precompute_fast.log

2025-11-26 19:43:29,169 - INFO - ============================================================
2025-11-26 19:43:29,169 - INFO - FAST PRE-COMPUTATION STARTED
2025-11-26 19:43:29,169 - INFO - ============================================================
2025-11-26 19:43:29,169 - INFO - Sample size: 150,000
2025-11-26 19:43:29,169 - INFO - Output directory: precomputed_data
2025-11-26 19:43:29,169 - INFO - Version: v1
2025-11-26 19:43:29,169 - INFO - PCA pre-reduction: True (50 dims)
2025-11-26 19:43:29,169 - INFO - ============================================================
2025-11-26 19:43:29,169 - INFO - Step 1/5: Loading model data...
Repo card metadata block was not found. Setting CardData to empty.
2025-11-26 19:43:29,530 - WARNING - Repo card metadata block was not found. Setting CardData to empty.
2025-11-26 19:43:52,328 - INFO - Loaded 150,000 models in 23.2 seconds
2025-11-26 19:43:52,329 - INFO - Step 2/5: Generating embeddings...
2025-11-26 19:43:52,329 - INFO - Building combined text from model fields...
2025-11-26 19:43:52,878 - INFO - Use pytorch device_name: mps
2025-11-26 19:43:52,878 - INFO - Load pretrained SentenceTransformer: all-MiniLM-L6-v2

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Batches:  48%|████▊     | 279/586 [09:43<04:00,  1.28it/s]
Batches:  48%|████▊     | 280/586 [09:52<15:46,  3.09s/it]
Batches:  48%|████▊     | 281/586 [09:59<22:02,  4.34s/it]
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Batches:  48%|████▊     | 284/586 [10:02<11:05,  2.20s/it]
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Batches:  49%|████▉     | 286/586 [10:04<07:25,  1.49s/it]
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Batches:  51%|█████     | 296/586 [10:13<04:03,  1.19it/s]
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Batches:  51%|█████     | 298/586 [10:15<05:05,  1.06s/it]
Batches:  51%|█████     | 299/586 [10:16<04:34,  1.04it/s]
Batches:  51%|█████     | 300/586 [10:17<04:10,  1.14it/s]
Batches:  51%|█████▏    | 301/586 [10:17<03:50,  1.24it/s]
Batches:  52%|█████▏    | 302/586 [10:18<03:41,  1.28it/s]
Batches:  52%|█████▏    | 303/586 [10:19<03:36,  1.31it/s]
Batches:  52%|█████▏    | 304/586 [10:20<03:54,  1.20it/s]
Batches:  52%|█████▏    | 305/586 [10:20<03:34,  1.31it/s]
Batches:  52%|█████▏    | 306/586 [10:22<04:19,  1.08it/s]
Batches:  52%|█████▏    | 307/586 [10:23<04:26,  1.05it/s]
Batches:  53%|█████▎    | 308/586 [10:24<04:16,  1.08it/s]
Batches:  53%|█████▎    | 309/586 [10:25<04:32,  1.02it/s]
Batches:  53%|█████▎    | 310/586 [10:26<04:18,  1.07it/s]
Batches:  53%|█████▎    | 311/586 [10:30<09:41,  2.12s/it]
Batches:  53%|█████▎    | 312/586 [10:32<08:54,  1.95s/it]
Batches:  53%|█████▎    | 313/586 [10:35<09:47,  2.15s/it]
Batches:  54%|█████▎    | 314/586 [10:36<09:15,  2.04s/it]
Batches:  54%|█████▍    | 315/586 [10:37<07:30,  1.66s/it]
Batches:  54%|█████▍    | 316/586 [10:38<06:26,  1.43s/it]
Batches:  54%|█████▍    | 317/586 [10:39<05:38,  1.26s/it]
Batches:  54%|█████▍    | 318/586 [10:41<06:34,  1.47s/it]
Batches:  54%|█████▍    | 319/586 [10:56<25:00,  5.62s/it]
Batches:  55%|█████▍    | 320/586 [10:58<19:20,  4.36s/it]
Batches:  55%|█████▍    | 321/586 [11:09<28:20,  6.42s/it]
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Batches:  55%|█████▌    | 323/586 [11:11<16:37,  3.79s/it]
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Batches:  56%|█████▌    | 327/586 [11:18<09:57,  2.31s/it]
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Batches:  56%|█████▌    | 329/586 [11:20<06:57,  1.63s/it]
Batches:  56%|█████▋    | 330/586 [11:21<06:03,  1.42s/it]
Batches:  56%|█████▋    | 331/586 [11:23<06:26,  1.52s/it]
Batches:  57%|█████▋    | 332/586 [11:24<05:33,  1.31s/it]
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Batches:  57%|█████▋    | 335/586 [11:27<05:09,  1.23s/it]
Batches:  57%|█████▋    | 336/586 [11:28<04:37,  1.11s/it]
Batches:  58%|█████▊    | 337/586 [11:30<04:57,  1.19s/it]
Batches:  58%|█████▊    | 338/586 [11:31<05:15,  1.27s/it]
Batches:  58%|█████▊    | 339/586 [11:32<04:46,  1.16s/it]
Batches:  58%|█████▊    | 340/586 [11:33<04:40,  1.14s/it]
Batches:  58%|█████▊    | 341/586 [11:35<05:42,  1.40s/it]
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Batches:  59%|█████▊    | 343/586 [11:37<04:41,  1.16s/it]
Batches:  59%|█████▊    | 344/586 [11:38<04:13,  1.05s/it]
Batches:  59%|█████▉    | 345/586 [11:39<04:19,  1.08s/it]
Batches:  59%|█████▉    | 346/586 [11:40<04:28,  1.12s/it]
Batches:  59%|█████▉    | 347/586 [11:41<03:47,  1.05it/s]
Batches:  59%|█████▉    | 348/586 [11:41<03:37,  1.09it/s]
Batches:  60%|█████▉    | 349/586 [11:43<04:15,  1.08s/it]
Batches:  60%|█████▉    | 350/586 [11:44<04:07,  1.05s/it]
Batches:  60%|█████▉    | 351/586 [11:45<03:53,  1.00it/s]
Batches:  60%|██████    | 352/586 [11:45<03:07,  1.25it/s]
Batches:  60%|██████    | 353/586 [11:46<02:41,  1.45it/s]
Batches:  60%|██████    | 354/586 [11:47<03:37,  1.07it/s]
Batches:  61%|██████    | 355/586 [11:48<03:29,  1.10it/s]
Batches:  61%|██████    | 356/586 [11:51<05:31,  1.44s/it]
Batches:  61%|██████    | 357/586 [11:56<10:20,  2.71s/it]
Batches:  61%|██████    | 358/586 [11:58<09:00,  2.37s/it]
Batches:  61%|██████▏   | 359/586 [11:59<06:59,  1.85s/it]
Batches:  61%|██████▏   | 360/586 [11:59<05:37,  1.49s/it]
Batches:  62%|██████▏   | 361/586 [12:00<04:35,  1.22s/it]
Batches:  62%|██████▏   | 362/586 [12:01<04:01,  1.08s/it]
Batches:  62%|██████▏   | 363/586 [12:01<03:32,  1.05it/s]
Batches:  62%|██████▏   | 364/586 [12:02<03:04,  1.20it/s]
Batches:  62%|██████▏   | 365/586 [12:02<02:35,  1.42it/s]
Batches:  62%|██████▏   | 366/586 [12:03<02:19,  1.58it/s]
Batches:  63%|██████▎   | 367/586 [12:04<02:47,  1.31it/s]
Batches:  63%|██████▎   | 368/586 [12:04<02:24,  1.51it/s]
Batches:  63%|██████▎   | 369/586 [12:05<02:36,  1.38it/s]
Batches:  63%|██████▎   | 370/586 [12:06<02:55,  1.23it/s]
Batches:  63%|██████▎   | 371/586 [12:06<02:34,  1.39it/s]
Batches:  63%|██████▎   | 372/586 [12:07<02:12,  1.61it/s]
Batches:  64%|██████▎   | 373/586 [12:08<02:17,  1.55it/s]
Batches:  64%|██████▍   | 374/586 [12:08<02:20,  1.51it/s]
Batches:  64%|██████▍   | 375/586 [12:09<02:29,  1.41it/s]
Batches:  64%|██████▍   | 376/586 [12:10<02:14,  1.57it/s]
Batches:  64%|██████▍   | 377/586 [12:12<04:11,  1.20s/it]
Batches:  65%|██████▍   | 378/586 [12:12<03:08,  1.10it/s]
Batches:  65%|██████▍   | 379/586 [12:13<02:57,  1.17it/s]
Batches:  65%|██████▍   | 380/586 [12:14<03:07,  1.10it/s]
Batches:  65%|██████▌   | 381/586 [12:15<02:56,  1.16it/s]
Batches:  65%|██████▌   | 382/586 [12:15<02:26,  1.39it/s]
Batches:  65%|██████▌   | 383/586 [12:16<02:37,  1.29it/s]
Batches:  66%|██████▌   | 384/586 [12:17<02:28,  1.36it/s]
Batches:  66%|██████▌   | 385/586 [12:17<02:28,  1.35it/s]
Batches:  66%|██████▌   | 386/586 [12:18<02:21,  1.41it/s]
Batches:  66%|██████▌   | 387/586 [12:18<01:56,  1.71it/s]
Batches:  66%|██████▌   | 388/586 [12:19<01:39,  1.99it/s]
Batches:  66%|██████▋   | 389/586 [12:19<01:33,  2.11it/s]
Batches:  67%|██████▋   | 390/586 [12:20<01:31,  2.15it/s]
Batches:  67%|██████▋   | 391/586 [12:20<01:49,  1.78it/s]
Batches:  67%|██████▋   | 392/586 [12:21<01:36,  2.00it/s]
Batches:  67%|██████▋   | 393/586 [12:21<01:21,  2.36it/s]
Batches:  67%|██████▋   | 394/586 [12:21<01:18,  2.46it/s]
Batches:  67%|██████▋   | 395/586 [12:22<01:04,  2.96it/s]
Batches:  68%|██████▊   | 396/586 [12:22<01:17,  2.44it/s]
Batches:  68%|██████▊   | 397/586 [12:22<01:09,  2.72it/s]
Batches:  68%|██████▊   | 398/586 [12:23<01:40,  1.86it/s]
Batches:  68%|██████▊   | 399/586 [12:24<01:27,  2.14it/s]
Batches:  68%|██████▊   | 400/586 [12:24<01:34,  1.97it/s]
Batches:  68%|██████▊   | 401/586 [12:25<01:35,  1.93it/s]
Batches:  69%|██████▊   | 402/586 [12:25<01:27,  2.09it/s]
Batches:  69%|██████▉   | 403/586 [12:26<01:32,  1.98it/s]
Batches:  69%|██████▉   | 404/586 [12:26<01:43,  1.75it/s]
Batches:  69%|██████▉   | 405/586 [12:27<01:29,  2.02it/s]
Batches:  69%|██████▉   | 406/586 [12:27<01:35,  1.88it/s]
Batches:  69%|██████▉   | 407/586 [12:28<01:21,  2.19it/s]
Batches:  70%|██████▉   | 408/586 [12:28<01:07,  2.62it/s]
Batches:  70%|██████▉   | 409/586 [12:28<01:01,  2.89it/s]
Batches:  70%|██████▉   | 410/586 [12:28<00:53,  3.27it/s]
Batches:  70%|███████   | 411/586 [12:29<00:53,  3.25it/s]
Batches:  70%|███████   | 412/586 [12:36<07:13,  2.49s/it]
Batches:  70%|███████   | 413/586 [12:36<05:11,  1.80s/it]
Batches:  71%|███████   | 414/586 [12:37<03:45,  1.31s/it]
Batches:  71%|███████   | 415/586 [12:37<02:46,  1.03it/s]
Batches:  71%|███████   | 416/586 [12:37<02:08,  1.32it/s]
Batches:  71%|███████   | 417/586 [12:37<01:47,  1.58it/s]
Batches:  71%|███████▏  | 418/586 [12:38<01:34,  1.77it/s]
Batches:  72%|███████▏  | 419/586 [12:38<01:20,  2.07it/s]
Batches:  72%|███████▏  | 420/586 [12:38<01:10,  2.36it/s]
Batches:  72%|███████▏  | 421/586 [12:39<01:04,  2.57it/s]
Batches:  72%|███████▏  | 422/586 [12:39<00:59,  2.74it/s]
Batches:  72%|███████▏  | 423/586 [12:39<00:57,  2.86it/s]
Batches:  72%|███████▏  | 424/586 [12:40<01:10,  2.29it/s]
Batches:  73%|███████▎  | 425/586 [12:40<01:02,  2.59it/s]
Batches:  73%|███████▎  | 426/586 [12:40<00:52,  3.06it/s]
Batches:  73%|███████▎  | 427/586 [12:41<00:50,  3.13it/s]
Batches:  73%|███████▎  | 428/586 [12:41<00:48,  3.25it/s]
Batches:  73%|███████▎  | 429/586 [12:41<00:44,  3.54it/s]
Batches:  73%|███████▎  | 430/586 [12:41<00:41,  3.77it/s]
Batches:  74%|███████▎  | 431/586 [12:42<00:42,  3.62it/s]
Batches:  74%|███████▎  | 432/586 [12:42<00:39,  3.89it/s]
Batches:  74%|███████▍  | 433/586 [12:42<00:52,  2.90it/s]
Batches:  74%|███████▍  | 434/586 [12:43<01:00,  2.50it/s]
Batches:  74%|███████▍  | 435/586 [12:43<01:03,  2.36it/s]
Batches:  74%|███████▍  | 436/586 [12:44<00:55,  2.72it/s]
Batches:  75%|███████▍  | 437/586 [12:44<00:48,  3.04it/s]
Batches:  75%|███████▍  | 438/586 [12:44<00:44,  3.33it/s]
Batches:  75%|███████▍  | 439/586 [12:44<00:37,  3.91it/s]
Batches:  75%|███████▌  | 440/586 [12:45<00:37,  3.90it/s]
Batches:  75%|███████▌  | 441/586 [12:45<00:36,  4.02it/s]
Batches:  75%|███████▌  | 442/586 [12:45<00:34,  4.16it/s]
Batches:  76%|███████▌  | 443/586 [12:46<00:55,  2.59it/s]
Batches:  76%|███████▌  | 444/586 [12:46<00:47,  2.99it/s]
Batches:  76%|███████▌  | 445/586 [12:46<00:47,  3.00it/s]
Batches:  76%|███████▌  | 446/586 [12:47<00:52,  2.67it/s]
Batches:  76%|███████▋  | 447/586 [12:47<00:56,  2.44it/s]
Batches:  76%|███████▋  | 448/586 [12:48<01:03,  2.18it/s]
Batches:  77%|███████▋  | 449/586 [12:48<01:06,  2.06it/s]
Batches:  77%|███████▋  | 450/586 [12:49<01:02,  2.18it/s]
Batches:  77%|███████▋  | 451/586 [12:55<05:06,  2.27s/it]
Batches:  77%|███████▋  | 452/586 [12:56<03:53,  1.74s/it]
Batches:  77%|███████▋  | 453/586 [12:56<02:51,  1.29s/it]
Batches:  77%|███████▋  | 454/586 [12:56<02:04,  1.06it/s]
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2025-11-26 19:57:31,904 - INFO - Generated embeddings: (150000, 384) in 13.7 minutes
2025-11-26 19:57:31,906 - INFO - Step 2.5/5: PCA reduction (384 -> 50 dims)...
/Users/hamidaho/hf_viz/venv/lib/python3.13/site-packages/sklearn/decomposition/_pca.py:604: RuntimeWarning: divide by zero encountered in matmul
  C = X.T @ X
/Users/hamidaho/hf_viz/venv/lib/python3.13/site-packages/sklearn/decomposition/_pca.py:604: RuntimeWarning: overflow encountered in matmul
  C = X.T @ X
/Users/hamidaho/hf_viz/venv/lib/python3.13/site-packages/sklearn/decomposition/_pca.py:604: RuntimeWarning: invalid value encountered in matmul
  C = X.T @ X
/Users/hamidaho/hf_viz/venv/lib/python3.13/site-packages/sklearn/decomposition/_base.py:148: RuntimeWarning: divide by zero encountered in matmul
  X_transformed = X @ self.components_.T
/Users/hamidaho/hf_viz/venv/lib/python3.13/site-packages/sklearn/decomposition/_base.py:148: RuntimeWarning: overflow encountered in matmul
  X_transformed = X @ self.components_.T
/Users/hamidaho/hf_viz/venv/lib/python3.13/site-packages/sklearn/decomposition/_base.py:148: RuntimeWarning: invalid value encountered in matmul
  X_transformed = X @ self.components_.T
/Users/hamidaho/hf_viz/venv/lib/python3.13/site-packages/sklearn/decomposition/_base.py:155: RuntimeWarning: divide by zero encountered in matmul
  X_transformed -= xp.reshape(self.mean_, (1, -1)) @ self.components_.T
/Users/hamidaho/hf_viz/venv/lib/python3.13/site-packages/sklearn/decomposition/_base.py:155: RuntimeWarning: overflow encountered in matmul
  X_transformed -= xp.reshape(self.mean_, (1, -1)) @ self.components_.T
/Users/hamidaho/hf_viz/venv/lib/python3.13/site-packages/sklearn/decomposition/_base.py:155: RuntimeWarning: invalid value encountered in matmul
  X_transformed -= xp.reshape(self.mean_, (1, -1)) @ self.components_.T
2025-11-26 19:57:32,249 - INFO - PCA complete in 0.3s (preserved 93.3% variance)
2025-11-26 19:57:32,250 - INFO - Reduced embeddings: (150000, 50)
2025-11-26 19:57:32,250 - INFO - Step 3/5: Running OPTIMIZED UMAP for 3D coordinates...
UMAP(low_memory=False, n_components=3, spread=1.5, verbose=True)
Wed Nov 26 19:57:32 2025 Construct fuzzy simplicial set
Wed Nov 26 19:57:32 2025 Finding Nearest Neighbors
Wed Nov 26 19:57:32 2025 Building RP forest with 24 trees
Wed Nov 26 19:57:43 2025 NN descent for 17 iterations
	 1  /  17
	 2  /  17
	 3  /  17
	 4  /  17
	Stopping threshold met -- exiting after 4 iterations
Wed Nov 26 19:58:03 2025 Finished Nearest Neighbor Search
Wed Nov 26 19:58:07 2025 Construct embedding

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2025-11-26 20:06:05,835 - INFO - Generated 3D coordinates: (150000, 3) in 8.6 minutes
2025-11-26 20:06:05,856 - INFO - Step 4/5: Running OPTIMIZED UMAP for 2D coordinates...
/Users/hamidaho/hf_viz/venv/lib/python3.13/site-packages/umap/spectral.py:548: UserWarning: Spectral initialisation failed! The eigenvector solver
failed. This is likely due to too small an eigengap. Consider
adding some noise or jitter to your data.

Falling back to random initialisation!
  warn(
/Users/hamidaho/hf_viz/venv/lib/python3.13/site-packages/umap/spectral.py:548: UserWarning: Spectral initialisation failed! The eigenvector solver
failed. This is likely due to too small an eigengap. Consider
adding some noise or jitter to your data.

Falling back to random initialisation!
  warn(
/Users/hamidaho/hf_viz/venv/lib/python3.13/site-packages/umap/spectral.py:548: UserWarning: Spectral initialisation failed! The eigenvector solver
failed. This is likely due to too small an eigengap. Consider
adding some noise or jitter to your data.

Falling back to random initialisation!
  warn(
/Users/hamidaho/hf_viz/venv/lib/python3.13/site-packages/umap/spectral.py:548: UserWarning: Spectral initialisation failed! The eigenvector solver
failed. This is likely due to too small an eigengap. Consider
adding some noise or jitter to your data.

Falling back to random initialisation!
  warn(
/Users/hamidaho/hf_viz/venv/lib/python3.13/site-packages/umap/spectral.py:548: UserWarning: Spectral initialisation failed! The eigenvector solver
failed. This is likely due to too small an eigengap. Consider
adding some noise or jitter to your data.

Falling back to random initialisation!
  warn(
/Users/hamidaho/hf_viz/venv/lib/python3.13/site-packages/umap/spectral.py:548: UserWarning: Spectral initialisation failed! The eigenvector solver
failed. This is likely due to too small an eigengap. Consider
adding some noise or jitter to your data.

Falling back to random initialisation!
  warn(
/Users/hamidaho/hf_viz/venv/lib/python3.13/site-packages/umap/spectral.py:548: UserWarning: Spectral initialisation failed! The eigenvector solver
failed. This is likely due to too small an eigengap. Consider
adding some noise or jitter to your data.

Falling back to random initialisation!
  warn(
/Users/hamidaho/hf_viz/venv/lib/python3.13/site-packages/umap/spectral.py:548: UserWarning: Spectral initialisation failed! The eigenvector solver
failed. This is likely due to too small an eigengap. Consider
adding some noise or jitter to your data.

Falling back to random initialisation!
  warn(
/Users/hamidaho/hf_viz/venv/lib/python3.13/site-packages/umap/spectral.py:548: UserWarning: Spectral initialisation failed! The eigenvector solver
failed. This is likely due to too small an eigengap. Consider
adding some noise or jitter to your data.

Falling back to random initialisation!
  warn(
/Users/hamidaho/hf_viz/venv/lib/python3.13/site-packages/umap/spectral.py:548: UserWarning: Spectral initialisation failed! The eigenvector solver
failed. This is likely due to too small an eigengap. Consider
adding some noise or jitter to your data.

Falling back to random initialisation!
  warn(
/Users/hamidaho/hf_viz/venv/lib/python3.13/site-packages/umap/spectral.py:548: UserWarning: Spectral initialisation failed! The eigenvector solver
failed. This is likely due to too small an eigengap. Consider
adding some noise or jitter to your data.

Falling back to random initialisation!
  warn(
/Users/hamidaho/hf_viz/venv/lib/python3.13/site-packages/umap/spectral.py:548: UserWarning: Spectral initialisation failed! The eigenvector solver
failed. This is likely due to too small an eigengap. Consider
adding some noise or jitter to your data.

Falling back to random initialisation!
  warn(
/Users/hamidaho/hf_viz/venv/lib/python3.13/site-packages/umap/spectral.py:548: UserWarning: Spectral initialisation failed! The eigenvector solver
failed. This is likely due to too small an eigengap. Consider
adding some noise or jitter to your data.

Falling back to random initialisation!
  warn(
/Users/hamidaho/hf_viz/venv/lib/python3.13/site-packages/umap/spectral.py:548: UserWarning: Spectral initialisation failed! The eigenvector solver
failed. This is likely due to too small an eigengap. Consider
adding some noise or jitter to your data.

Falling back to random initialisation!
  warn(
/Users/hamidaho/hf_viz/venv/lib/python3.13/site-packages/umap/spectral.py:548: UserWarning: Spectral initialisation failed! The eigenvector solver
failed. This is likely due to too small an eigengap. Consider
adding some noise or jitter to your data.

Falling back to random initialisation!
  warn(
	completed  0  /  200 epochs
	completed  20  /  200 epochs
	completed  40  /  200 epochs
	completed  60  /  200 epochs
	completed  80  /  200 epochs
	completed  100  /  200 epochs
	completed  120  /  200 epochs
	completed  140  /  200 epochs
	completed  160  /  200 epochs
	completed  180  /  200 epochs
Wed Nov 26 20:06:05 2025 Finished embedding
UMAP(low_memory=False, spread=1.5, verbose=True)
Wed Nov 26 20:06:06 2025 Construct fuzzy simplicial set
Wed Nov 26 20:06:06 2025 Finding Nearest Neighbors
Wed Nov 26 20:06:06 2025 Building RP forest with 24 trees
Wed Nov 26 20:06:08 2025 NN descent for 17 iterations
	 1  /  17
	 2  /  17
	 3  /  17
	 4  /  17
	Stopping threshold met -- exiting after 4 iterations
Wed Nov 26 20:06:21 2025 Finished Nearest Neighbor Search
Wed Nov 26 20:06:22 2025 Construct embedding

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2025-11-26 20:18:33,898 - INFO - Generated 2D coordinates: (150000, 2) in 12.5 minutes
2025-11-26 20:18:33,902 - INFO - Step 5/5: Saving to Parquet files...
2025-11-26 20:18:35,549 - INFO - Saved models data: precomputed_data/models_v1.parquet (22.1 MB)
2025-11-26 20:18:35,551 - ERROR - Pre-computation failed: 'modelId'
Traceback (most recent call last):
  File "/Users/hamidaho/hf_viz/venv/lib/python3.13/site-packages/pandas/core/indexes/base.py", line 3812, in get_loc
    return self._engine.get_loc(casted_key)
           ~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^
  File "pandas/_libs/index.pyx", line 167, in pandas._libs.index.IndexEngine.get_loc
  File "pandas/_libs/index.pyx", line 196, in pandas._libs.index.IndexEngine.get_loc
  File "pandas/_libs/hashtable_class_helper.pxi", line 7088, in pandas._libs.hashtable.PyObjectHashTable.get_item
  File "pandas/_libs/hashtable_class_helper.pxi", line 7096, in pandas._libs.hashtable.PyObjectHashTable.get_item
KeyError: 'modelId'

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/Users/hamidaho/hf_viz/backend/scripts/precompute_fast.py", line 266, in <module>
    precompute_fast(
    ~~~~~~~~~~~~~~~^
        sample_size=args.sample_size,
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
    ...<3 lines>...
        use_pca=not args.no_pca
        ^^^^^^^^^^^^^^^^^^^^^^^
    )
    ^
  File "/Users/hamidaho/hf_viz/backend/scripts/precompute_fast.py", line 187, in precompute_fast
    'model_id': df['modelId'].values,
                ~~^^^^^^^^^^^
  File "/Users/hamidaho/hf_viz/venv/lib/python3.13/site-packages/pandas/core/frame.py", line 4113, in __getitem__
    indexer = self.columns.get_loc(key)
  File "/Users/hamidaho/hf_viz/venv/lib/python3.13/site-packages/pandas/core/indexes/base.py", line 3819, in get_loc
    raise KeyError(key) from err
KeyError: 'modelId'
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Wed Nov 26 20:18:33 2025 Finished embedding