1.0,9.0,0.12606545905407157,0.0005619525909423828,2.9794347286224365
+gpt-4,ukrainian,call,11.302998685836792,375.4,51.7,,,,,,
+gpt-4,ukrainian,json,11.601064097881316,398.3,55.2,1.7596642368014137,1.0,12.0,0.047534360352641095,0.0006132125854492188,2.0929741859436035
+gpt-4,ukrainian,markdown,14.820814204216003,462.45,62.9,1.7401693320790217,1.0,12.0,0.05274127191344411,0.0006010532379150391,1.103933572769165
gpt-4-turbo,english,call,7.319288367695279,372.86,58.532222222222224,,,,,,
gpt-4-turbo,english,json,6.457074524425879,407.0243902439024,64.18414634146342,4.735998183701559,1.0,17.0,0.06474667943090798,0.0006806850433349609,5.431884765625
gpt-4-turbo,english,markdown,8.325546002388,462.1125,71.925,4.984360253471754,1.0,16.0,0.07950969425127868,0.0006999969482421875,2.0926766395568848
gpt-4-turbo,japanese,call,32.67405146055443,372.7860465116279,24.886046511627907,,,,,,
gpt-4-turbo,japanese,json,19.359180968999862,250.44166666666666,6.183333333333334,1.184261339007763,1.0,8.0,0.08582140138847597,0.0006830692291259766,13.252992153167725
gpt-4-turbo,japanese,markdown,20.977174520492554,283.55,4.45,1.1460038395473375,1.0,10.0,0.07991695286011809,0.0007085800170898438,2.3845231533050537
+gpt-4-turbo,ukrainian,call,7.4254711985588076,231.6,26.4,,,,,,
+gpt-4-turbo,ukrainian,json,11.307173728942871,361.7,51.0,1.792812887236679,1.0,10.0,0.05152765177144142,0.0007297992706298828,1.2793521881103516
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llama-2-70b-chat,english,json,2.917410132025375,355.7463556851312,54.861030126336246,4.02267032967033,1.0,15.0,0.019103390617685005,0.0001990795135498047,3.8762006759643555
llama-2-70b-chat,english,markdown,4.99397261045417,827.5714285714286,125.95918367346938,4.177931176591799,1.0,16.0,0.018161741699259625,0.0002124309539794922,0.7439091205596924
llama-2-70b-chat,japanese,json,5.265992468681889,369.3768115942029,42.56521739130435,2.3362207250561435,1.0,15.0,0.023359512199899433,0.00020837783813476562,0.9314718246459961
llama-2-70b-chat,japanese,markdown,11.084085723449444,1379.6896551724137,158.01724137931035,2.2644745033674796,1.0,16.0,0.01566780037868879,0.0002117156982421875,76.44498753547668
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zephyr-7b-beta,english,json,4.252429554081262,376.7899686520376,61.78369905956113,4.26030553291036,1.0,14.0,0.03764735399563352,0.0003070831298828125,1.0504331588745117
zephyr-7b-beta,english,markdown,4.788659170695714,376.10714285714283,61.15,4.393408427200668,1.0,15.0,0.04506704832147846,0.00040459632873535156,75.15552616119385
zephyr-7b-beta,japanese,json,2.6574003274624163,44.75,3.6923076923076925,1.152208358090711,1.0,7.0,0.044344271615274496,0.018090486526489258,1.1675689220428467