Lisa Dunlap commited on
Commit
247422d
β€’
2 Parent(s): 350ba9e e121d4e

Merge branch 'per_task_results' into pr/26

Browse files
Files changed (2) hide show
  1. app.py +145 -61
  2. theme.json +1 -0
app.py CHANGED
@@ -26,23 +26,28 @@ def make_default_md(arena_df, elo_results):
26
  | [Vote](https://chat.lmsys.org) | [Blog](https://lmsys.org/blog/2023-05-03-arena/) | [GitHub](https://github.com/lm-sys/FastChat) | [Paper](https://arxiv.org/abs/2306.05685) | [Dataset](https://github.com/lm-sys/FastChat/blob/main/docs/dataset_release.md) | [Twitter](https://twitter.com/lmsysorg) | [Discord](https://discord.gg/HSWAKCrnFx) |
27
 
28
  LMSYS [Chatbot Arena](https://lmsys.org/blog/2023-05-03-arena/) is a crowdsourced open platform for LLM evals.
29
- We've collected over **500,000** human preference votes to rank LLMs with the Elo ranking system.
30
  """
31
  return leaderboard_md
32
 
33
 
34
- def make_arena_leaderboard_md(arena_df):
35
  total_votes = sum(arena_df["num_battles"]) // 2
36
  total_models = len(arena_df)
37
-
 
 
 
 
 
 
38
  leaderboard_md = f"""
39
- Total #models: **{total_models}**. Total #votes: **{total_votes}**. Last updated: March 29, 2024.
40
 
41
- Contribute your vote πŸ—³οΈ at [chat.lmsys.org](https://chat.lmsys.org)! Find more analysis in the [notebook]({notebook_url}).
42
  """
43
  return leaderboard_md
44
 
45
-
46
  def make_full_leaderboard_md(elo_results):
47
  leaderboard_md = f"""
48
  Three benchmarks are displayed: **Arena Elo**, **MT-Bench** and **MMLU**.
@@ -202,51 +207,82 @@ def get_full_table(arena_df, model_table_df):
202
  values.sort(key=lambda x: -x[1] if not np.isnan(x[1]) else 1e9)
203
  return values
204
 
205
-
206
- def get_arena_table(arena_df, model_table_df):
207
- # sort by rating
 
 
 
 
 
 
208
  arena_df = arena_df.sort_values(by=["rating"], ascending=False)
 
 
 
 
 
 
 
 
 
 
209
  values = []
210
  for i in range(len(arena_df)):
211
  row = []
212
  model_key = arena_df.index[i]
213
- model_name = model_table_df[model_table_df["key"] == model_key]["Model"].values[
214
- 0
215
- ]
216
-
217
- # rank
218
- ranking = arena_df.iloc[i].get("final_ranking") or i+1
219
- row.append(ranking)
220
- # model display name
221
- row.append(model_name)
222
- # elo rating
223
- row.append(round(arena_df.iloc[i]["rating"]))
224
- upper_diff = round(
225
- arena_df.iloc[i]["rating_q975"] - arena_df.iloc[i]["rating"]
226
- )
227
- lower_diff = round(
228
- arena_df.iloc[i]["rating"] - arena_df.iloc[i]["rating_q025"]
229
- )
230
- row.append(f"+{upper_diff}/-{lower_diff}")
231
- # num battles
232
- row.append(round(arena_df.iloc[i]["num_battles"]))
233
- # Organization
234
- row.append(
235
- model_table_df[model_table_df["key"] == model_key]["Organization"].values[0]
236
- )
237
- # license
238
- row.append(
239
- model_table_df[model_table_df["key"] == model_key]["License"].values[0]
240
- )
 
241
 
242
- cutoff_date = model_table_df[model_table_df["key"] == model_key]["Knowledge cutoff date"].values[0]
243
- if cutoff_date == "-":
244
- row.append("Unknown")
245
- else:
246
- row.append(cutoff_date)
247
- values.append(row)
 
 
248
  return values
249
 
 
 
 
 
 
 
 
 
 
 
 
 
250
  def build_leaderboard_tab(elo_results_file, leaderboard_table_file, show_plot=False):
251
  if elo_results_file is None: # Do live update
252
  default_md = "Loading ..."
@@ -255,6 +291,9 @@ def build_leaderboard_tab(elo_results_file, leaderboard_table_file, show_plot=Fa
255
  with open(elo_results_file, "rb") as fin:
256
  elo_results = pickle.load(fin)
257
  if "full" in elo_results:
 
 
 
258
  elo_results = elo_results["full"]
259
 
260
  p1 = elo_results["win_fraction_heatmap"]
@@ -262,9 +301,13 @@ def build_leaderboard_tab(elo_results_file, leaderboard_table_file, show_plot=Fa
262
  p3 = elo_results["bootstrap_elo_rating"]
263
  p4 = elo_results["average_win_rate_bar"]
264
  arena_df = elo_results["leaderboard_table_df"]
 
 
 
265
  default_md = make_default_md(arena_df, elo_results)
266
 
267
  md_1 = gr.Markdown(default_md, elem_id="leaderboard_markdown")
 
268
  if leaderboard_table_file:
269
  data = load_leaderboard_table_csv(leaderboard_table_file)
270
  model_table_df = pd.DataFrame(data)
@@ -274,8 +317,21 @@ def build_leaderboard_tab(elo_results_file, leaderboard_table_file, show_plot=Fa
274
  arena_table_vals = get_arena_table(arena_df, model_table_df)
275
  with gr.Tab("Arena Elo", id=0):
276
  md = make_arena_leaderboard_md(arena_df)
277
- gr.Markdown(md, elem_id="leaderboard_markdown")
278
- gr.Dataframe(
 
 
 
 
 
 
 
 
 
 
 
 
 
279
  headers=[
280
  "Rank",
281
  "πŸ€– Model",
@@ -299,9 +355,10 @@ def build_leaderboard_tab(elo_results_file, leaderboard_table_file, show_plot=Fa
299
  value=arena_table_vals,
300
  elem_id="arena_leaderboard_dataframe",
301
  height=700,
302
- column_widths=[50, 200, 120, 100, 100, 150, 150, 100],
303
  wrap=True,
304
  )
 
305
  with gr.Tab("Full Leaderboard", id=1):
306
  md = make_full_leaderboard_md(elo_results)
307
  gr.Markdown(md, elem_id="leaderboard_markdown")
@@ -335,7 +392,7 @@ def build_leaderboard_tab(elo_results_file, leaderboard_table_file, show_plot=Fa
335
  gr.Markdown(
336
  f"""Note: we take the 95% confidence interval into account when determining a model's ranking.
337
  A model is ranked higher only if its lower bound of model score is higher than the upper bound of the other model's score.
338
- See Figure 3 below for visualization of the confidence intervals.
339
  """,
340
  elem_id="leaderboard_markdown"
341
  )
@@ -343,35 +400,38 @@ See Figure 3 below for visualization of the confidence intervals.
343
  leader_component_values[:] = [default_md, p1, p2, p3, p4]
344
 
345
  if show_plot:
346
- gr.Markdown(
347
- f"""## More Statistics for Chatbot Arena\n
348
- Below are figures for more statistics. The code for generating them is also included in this [notebook]({notebook_url}).
349
- You can find more discussions in this blog [post](https://lmsys.org/blog/2023-12-07-leaderboard/).
350
- """,
351
- elem_id="leaderboard_markdown"
352
- )
353
  with gr.Row():
354
  with gr.Column():
355
  gr.Markdown(
356
- "#### Figure 1: Fraction of Model A Wins for All Non-tied A vs. B Battles"
357
  )
358
- plot_1 = gr.Plot(p1, show_label=False)
359
  with gr.Column():
360
  gr.Markdown(
361
- "#### Figure 2: Battle Count for Each Combination of Models (without Ties)"
362
  )
363
  plot_2 = gr.Plot(p2, show_label=False)
364
  with gr.Row():
365
  with gr.Column():
366
  gr.Markdown(
367
- "#### Figure 3: Confidence Intervals on Model Strength (via Bootstrapping)"
368
  )
369
  plot_3 = gr.Plot(p3, show_label=False)
370
  with gr.Column():
371
  gr.Markdown(
372
- "#### Figure 4: Average Win Rate Against All Other Models (Assuming Uniform Sampling and No Ties)"
373
  )
374
  plot_4 = gr.Plot(p4, show_label=False)
 
 
 
 
 
375
 
376
  gr.Markdown(acknowledgment_md)
377
 
@@ -379,6 +439,7 @@ You can find more discussions in this blog [post](https://lmsys.org/blog/2023-12
379
  return [md_1, plot_1, plot_2, plot_3, plot_4]
380
  return [md_1]
381
 
 
382
  block_css = """
383
  #notice_markdown {
384
  font-size: 104%
@@ -397,9 +458,30 @@ block_css = """
397
  padding-top: 6px;
398
  padding-bottom: 6px;
399
  }
 
400
  #leaderboard_dataframe td {
401
  line-height: 0.1em;
402
  }
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
403
  footer {
404
  display:none !important
405
  }
@@ -429,10 +511,12 @@ We thank [Kaggle](https://www.kaggle.com/), [MBZUAI](https://mbzuai.ac.ae/), [a1
429
 
430
  def build_demo(elo_results_file, leaderboard_table_file):
431
  text_size = gr.themes.sizes.text_lg
432
-
 
433
  with gr.Blocks(
434
  title="Chatbot Arena Leaderboard",
435
- theme=gr.themes.Base(text_size=text_size),
 
436
  css=block_css,
437
  ) as demo:
438
  leader_components = build_leaderboard_tab(
 
26
  | [Vote](https://chat.lmsys.org) | [Blog](https://lmsys.org/blog/2023-05-03-arena/) | [GitHub](https://github.com/lm-sys/FastChat) | [Paper](https://arxiv.org/abs/2306.05685) | [Dataset](https://github.com/lm-sys/FastChat/blob/main/docs/dataset_release.md) | [Twitter](https://twitter.com/lmsysorg) | [Discord](https://discord.gg/HSWAKCrnFx) |
27
 
28
  LMSYS [Chatbot Arena](https://lmsys.org/blog/2023-05-03-arena/) is a crowdsourced open platform for LLM evals.
29
+ We've collected over **500,000** human preference votes to rank LLMs with the Elo ranking system. Contribute your vote πŸ—³οΈ at [chat.lmsys.org](https://chat.lmsys.org)!
30
  """
31
  return leaderboard_md
32
 
33
 
34
+ def make_arena_leaderboard_md(arena_df, arena_subset_df=None, name="Overall"):
35
  total_votes = sum(arena_df["num_battles"]) // 2
36
  total_models = len(arena_df)
37
+ space = "   "
38
+ if arena_subset_df is not None:
39
+ total_subset_votes = sum(arena_subset_df["num_battles"]) // 2
40
+ total_subset_models = len(arena_subset_df)
41
+ vote_str = f"{space} {name} #models: **{total_subset_models}**.{space} {name} #votes: **{'{:,}'.format(total_subset_votes)}**."
42
+ else:
43
+ vote_str = ""
44
  leaderboard_md = f"""
45
+ Total #models: **{total_models}**.{space} Total #votes: **{"{:,}".format(total_votes)}**.{vote_str}{space} Last updated: March 29, 2024.
46
 
47
+ **NEW!** Click the buttons below to view the ELO leaderboard and stats for different input categories. You are currently viewing **{name}** inputs.
48
  """
49
  return leaderboard_md
50
 
 
51
  def make_full_leaderboard_md(elo_results):
52
  leaderboard_md = f"""
53
  Three benchmarks are displayed: **Arena Elo**, **MT-Bench** and **MMLU**.
 
207
  values.sort(key=lambda x: -x[1] if not np.isnan(x[1]) else 1e9)
208
  return values
209
 
210
+ def create_ranking_str(ranking, ranking_difference):
211
+ if ranking_difference > 0:
212
+ return f"{int(ranking)} (\u2191{int(ranking_difference)})"
213
+ elif ranking_difference < 0:
214
+ return f"{int(ranking)} (\u2193{int(-ranking_difference)})"
215
+ else:
216
+ return f"{int(ranking)}"
217
+
218
+ def get_arena_table(arena_df, model_table_df, arena_subset_df=None):
219
  arena_df = arena_df.sort_values(by=["rating"], ascending=False)
220
+ arena_df = arena_df.sort_values(by=["final_ranking"], ascending=True)
221
+ # sort by rating
222
+ if arena_subset_df is not None:
223
+ arena_subset_df = arena_subset_df.sort_values(by=["rating"], ascending=False)
224
+ arena_subset_df = arena_subset_df.sort_values(by=["final_ranking"], ascending=True)
225
+ # join arena_df and arena_subset_df on index
226
+ arena_df = arena_subset_df.join(arena_df["final_ranking"], rsuffix="_global", how="inner")
227
+ arena_df['ranking_difference'] = arena_df['final_ranking_global'] - arena_df['final_ranking']
228
+ arena_df["final_ranking"] = arena_df.apply(lambda x: create_ranking_str(x["final_ranking"], x["ranking_difference"]), axis=1)
229
+
230
  values = []
231
  for i in range(len(arena_df)):
232
  row = []
233
  model_key = arena_df.index[i]
234
+ try:
235
+ model_name = model_table_df[model_table_df["key"] == model_key]["Model"].values[
236
+ 0
237
+ ]
238
+
239
+ # rank
240
+ ranking = arena_df.iloc[i].get("final_ranking") or i+1
241
+ row.append(ranking)
242
+ # model display name
243
+ row.append(model_name)
244
+ # elo rating
245
+ row.append(round(arena_df.iloc[i]["rating"]))
246
+ upper_diff = round(
247
+ arena_df.iloc[i]["rating_q975"] - arena_df.iloc[i]["rating"]
248
+ )
249
+ lower_diff = round(
250
+ arena_df.iloc[i]["rating"] - arena_df.iloc[i]["rating_q025"]
251
+ )
252
+ row.append(f"+{upper_diff}/-{lower_diff}")
253
+ # num battles
254
+ row.append(round(arena_df.iloc[i]["num_battles"]))
255
+ # Organization
256
+ row.append(
257
+ model_table_df[model_table_df["key"] == model_key]["Organization"].values[0]
258
+ )
259
+ # license
260
+ row.append(
261
+ model_table_df[model_table_df["key"] == model_key]["License"].values[0]
262
+ )
263
 
264
+ cutoff_date = model_table_df[model_table_df["key"] == model_key]["Knowledge cutoff date"].values[0]
265
+ if cutoff_date == "-":
266
+ row.append("Unknown")
267
+ else:
268
+ row.append(cutoff_date)
269
+ values.append(row)
270
+ except Exception as e:
271
+ print(f"{model_key} - {e}")
272
  return values
273
 
274
+ def update_leaderboard_and_plots(button, arena_df, model_table_df, arena_subset_df, elo_subset_results):
275
+ arena_values = get_arena_table(arena_df, model_table_df, arena_subset_df)
276
+ p1 = elo_subset_results["win_fraction_heatmap"]
277
+ p2 = elo_subset_results["battle_count_heatmap"]
278
+ p3 = elo_subset_results["bootstrap_elo_rating"]
279
+ p4 = elo_subset_results["average_win_rate_bar"]
280
+ more_stats_md = f"""## More Statistics for Chatbot Arena ({button})
281
+ """
282
+ leaderboard_md = make_arena_leaderboard_md(arena_df, arena_subset_df, name=button)
283
+ return arena_values, p1, p2, p3, p4, more_stats_md, leaderboard_md
284
+
285
+
286
  def build_leaderboard_tab(elo_results_file, leaderboard_table_file, show_plot=False):
287
  if elo_results_file is None: # Do live update
288
  default_md = "Loading ..."
 
291
  with open(elo_results_file, "rb") as fin:
292
  elo_results = pickle.load(fin)
293
  if "full" in elo_results:
294
+ elo_chinese_results = elo_results["chinese"]
295
+ elo_long_results = elo_results["long"]
296
+ elo_english_results = elo_results["english"]
297
  elo_results = elo_results["full"]
298
 
299
  p1 = elo_results["win_fraction_heatmap"]
 
301
  p3 = elo_results["bootstrap_elo_rating"]
302
  p4 = elo_results["average_win_rate_bar"]
303
  arena_df = elo_results["leaderboard_table_df"]
304
+ arena_chinese_df = elo_chinese_results["leaderboard_table_df"]
305
+ arena_long_df = elo_long_results["leaderboard_table_df"]
306
+ arena_english_df = elo_english_results["leaderboard_table_df"]
307
  default_md = make_default_md(arena_df, elo_results)
308
 
309
  md_1 = gr.Markdown(default_md, elem_id="leaderboard_markdown")
310
+ # md = make_arena_leaderboard_md(arena_df, arena_chinese_df, arena_long_df, arena_english_df)
311
  if leaderboard_table_file:
312
  data = load_leaderboard_table_csv(leaderboard_table_file)
313
  model_table_df = pd.DataFrame(data)
 
317
  arena_table_vals = get_arena_table(arena_df, model_table_df)
318
  with gr.Tab("Arena Elo", id=0):
319
  md = make_arena_leaderboard_md(arena_df)
320
+ leaderboard_markdown = gr.Markdown(md, elem_id="leaderboard_markdown")
321
+ with gr.Row():
322
+ overall_rating = gr.Button("Overall")
323
+ # update_overall_rating_df = lambda _: get_arena_table(arena_df, model_table_df)
324
+ update_overall_rating_df = lambda x: update_leaderboard_and_plots(x, arena_df, model_table_df, None, elo_results)
325
+ english_rating = gr.Button("English")
326
+ update_english_rating_df = lambda x: update_leaderboard_and_plots(x, arena_df, model_table_df, arena_english_df, elo_english_results)
327
+ # update_english_rating_df = lambda _: get_arena_table(arena_df, model_table_df, arena_english_df)
328
+ chinese_rating = gr.Button("Chinese")
329
+ update_chinese_rating_df = lambda x: update_leaderboard_and_plots(x, arena_df, model_table_df, arena_chinese_df, elo_chinese_results)
330
+ # update_chinese_rating_df = lambda _: get_arena_table(arena_df, model_table_df, arena_chinese_df)
331
+ long_context_rating = gr.Button("Long Context")
332
+ update_long_context_rating_df = lambda x: update_leaderboard_and_plots(x, arena_df, model_table_df, arena_long_df, elo_long_results)
333
+ # update_long_context_rating_df = lambda _: get_arena_table(arena_df, model_table_df, arena_long_df)
334
+ elo_display_df = gr.Dataframe(
335
  headers=[
336
  "Rank",
337
  "πŸ€– Model",
 
355
  value=arena_table_vals,
356
  elem_id="arena_leaderboard_dataframe",
357
  height=700,
358
+ column_widths=[70, 190, 110, 100, 90, 160, 150, 140],
359
  wrap=True,
360
  )
361
+
362
  with gr.Tab("Full Leaderboard", id=1):
363
  md = make_full_leaderboard_md(elo_results)
364
  gr.Markdown(md, elem_id="leaderboard_markdown")
 
392
  gr.Markdown(
393
  f"""Note: we take the 95% confidence interval into account when determining a model's ranking.
394
  A model is ranked higher only if its lower bound of model score is higher than the upper bound of the other model's score.
395
+ See Figure 3 below for visualization of the confidence intervals. Code to recreate these tables and plots in this [notebook]({notebook_url}) and more discussions in this blog [post](https://lmsys.org/blog/2023-12-07-leaderboard/).
396
  """,
397
  elem_id="leaderboard_markdown"
398
  )
 
400
  leader_component_values[:] = [default_md, p1, p2, p3, p4]
401
 
402
  if show_plot:
403
+ # more_stats_md = gr.Markdown(
404
+ # f"""## More Statistics for Chatbot Arena (Overall)""",
405
+ # elem_id="leaderboard_markdown"
406
+ # )
407
+ more_stats_md = gr.Button("More Statistics for Chatbot Arena (Overall)", elem_id="non-interactive-button")
 
 
408
  with gr.Row():
409
  with gr.Column():
410
  gr.Markdown(
411
+ "#### Figure 1: Fraction of Model A Wins for All Non-tied A vs. B Battles", elem_id="plot-title", variant="panel"
412
  )
413
+ plot_1 = gr.Plot(p1, show_label=False, elem_id="plot-container")
414
  with gr.Column():
415
  gr.Markdown(
416
+ "#### Figure 2: Battle Count for Each Combination of Models (without Ties)", elem_id="plot-title"
417
  )
418
  plot_2 = gr.Plot(p2, show_label=False)
419
  with gr.Row():
420
  with gr.Column():
421
  gr.Markdown(
422
+ "#### Figure 3: Confidence Intervals on Model Strength (via Bootstrapping)", elem_id="plot-title"
423
  )
424
  plot_3 = gr.Plot(p3, show_label=False)
425
  with gr.Column():
426
  gr.Markdown(
427
+ "#### Figure 4: Average Win Rate Against All Other Models (Assuming Uniform Sampling and No Ties)", elem_id="plot-title"
428
  )
429
  plot_4 = gr.Plot(p4, show_label=False)
430
+
431
+ overall_rating.click(fn=update_overall_rating_df, inputs=overall_rating, outputs=[elo_display_df, plot_1, plot_2, plot_3, plot_4, more_stats_md, leaderboard_markdown])
432
+ english_rating.click(fn=update_english_rating_df, inputs=english_rating, outputs=[elo_display_df, plot_1, plot_2, plot_3, plot_4, more_stats_md, leaderboard_markdown])
433
+ chinese_rating.click(fn=update_chinese_rating_df, inputs=chinese_rating ,outputs=[elo_display_df, plot_1, plot_2, plot_3, plot_4, more_stats_md, leaderboard_markdown])
434
+ long_context_rating.click(fn=update_long_context_rating_df, inputs=long_context_rating, outputs=[elo_display_df, plot_1, plot_2, plot_3, plot_4, more_stats_md, leaderboard_markdown])
435
 
436
  gr.Markdown(acknowledgment_md)
437
 
 
439
  return [md_1, plot_1, plot_2, plot_3, plot_4]
440
  return [md_1]
441
 
442
+
443
  block_css = """
444
  #notice_markdown {
445
  font-size: 104%
 
458
  padding-top: 6px;
459
  padding-bottom: 6px;
460
  }
461
+
462
  #leaderboard_dataframe td {
463
  line-height: 0.1em;
464
  }
465
+
466
+ #plot-title {
467
+ text-align: center;
468
+ display:block;
469
+ }
470
+
471
+ #non-interactive-button {
472
+ display: inline-block;
473
+ padding: 10px 10px;
474
+ background-color: #f7f7f7; /* Super light grey background */
475
+ color: #000000; /* Black text */
476
+ text-align: center;
477
+ font-size: 26px; /* Larger text */
478
+ border-radius: 0; /* Straight edges, no border radius */
479
+ border: 0px solid #dcdcdc; /* A light grey border to match the background */
480
+ font-weight: bold;
481
+ user-select: none; /* The text inside the button is not selectable */
482
+ pointer-events: none; /* The button is non-interactive */
483
+ }
484
+
485
  footer {
486
  display:none !important
487
  }
 
511
 
512
  def build_demo(elo_results_file, leaderboard_table_file):
513
  text_size = gr.themes.sizes.text_lg
514
+ theme = gr.themes.Base(text_size=text_size)
515
+ theme.set(button_secondary_background_fill_hover="*primary_300", button_secondary_background_fill_hover_dark="*primary_700")
516
  with gr.Blocks(
517
  title="Chatbot Arena Leaderboard",
518
+ theme=theme,
519
+ # theme = gr.themes.Base.load("theme.json"),
520
  css=block_css,
521
  ) as demo:
522
  leader_components = build_leaderboard_tab(
theme.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"theme": {"_font": [{"__gradio_font__": true, "name": "Rubik", "class": "google"}, {"__gradio_font__": true, "name": "ui-sans-serif", "class": "font"}, {"__gradio_font__": true, "name": "system-ui", "class": "font"}, {"__gradio_font__": true, "name": "sans-serif", "class": "font"}], "_font_mono": [{"__gradio_font__": true, "name": "Inconsolata", "class": "google"}, {"__gradio_font__": true, "name": "ui-monospace", "class": "font"}, {"__gradio_font__": true, "name": "Consolas", "class": "font"}, {"__gradio_font__": true, "name": "monospace", "class": "font"}], "_stylesheets": ["https://fonts.googleapis.com/css2?family=Rubik:wght@400;500&display=swap", "https://fonts.googleapis.com/css2?family=Inconsolata:wght@400;500&display=swap"], "text_size": "20px", "background_fill_primary": "white", "background_fill_primary_dark": "*neutral_950", "background_fill_secondary": "*neutral_50", "background_fill_secondary_dark": "*neutral_900", "block_background_fill": "*background_fill_primary", "block_background_fill_dark": "*neutral_800", "block_border_color": "*border_color_primary", "block_border_color_dark": "*border_color_primary", "block_border_width": "1px", "block_border_width_dark": "1px", "block_info_text_color": "*body_text_color_subdued", "block_info_text_color_dark": "*body_text_color_subdued", "block_info_text_size": "*text_sm", "block_info_text_weight": "400", "block_label_background_fill": "*background_fill_primary", "block_label_background_fill_dark": "*background_fill_secondary", "block_label_border_color": "*border_color_primary", "block_label_border_color_dark": "*border_color_primary", "block_label_border_width": "1px", "block_label_border_width_dark": "1px", "block_label_margin": "0", "block_label_padding": "*spacing_sm *spacing_lg", "block_label_radius": "calc(*radius_lg - 1px) 0 calc(*radius_lg - 1px) 0", "block_label_right_radius": "0 calc(*radius_lg - 1px) 0 calc(*radius_lg - 1px)", "block_label_shadow": "*block_shadow", "block_label_text_color": "*neutral_500", "block_label_text_color_dark": "*neutral_200", "block_label_text_size": "*text_sm", "block_label_text_weight": "400", "block_padding": "*spacing_xl calc(*spacing_xl + 2px)", "block_radius": "*radius_lg", "block_shadow": "none", "block_shadow_dark": "none", "block_title_background_fill": "none", "block_title_background_fill_dark": "none", "block_title_border_color": "none", "block_title_border_color_dark": "none", "block_title_border_width": "0px", "block_title_border_width_dark": "0px", "block_title_padding": "0", "block_title_radius": "none", "block_title_text_color": "*neutral_500", "block_title_text_color_dark": "*neutral_200", "block_title_text_size": "*text_md", "block_title_text_weight": "400", "body_background_fill": "*background_fill_primary", "body_background_fill_dark": "*background_fill_primary", "body_text_color": "*neutral_700", "body_text_color_dark": "*neutral_200", "body_text_color_subdued": "*neutral_400", "body_text_color_subdued_dark": "*neutral_500", "body_text_size": "*text_md", "body_text_weight": "400", "border_color_accent": "*primary_300", "border_color_accent_dark": "*neutral_600", "border_color_primary": "*neutral_200", "border_color_primary_dark": "*neutral_700", "button_border_width": "*input_border_width", "button_border_width_dark": "*input_border_width", "button_cancel_background_fill": "*button_secondary_background_fill", "button_cancel_background_fill_dark": "*button_secondary_background_fill", "button_cancel_background_fill_hover": "*button_cancel_background_fill", "button_cancel_background_fill_hover_dark": "*button_cancel_background_fill", "button_cancel_border_color": "*button_secondary_border_color", "button_cancel_border_color_dark": "*button_secondary_border_color", "button_cancel_border_color_hover": "*button_cancel_border_color", "button_cancel_border_color_hover_dark": "*button_cancel_border_color", "button_cancel_text_color": "*button_secondary_text_color", "button_cancel_text_color_dark": "*button_secondary_text_color", "button_cancel_text_color_hover": "*button_cancel_text_color", "button_cancel_text_color_hover_dark": "*button_cancel_text_color", "button_large_padding": "*spacing_lg calc(2 * *spacing_lg)", "button_large_radius": "*radius_lg", "button_large_text_size": "*text_lg", "button_large_text_weight": "500", "button_primary_background_fill": "*primary_200", "button_primary_background_fill_dark": "*primary_700", "button_primary_background_fill_hover": "*button_primary_background_fill", "button_primary_background_fill_hover_dark": "*button_primary_background_fill", "button_primary_border_color": "*primary_200", "button_primary_border_color_dark": "*primary_600", "button_primary_border_color_hover": "*button_primary_border_color", "button_primary_border_color_hover_dark": "*button_primary_border_color", "button_primary_text_color": "*primary_600", "button_primary_text_color_dark": "white", "button_primary_text_color_hover": "*button_primary_text_color", "button_primary_text_color_hover_dark": "*button_primary_text_color", "button_secondary_background_fill": "*neutral_200", "button_secondary_background_fill_dark": "*neutral_600", "button_secondary_background_fill_hover": "*neutral_300", "button_secondary_background_fill_hover_dark": "*neutral_500", "button_secondary_border_color": "*neutral_200", "button_secondary_border_color_dark": "*neutral_600", "button_secondary_border_color_hover": "*button_secondary_border_color", "button_secondary_border_color_hover_dark": "*button_secondary_border_color", "button_secondary_text_color": "*neutral_700", "button_secondary_text_color_dark": "white", "button_secondary_text_color_hover": "*button_secondary_text_color", "button_secondary_text_color_hover_dark": "*button_secondary_text_color", "button_shadow": "none", "button_shadow_active": "none", "button_shadow_hover": "none", "button_small_padding": "*spacing_sm calc(2 * *spacing_sm)", "button_small_radius": "*radius_lg", "button_small_text_size": "*text_md", "button_small_text_weight": "400", "button_transition": "background-color 0.2s ease", "checkbox_background_color": "*background_fill_primary", "checkbox_background_color_dark": "*neutral_800", "checkbox_background_color_focus": "*checkbox_background_color", "checkbox_background_color_focus_dark": "*checkbox_background_color", "checkbox_background_color_hover": "*checkbox_background_color", "checkbox_background_color_hover_dark": "*checkbox_background_color", "checkbox_background_color_selected": "*secondary_600", "checkbox_background_color_selected_dark": "*secondary_600", "checkbox_border_color": "*neutral_300", "checkbox_border_color_dark": "*neutral_700", "checkbox_border_color_focus": "*secondary_500", "checkbox_border_color_focus_dark": "*secondary_500", "checkbox_border_color_hover": "*neutral_300", "checkbox_border_color_hover_dark": "*neutral_600", "checkbox_border_color_selected": "*secondary_600", "checkbox_border_color_selected_dark": "*secondary_600", "checkbox_border_radius": "*radius_sm", "checkbox_border_width": "*input_border_width", "checkbox_border_width_dark": "*input_border_width", "checkbox_check": "url(\"data:image/svg+xml,%3csvg viewBox='0 0 16 16' fill='white' xmlns='http://www.w3.org/2000/svg'%3e%3cpath d='M12.207 4.793a1 1 0 010 1.414l-5 5a1 1 0 01-1.414 0l-2-2a1 1 0 011.414-1.414L6.5 9.086l4.293-4.293a1 1 0 011.414 0z'/%3e%3c/svg%3e\")", "checkbox_label_background_fill": "*button_secondary_background_fill", "checkbox_label_background_fill_dark": "*button_secondary_background_fill", "checkbox_label_background_fill_hover": "*button_secondary_background_fill_hover", "checkbox_label_background_fill_hover_dark": "*button_secondary_background_fill_hover", "checkbox_label_background_fill_selected": "*checkbox_label_background_fill", "checkbox_label_background_fill_selected_dark": "*checkbox_label_background_fill", "checkbox_label_border_color": "*border_color_primary", "checkbox_label_border_color_dark": "*border_color_primary", "checkbox_label_border_color_hover": "*checkbox_label_border_color", "checkbox_label_border_color_hover_dark": "*checkbox_label_border_color", "checkbox_label_border_width": "*input_border_width", "checkbox_label_border_width_dark": "*input_border_width", "checkbox_label_gap": "*spacing_lg", "checkbox_label_padding": "*spacing_md calc(2 * *spacing_md)", "checkbox_label_shadow": "none", "checkbox_label_text_color": "*body_text_color", "checkbox_label_text_color_dark": "*body_text_color", "checkbox_label_text_color_selected": "*checkbox_label_text_color", "checkbox_label_text_color_selected_dark": "*checkbox_label_text_color", "checkbox_label_text_size": "*text_md", "checkbox_label_text_weight": "400", "checkbox_shadow": "*input_shadow", "color_accent": "*primary_500", "color_accent_soft": "*primary_50", "color_accent_soft_dark": "*neutral_700", "container_radius": "*radius_lg", "embed_radius": "*radius_md", "error_background_fill": "#fee2e2", "error_background_fill_dark": "*background_fill_primary", "error_border_color": "#fecaca", "error_border_color_dark": "*border_color_primary", "error_border_width": "1px", "error_border_width_dark": "1px", "error_text_color": "#ef4444", "error_text_color_dark": "#ef4444", "font": "'Rubik', 'ui-sans-serif', 'system-ui', sans-serif", "font_mono": "'Inconsolata', 'ui-monospace', 'Consolas', monospace", "form_gap_width": "0px", "input_background_fill": "*neutral_100", "input_background_fill_dark": "*neutral_700", "input_background_fill_focus": "*secondary_500", "input_background_fill_focus_dark": "*secondary_600", "input_background_fill_hover": "*input_background_fill", "input_background_fill_hover_dark": "*input_background_fill", "input_border_color": "*border_color_primary", "input_border_color_dark": "*border_color_primary", "input_border_color_focus": "*secondary_300", "input_border_color_focus_dark": "*neutral_700", "input_border_color_hover": "*input_border_color", "input_border_color_hover_dark": "*input_border_color", "input_border_width": "0px", "input_border_width_dark": "0px", "input_padding": "*spacing_xl", "input_placeholder_color": "*neutral_400", "input_placeholder_color_dark": "*neutral_500", "input_radius": "*radius_lg", "input_shadow": "none", "input_shadow_dark": "none", "input_shadow_focus": "*input_shadow", "input_shadow_focus_dark": "*input_shadow", "input_text_size": "*text_md", "input_text_weight": "400", "layout_gap": "*spacing_xxl", "link_text_color": "*secondary_600", "link_text_color_active": "*secondary_600", "link_text_color_active_dark": "*secondary_500", "link_text_color_dark": "*secondary_500", "link_text_color_hover": "*secondary_700", "link_text_color_hover_dark": "*secondary_400", "link_text_color_visited": "*secondary_500", "link_text_color_visited_dark": "*secondary_600", "loader_color": "*color_accent", "loader_color_dark": "*color_accent", "name": "base", "neutral_100": "#f5f5f4", "neutral_200": "#e7e5e4", "neutral_300": "#d6d3d1", "neutral_400": "#a8a29e", "neutral_50": "#fafaf9", "neutral_500": "#78716c", "neutral_600": "#57534e", "neutral_700": "#44403c", "neutral_800": "#292524", "neutral_900": "#1c1917", "neutral_950": "#0f0e0d", "panel_background_fill": "*background_fill_secondary", "panel_background_fill_dark": "*background_fill_secondary", "panel_border_color": "*border_color_primary", "panel_border_color_dark": "*border_color_primary", "panel_border_width": "0", "panel_border_width_dark": "0", "primary_100": "#e0f2fe", "primary_200": "#bae6fd", "primary_300": "#7dd3fc", "primary_400": "#38bdf8", "primary_50": "#f0f9ff", "primary_500": "#0ea5e9", "primary_600": "#0284c7", "primary_700": "#0369a1", "primary_800": "#075985", "primary_900": "#0c4a6e", "primary_950": "#0b4165", "prose_header_text_weight": "500", "prose_text_size": "*text_md", "prose_text_weight": "400", "radio_circle": "url(\"data:image/svg+xml,%3csvg viewBox='0 0 16 16' fill='white' xmlns='http://www.w3.org/2000/svg'%3e%3ccircle cx='8' cy='8' r='3'/%3e%3c/svg%3e\")", "radius_lg": "3px", "radius_md": "3px", "radius_sm": "3px", "radius_xl": "3px", "radius_xs": "3px", "radius_xxl": "3px", "radius_xxs": "3px", "secondary_100": "#e0f2fe", "secondary_200": "#bae6fd", "secondary_300": "#7dd3fc", "secondary_400": "#38bdf8", "secondary_50": "#f0f9ff", "secondary_500": "#0ea5e9", "secondary_600": "#0284c7", "secondary_700": "#0369a1", "secondary_800": "#075985", "secondary_900": "#0c4a6e", "secondary_950": "#0b4165", "section_header_text_size": "*text_md", "section_header_text_weight": "400", "shadow_drop": "rgba(0,0,0,0.05) 0px 1px 2px 0px", "shadow_drop_lg": "0 1px 3px 0 rgb(0 0 0 / 0.1), 0 1px 2px -1px rgb(0 0 0 / 0.1)", "shadow_inset": "rgba(0,0,0,0.05) 0px 2px 4px 0px inset", "shadow_spread": "3px", "shadow_spread_dark": "1px", "slider_color": "*primary_600", "slider_color_dark": "*primary_600", "spacing_lg": "8px", "spacing_md": "6px", "spacing_sm": "4px", "spacing_xl": "10px", "spacing_xs": "2px", "spacing_xxl": "16px", "spacing_xxs": "1px", "stat_background_fill": "*primary_300", "stat_background_fill_dark": "*primary_500", "table_border_color": "*neutral_300", "table_border_color_dark": "*neutral_700", "table_even_background_fill": "white", "table_even_background_fill_dark": "*neutral_950", "table_odd_background_fill": "*neutral_50", "table_odd_background_fill_dark": "*neutral_900", "table_radius": "*radius_lg", "table_row_focus": "*color_accent_soft", "table_row_focus_dark": "*color_accent_soft", "text_lg": "20px", "text_md": "16px", "text_sm": "14px", "text_xl": "24px", "text_xs": "12px", "text_xxl": "28px", "text_xxs": "10px"}, "version": "0.0.1"}