layer_id
int64 0
223
| name
stringlengths 26
32
| D
float64 0.02
0.18
| M
int64 1.02k
4.1k
| N
int64 4.1k
14.3k
| Q
float64 1
4
| alpha
float64 3.57
40.4
| alpha_weighted
float64 -123.19
-7.38
| entropy
float64 1.1
1.57
| has_esd
bool 1
class | lambda_max
float32 0
0.01
| layer_type
stringclasses 1
value | log_alpha_norm
float64 -123.03
-6.97
| log_norm
float32 -1.93
-0.76
| log_spectral_norm
float32 -3.35
-2
| matrix_rank
int64 64
64
| norm
float32 0.01
0.17
| num_evals
int64 1.02k
4.1k
| num_pl_spikes
int64 7
64
| rank_loss
int64 960
4.03k
| rf
int64 1
1
| sigma
float64 0.59
11.9
| spectral_norm
float32 0
0.01
| stable_rank
float32 14
56.5
| status
stringclasses 1
value | sv_max
float64 0.02
0.1
| 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
| 0.097805
| 4,096
| 4,096
| 1
| 13.500109
| -36.692384
| 1.566192
| true
| 0.001915
|
dense
| -36.688367
| -1.222852
| -2.717932
| 64
| 0.059862
| 4,096
| 64
| 4,032
| 1
| 1.562514
| 0.001915
| 31.266609
|
success
| 0.043756
| 0
|
under-trained
| 4,032
| 0.001915
| 0.000857
|
201
|
model.layers.28.self_attn.q_proj
| 0.032679
| 4,096
| 4,096
| 1
| 8.820552
| -23.808232
| 1.564349
| true
| 0.001999
|
dense
| -23.786964
| -1.270111
| -2.699177
| 64
| 0.05369
| 4,096
| 63
| 4,032
| 1
| 0.985297
| 0.001999
| 26.857561
|
success
| 0.044711
| 0
|
under-trained
| 4,032
| 0.001999
| 0.000729
|
202
|
model.layers.28.self_attn.v_proj
| 0.05884
| 1,024
| 4,096
| 4
| 26.562079
| -80.719735
| 1.137033
| true
| 0.000914
|
dense
| -80.605992
| -1.336471
| -3.038909
| 64
| 0.046082
| 1,024
| 15
| 960
| 1
| 6.600101
| 0.000914
| 50.400822
|
success
| 0.030237
| 0.000001
|
under-trained
| 960
| 0.000914
| 0.000756
|
203
|
model.layers.29.mlp.down_proj
| 0.116928
| 4,096
| 14,336
| 3.5
| 5.635298
| -12.798872
| 1.563257
| true
| 0.005356
|
dense
| -12.7392
| -0.926453
| -2.271197
| 64
| 0.118453
| 4,096
| 8
| 4,032
| 1
| 1.638825
| 0.005356
| 22.117905
|
success
| 0.073182
| 0.000001
| 4,032
| 0.005356
| 0.001893
|
|
204
|
model.layers.29.mlp.gate_proj
| 0.095314
| 4,096
| 14,336
| 3.5
| 5.709415
| -11.900639
| 1.558272
| true
| 0.008234
|
dense
| -11.889177
| -0.86286
| -2.084388
| 64
| 0.137132
| 4,096
| 14
| 4,032
| 1
| 1.258644
| 0.008234
| 16.654388
|
success
| 0.090741
| 0.000001
| 4,032
| 0.008234
| 0.002101
|
|
205
|
model.layers.29.mlp.up_proj
| 0.089241
| 4,096
| 14,336
| 3.5
| 5.499295
| -12.090405
| 1.560934
| true
| 0.006331
|
dense
| -12.041353
| -0.894798
| -2.198537
| 64
| 0.12741
| 4,096
| 15
| 4,032
| 1
| 1.161713
| 0.006331
| 20.125153
|
success
| 0.079567
| 0.000001
| 4,032
| 0.006331
| 0.001928
|
|
206
|
model.layers.29.self_attn.k_proj
| 0.047418
| 1,024
| 4,096
| 4
| 8.436308
| -25.361052
| 1.135113
| true
| 0.000986
|
dense
| -24.77831
| -1.396343
| -3.006179
| 64
| 0.040147
| 1,024
| 57
| 960
| 1
| 0.984963
| 0.000986
| 40.722656
|
success
| 0.031399
| 0.000001
|
under-trained
| 960
| 0.000986
| 0.000552
|
207
|
model.layers.29.self_attn.o_proj
| 0.112862
| 4,096
| 4,096
| 1
| 12.550207
| -31.941633
| 1.563841
| true
| 0.00285
|
dense
| -31.941213
| -1.169205
| -2.545108
| 64
| 0.067732
| 4,096
| 64
| 4,032
| 1
| 1.443776
| 0.00285
| 23.763067
|
success
| 0.053388
| 0
|
under-trained
| 4,032
| 0.00285
| 0.000957
|
208
|
model.layers.29.self_attn.q_proj
| 0.031023
| 4,096
| 4,096
| 1
| 8.850884
| -24.35266
| 1.564856
| true
| 0.001772
|
dense
| -24.327975
| -1.301656
| -2.751438
| 64
| 0.049928
| 4,096
| 61
| 4,032
| 1
| 1.005203
| 0.001772
| 28.169666
|
success
| 0.0421
| 0
|
under-trained
| 4,032
| 0.001772
| 0.000683
|
209
|
model.layers.29.self_attn.v_proj
| 0.082863
| 1,024
| 4,096
| 4
| 25.604042
| -76.527974
| 1.137019
| true
| 0.001026
|
dense
| -76.441977
| -1.29374
| -2.988902
| 64
| 0.050846
| 1,024
| 16
| 960
| 1
| 6.15101
| 0.001026
| 49.56353
|
success
| 0.032029
| 0.000001
|
under-trained
| 960
| 0.001026
| 0.000831
|
210
|
model.layers.30.mlp.down_proj
| 0.121525
| 4,096
| 14,336
| 3.5
| 4.095597
| -8.869663
| 1.558633
| true
| 0.006829
|
dense
| -8.754653
| -0.922824
| -2.165658
| 64
| 0.119447
| 4,096
| 7
| 4,032
| 1
| 1.170026
| 0.006829
| 17.491791
|
success
| 0.082636
| 0.000001
| 4,032
| 0.006829
| 0.00197
|
|
211
|
model.layers.30.mlp.gate_proj
| 0.095954
| 4,096
| 14,336
| 3.5
| 5.390292
| -10.79068
| 1.555076
| true
| 0.009957
|
dense
| -10.781551
| -0.834772
| -2.001873
| 64
| 0.146295
| 4,096
| 16
| 4,032
| 1
| 1.097573
| 0.009957
| 14.692684
|
success
| 0.099785
| 0.000001
| 4,032
| 0.009957
| 0.002176
|
|
212
|
model.layers.30.mlp.up_proj
| 0.094755
| 4,096
| 14,336
| 3.5
| 4.092898
| -8.543034
| 1.556608
| true
| 0.008179
|
dense
| -8.448714
| -0.872864
| -2.087282
| 64
| 0.13401
| 4,096
| 9
| 4,032
| 1
| 1.030966
| 0.008179
| 16.383945
|
success
| 0.09044
| 0.000001
| 4,032
| 0.008179
| 0.002164
|
|
213
|
model.layers.30.self_attn.k_proj
| 0.033068
| 1,024
| 4,096
| 4
| 8.648706
| -25.553527
| 1.134789
| true
| 0.00111
|
dense
| -25.127229
| -1.372152
| -2.954607
| 64
| 0.042447
| 1,024
| 41
| 960
| 1
| 1.194527
| 0.00111
| 38.234444
|
success
| 0.033319
| 0.000001
|
under-trained
| 960
| 0.00111
| 0.000615
|
214
|
model.layers.30.self_attn.o_proj
| 0.148381
| 4,096
| 4,096
| 1
| 3.666627
| -9.161233
| 1.558121
| true
| 0.003173
|
dense
| -8.763318
| -1.17302
| -2.498545
| 64
| 0.06714
| 4,096
| 9
| 4,032
| 1
| 0.888876
| 0.003173
| 21.160452
|
success
| 0.056328
| 0
| 4,032
| 0.003173
| 0.001034
|
|
215
|
model.layers.30.self_attn.q_proj
| 0.028241
| 4,096
| 4,096
| 1
| 8.020482
| -20.355868
| 1.561349
| true
| 0.002897
|
dense
| -20.352326
| -1.227622
| -2.537986
| 64
| 0.059208
| 4,096
| 63
| 4,032
| 1
| 0.884498
| 0.002897
| 20.434505
|
success
| 0.053828
| 0
|
under-trained
| 4,032
| 0.002897
| 0.000786
|
216
|
model.layers.30.self_attn.v_proj
| 0.09179
| 1,024
| 4,096
| 4
| 32.460629
| -97.711113
| 1.137061
| true
| 0.000977
|
dense
| -97.642366
| -1.301774
| -3.010142
| 64
| 0.049914
| 1,024
| 10
| 960
| 1
| 9.948724
| 0.000977
| 51.093761
|
success
| 0.031256
| 0.000001
|
under-trained
| 960
| 0.000977
| 0.000839
|
217
|
model.layers.31.mlp.down_proj
| 0.125934
| 4,096
| 14,336
| 3.5
| 3.571153
| -7.375295
| 1.537734
| true
| 0.008605
|
dense
| -6.966406
| -0.87753
| -2.065242
| 64
| 0.132578
| 4,096
| 19
| 4,032
| 1
| 0.589863
| 0.008605
| 15.406788
|
success
| 0.092764
| 0.000001
| 4,032
| 0.008605
| 0.001777
|
|
218
|
model.layers.31.mlp.gate_proj
| 0.091779
| 4,096
| 14,336
| 3.5
| 4.415988
| -8.811718
| 1.555982
| true
| 0.010106
|
dense
| -8.763337
| -0.804311
| -1.995413
| 64
| 0.156924
| 4,096
| 10
| 4,032
| 1
| 1.08023
| 0.010106
| 15.527523
|
success
| 0.10053
| 0.000001
| 4,032
| 0.010106
| 0.002479
|
|
219
|
model.layers.31.mlp.up_proj
| 0.086961
| 4,096
| 14,336
| 3.5
| 4.211193
| -8.774598
| 1.557385
| true
| 0.008248
|
dense
| -8.662626
| -0.843593
| -2.083637
| 64
| 0.143353
| 4,096
| 10
| 4,032
| 1
| 1.015468
| 0.008248
| 17.379765
|
success
| 0.09082
| 0.000001
| 4,032
| 0.008248
| 0.00225
|
|
220
|
model.layers.31.self_attn.k_proj
| 0.028935
| 1,024
| 4,096
| 4
| 8.169591
| -23.565732
| 1.134226
| true
| 0.001304
|
dense
| -23.368861
| -1.361035
| -2.884567
| 64
| 0.043548
| 1,024
| 40
| 960
| 1
| 1.133612
| 0.001304
| 33.383484
|
success
| 0.036117
| 0.000001
|
under-trained
| 960
| 0.001304
| 0.000628
|
221
|
model.layers.31.self_attn.o_proj
| 0.135698
| 4,096
| 4,096
| 1
| 10.803733
| -25.741085
| 1.557611
| true
| 0.004144
|
dense
| -25.740958
| -1.164674
| -2.38261
| 64
| 0.068442
| 4,096
| 64
| 4,032
| 1
| 1.225467
| 0.004144
| 16.517179
|
success
| 0.064372
| 0
|
under-trained
| 4,032
| 0.004144
| 0.000936
|
222
|
model.layers.31.self_attn.q_proj
| 0.079925
| 4,096
| 4,096
| 1
| 7.315148
| -18.390798
| 1.559154
| true
| 0.003061
|
dense
| -18.376024
| -1.221106
| -2.514071
| 64
| 0.060103
| 4,096
| 64
| 4,032
| 1
| 0.789393
| 0.003061
| 19.63199
|
success
| 0.055331
| 0
|
under-trained
| 4,032
| 0.003061
| 0.000778
|
223
|
model.layers.31.self_attn.v_proj
| 0.070425
| 1,024
| 4,096
| 4
| 17.833163
| -53.131643
| 1.136837
| true
| 0.001049
|
dense
| -52.768134
| -1.29625
| -2.979373
| 64
| 0.050553
| 1,024
| 24
| 960
| 1
| 3.436055
| 0.001049
| 48.208412
|
success
| 0.032383
| 0.000001
|
under-trained
| 960
| 0.001049
| 0.000799
|
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