layer_id
int64 0
223
| name
stringlengths 26
32
| D
float64 0.03
0.21
| M
int64 1.02k
4.1k
| N
int64 4.1k
14.3k
| Q
float64 1
4
| alpha
float64 2.55
30.7
| alpha_weighted
float64 -100.7
-4.79
| entropy
float64 1.07
1.57
| has_esd
bool 1
class | lambda_max
float32 0
0.01
| layer_type
stringclasses 1
value | log_alpha_norm
float64 -100.63
-4.73
| log_norm
float32 -1.66
-0.94
| log_spectral_norm
float32 -3.31
-1.88
| matrix_rank
int64 64
64
| norm
float32 0.02
0.11
| num_evals
int64 1.02k
4.1k
| num_pl_spikes
int64 5
64
| rank_loss
int64 960
4.03k
| rf
int64 1
1
| sigma
float64 0.35
9.03
| spectral_norm
float32 0
0.01
| stable_rank
float32 4.91
54.6
| status
stringclasses 1
value | sv_max
float64 0.02
0.11
| sv_min
float64 0
0
| warning
stringclasses 2
values | weak_rank_loss
int64 960
4.03k
| xmax
float64 0
0.01
| xmin
float64 0
0
|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
200
|
model.layers.28.self_attn.o_proj
| 0.097567
| 4,096
| 4,096
| 1
| 13.455432
| -37.866849
| 1.56443
| true
| 0.001534
|
dense
| -37.866766
| -1.429151
| -2.814243
| 64
| 0.037226
| 4,096
| 64
| 4,032
| 1
| 1.556929
| 0.001534
| 24.271242
|
success
| 0.039163
| 0
|
under-trained
| 4,032
| 0.001534
| 0.00053
|
201
|
model.layers.28.self_attn.q_proj
| 0.067804
| 4,096
| 4,096
| 1
| 8.690762
| -24.135214
| 1.561632
| true
| 0.001671
|
dense
| -24.132781
| -1.462055
| -2.777111
| 64
| 0.03451
| 4,096
| 64
| 4,032
| 1
| 0.961345
| 0.001671
| 20.656462
|
success
| 0.040874
| 0
|
under-trained
| 4,032
| 0.001671
| 0.000464
|
202
|
model.layers.28.self_attn.v_proj
| 0.076671
| 1,024
| 4,096
| 4
| 21.357595
| -69.448551
| 1.136971
| true
| 0.00056
|
dense
| -69.02072
| -1.538136
| -3.251703
| 64
| 0.028964
| 1,024
| 20
| 960
| 1
| 4.552097
| 0.00056
| 51.709057
|
success
| 0.023667
| 0.000001
|
under-trained
| 960
| 0.00056
| 0.000467
|
203
|
model.layers.29.mlp.down_proj
| 0.174697
| 4,096
| 14,336
| 3.5
| 3.982349
| -9.512021
| 1.560833
| true
| 0.004087
|
dense
| -9.366647
| -1.121441
| -2.388545
| 64
| 0.075607
| 4,096
| 5
| 4,032
| 1
| 1.333747
| 0.004087
| 18.497126
|
success
| 0.063933
| 0.000001
| 4,032
| 0.004087
| 0.001206
|
|
204
|
model.layers.29.mlp.gate_proj
| 0.110376
| 4,096
| 14,336
| 3.5
| 4.602726
| -10.848719
| 1.560916
| true
| 0.004395
|
dense
| -10.739705
| -1.061237
| -2.35702
| 64
| 0.086849
| 4,096
| 8
| 4,032
| 1
| 1.273756
| 0.004395
| 19.759821
|
success
| 0.066296
| 0.000001
| 4,032
| 0.004395
| 0.001395
|
|
205
|
model.layers.29.mlp.up_proj
| 0.117209
| 4,096
| 14,336
| 3.5
| 4.878604
| -11.948773
| 1.561445
| true
| 0.003555
|
dense
| -11.741224
| -1.090014
| -2.449219
| 64
| 0.081281
| 4,096
| 12
| 4,032
| 1
| 1.119657
| 0.003555
| 22.866827
|
success
| 0.05962
| 0.000001
| 4,032
| 0.003555
| 0.001235
|
|
206
|
model.layers.29.self_attn.k_proj
| 0.077428
| 1,024
| 4,096
| 4
| 6.71173
| -19.3982
| 1.127362
| true
| 0.001288
|
dense
| -19.169781
| -1.536191
| -2.890194
| 64
| 0.029094
| 1,024
| 64
| 960
| 1
| 0.713966
| 0.001288
| 22.594532
|
success
| 0.035884
| 0.000001
|
under-trained
| 960
| 0.001288
| 0.000369
|
207
|
model.layers.29.self_attn.o_proj
| 0.0927
| 4,096
| 4,096
| 1
| 11.705402
| -31.38998
| 1.561996
| true
| 0.002081
|
dense
| -31.389846
| -1.37154
| -2.681666
| 64
| 0.042507
| 4,096
| 64
| 4,032
| 1
| 1.338175
| 0.002081
| 20.423292
|
success
| 0.045621
| 0
|
under-trained
| 4,032
| 0.002081
| 0.000593
|
208
|
model.layers.29.self_attn.q_proj
| 0.06169
| 4,096
| 4,096
| 1
| 7.948959
| -21.362541
| 1.557784
| true
| 0.002054
|
dense
| -21.361654
| -1.463992
| -2.687464
| 64
| 0.034356
| 4,096
| 64
| 4,032
| 1
| 0.86862
| 0.002054
| 16.729059
|
success
| 0.045318
| 0
|
under-trained
| 4,032
| 0.002054
| 0.000451
|
209
|
model.layers.29.self_attn.v_proj
| 0.058828
| 1,024
| 4,096
| 4
| 24.171611
| -77.009869
| 1.136972
| true
| 0.000652
|
dense
| -76.959004
| -1.505554
| -3.185963
| 64
| 0.031221
| 1,024
| 17
| 960
| 1
| 5.619941
| 0.000652
| 47.908161
|
success
| 0.025528
| 0.000001
|
under-trained
| 960
| 0.000652
| 0.000508
|
210
|
model.layers.30.mlp.down_proj
| 0.132176
| 4,096
| 14,336
| 3.5
| 3.446907
| -8.061023
| 1.557716
| true
| 0.004585
|
dense
| -7.880584
| -1.125973
| -2.338625
| 64
| 0.074822
| 4,096
| 6
| 4,032
| 1
| 0.998946
| 0.004585
| 16.317465
|
success
| 0.067715
| 0.000001
| 4,032
| 0.004585
| 0.001158
|
|
211
|
model.layers.30.mlp.gate_proj
| 0.108602
| 4,096
| 14,336
| 3.5
| 5.523198
| -12.654264
| 1.559638
| true
| 0.005116
|
dense
| -12.633893
| -1.042727
| -2.291112
| 64
| 0.09063
| 4,096
| 12
| 4,032
| 1
| 1.305735
| 0.005116
| 17.716766
|
success
| 0.071523
| 0.000001
| 4,032
| 0.005116
| 0.001391
|
|
212
|
model.layers.30.mlp.up_proj
| 0.136689
| 4,096
| 14,336
| 3.5
| 4.606794
| -10.939204
| 1.5597
| true
| 0.004221
|
dense
| -10.797425
| -1.076881
| -2.374581
| 64
| 0.083776
| 4,096
| 10
| 4,032
| 1
| 1.140568
| 0.004221
| 19.847208
|
success
| 0.06497
| 0.000001
| 4,032
| 0.004221
| 0.001308
|
|
213
|
model.layers.30.self_attn.k_proj
| 0.06174
| 1,024
| 4,096
| 4
| 7.277368
| -20.806148
| 1.128495
| true
| 0.001383
|
dense
| -20.728674
| -1.524596
| -2.859021
| 64
| 0.029882
| 1,024
| 64
| 960
| 1
| 0.784671
| 0.001383
| 21.598597
|
success
| 0.037195
| 0.000001
|
under-trained
| 960
| 0.001383
| 0.000387
|
214
|
model.layers.30.self_attn.o_proj
| 0.127274
| 4,096
| 4,096
| 1
| 4.013937
| -10.695391
| 1.559696
| true
| 0.002165
|
dense
| -10.531604
| -1.390642
| -2.664564
| 64
| 0.040678
| 4,096
| 8
| 4,032
| 1
| 1.065588
| 0.002165
| 18.789783
|
success
| 0.046528
| 0
| 4,032
| 0.002165
| 0.000634
|
|
215
|
model.layers.30.self_attn.q_proj
| 0.078681
| 4,096
| 4,096
| 1
| 7.80563
| -20.244153
| 1.555531
| true
| 0.00255
|
dense
| -20.243469
| -1.409045
| -2.593532
| 64
| 0.03899
| 4,096
| 64
| 4,032
| 1
| 0.850704
| 0.00255
| 15.29279
|
success
| 0.050493
| 0
|
under-trained
| 4,032
| 0.00255
| 0.000507
|
216
|
model.layers.30.self_attn.v_proj
| 0.088427
| 1,024
| 4,096
| 4
| 22.691435
| -73.210394
| 1.137023
| true
| 0.000594
|
dense
| -72.865368
| -1.51168
| -3.226345
| 64
| 0.030784
| 1,024
| 19
| 960
| 1
| 4.976356
| 0.000594
| 51.839954
|
success
| 0.024368
| 0.000001
|
under-trained
| 960
| 0.000594
| 0.000497
|
217
|
model.layers.31.mlp.down_proj
| 0.211114
| 4,096
| 14,336
| 3.5
| 3.167784
| -7.184908
| 1.544066
| true
| 0.005394
|
dense
| -6.866488
| -1.098212
| -2.268118
| 64
| 0.079761
| 4,096
| 10
| 4,032
| 1
| 0.685513
| 0.005394
| 14.787909
|
success
| 0.073441
| 0.000001
| 4,032
| 0.005394
| 0.001204
|
|
218
|
model.layers.31.mlp.gate_proj
| 0.09052
| 4,096
| 14,336
| 3.5
| 4.625173
| -10.50815
| 1.559803
| true
| 0.005346
|
dense
| -10.436979
| -1.012104
| -2.271947
| 64
| 0.097252
| 4,096
| 10
| 4,032
| 1
| 1.146381
| 0.005346
| 18.190453
|
success
| 0.073118
| 0.000001
| 4,032
| 0.005346
| 0.001479
|
|
219
|
model.layers.31.mlp.up_proj
| 0.129257
| 4,096
| 14,336
| 3.5
| 5.00781
| -11.894489
| 1.561102
| true
| 0.004215
|
dense
| -11.763711
| -1.047329
| -2.375188
| 64
| 0.089675
| 4,096
| 11
| 4,032
| 1
| 1.2084
| 0.004215
| 21.274475
|
success
| 0.064924
| 0.000001
| 4,032
| 0.004215
| 0.001375
|
|
220
|
model.layers.31.self_attn.k_proj
| 0.036201
| 1,024
| 4,096
| 4
| 7.879704
| -23.809126
| 1.133314
| true
| 0.000952
|
dense
| -23.701685
| -1.557212
| -3.021576
| 64
| 0.02772
| 1,024
| 52
| 960
| 1
| 0.954043
| 0.000952
| 29.131598
|
success
| 0.030847
| 0.000001
|
under-trained
| 960
| 0.000952
| 0.000381
|
221
|
model.layers.31.self_attn.o_proj
| 0.144043
| 4,096
| 4,096
| 1
| 11.16667
| -28.305311
| 1.555512
| true
| 0.002919
|
dense
| -28.305302
| -1.367495
| -2.534803
| 64
| 0.042905
| 4,096
| 64
| 4,032
| 1
| 1.270834
| 0.002919
| 14.699698
|
success
| 0.054025
| 0
|
under-trained
| 4,032
| 0.002919
| 0.000587
|
222
|
model.layers.31.self_attn.q_proj
| 0.077791
| 4,096
| 4,096
| 1
| 8.190492
| -22.01782
| 1.559381
| true
| 0.00205
|
dense
| -22.015165
| -1.4183
| -2.688217
| 64
| 0.038168
| 4,096
| 64
| 4,032
| 1
| 0.898811
| 0.00205
| 18.617321
|
success
| 0.045278
| 0
|
under-trained
| 4,032
| 0.00205
| 0.000505
|
223
|
model.layers.31.self_attn.v_proj
| 0.074278
| 1,024
| 4,096
| 4
| 15.33425
| -48.471702
| 1.136779
| true
| 0.00069
|
dense
| -48.338322
| -1.51178
| -3.161009
| 64
| 0.030777
| 1,024
| 39
| 960
| 1
| 2.295317
| 0.00069
| 44.589176
|
success
| 0.026272
| 0.000001
|
under-trained
| 960
| 0.00069
| 0.000466
|
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