layer_id
int64 0
223
| name
stringlengths 26
32
| D
float64 0.03
0.18
| M
int64 1.02k
4.1k
| N
int64 4.1k
14.3k
| Q
float64 1
4
| alpha
float64 2.02
8.65
| alpha_weighted
float64 -20.71
-1.63
| entropy
float64 0.77
1.55
| has_esd
bool 1
class | lambda_max
float32 0
0.2
| layer_type
stringclasses 1
value | log_alpha_norm
float64 -20.64
-1.6
| log_norm
float32 -1.48
-0.16
| log_spectral_norm
float32 -2.44
-0.7
| matrix_rank
int64 64
64
| norm
float32 0.03
0.69
| 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 0.19
1.31
| spectral_norm
float32 0
0.2
| stable_rank
float32 1.95
30.3
| status
stringclasses 1
value | sv_max
float64 0.06
0.45
| sv_min
float64 0
0
| warning
stringclasses 2
values | weak_rank_loss
int64 960
4.03k
| xmax
float64 0
0.2
| xmin
float64 0
0.01
|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
200
|
model.layers.28.self_attn.o_proj
| 0.145735
| 4,096
| 4,096
| 1
| 2.538182
| -4.21126
| 1.484161
| true
| 0.02192
|
dense
| -4.172619
| -0.93611
| -1.659164
| 64
| 0.115848
| 4,096
| 8
| 4,032
| 1
| 0.543829
| 0.02192
| 5.285113
|
success
| 0.148053
| 0
| 4,032
| 0.02192
| 0.001596
|
|
201
|
model.layers.28.self_attn.q_proj
| 0.057402
| 4,096
| 4,096
| 1
| 4.045189
| -7.383231
| 1.53592
| true
| 0.014956
|
dense
| -7.270875
| -0.743321
| -1.825188
| 64
| 0.180584
| 4,096
| 31
| 4,032
| 1
| 0.546932
| 0.014956
| 12.074456
|
success
| 0.122294
| 0
| 4,032
| 0.014956
| 0.002292
|
|
202
|
model.layers.28.self_attn.v_proj
| 0.082885
| 1,024
| 4,096
| 4
| 7.002583
| -15.899724
| 1.116428
| true
| 0.005364
|
dense
| -15.899541
| -1.207922
| -2.270551
| 64
| 0.061955
| 1,024
| 64
| 960
| 1
| 0.750323
| 0.005364
| 11.551245
|
success
| 0.073236
| 0.000001
|
under-trained
| 960
| 0.005364
| 0.000776
|
203
|
model.layers.29.mlp.down_proj
| 0.103636
| 4,096
| 14,336
| 3.5
| 2.782662
| -4.314618
| 1.512735
| true
| 0.028149
|
dense
| -4.116666
| -0.589399
| -1.550536
| 64
| 0.257396
| 4,096
| 16
| 4,032
| 1
| 0.445665
| 0.028149
| 9.144027
|
success
| 0.167777
| 0.000001
| 4,032
| 0.028149
| 0.003201
|
|
204
|
model.layers.29.mlp.gate_proj
| 0.13293
| 4,096
| 14,336
| 3.5
| 2.831996
| -4.282459
| 1.531378
| true
| 0.030749
|
dense
| -4.011525
| -0.439982
| -1.51217
| 64
| 0.363093
| 4,096
| 12
| 4,032
| 1
| 0.528852
| 0.030749
| 11.808329
|
success
| 0.175354
| 0.000001
| 4,032
| 0.030749
| 0.005316
|
|
205
|
model.layers.29.mlp.up_proj
| 0.131563
| 4,096
| 14,336
| 3.5
| 2.644039
| -3.954942
| 1.5121
| true
| 0.03193
|
dense
| -3.709622
| -0.525436
| -1.495796
| 64
| 0.298239
| 4,096
| 13
| 4,032
| 1
| 0.455974
| 0.03193
| 9.340278
|
success
| 0.178691
| 0.000001
| 4,032
| 0.03193
| 0.003979
|
|
206
|
model.layers.29.self_attn.k_proj
| 0.0856
| 1,024
| 4,096
| 4
| 3.743662
| -8.806612
| 1.119421
| true
| 0.004442
|
dense
| -8.455153
| -1.076607
| -2.352406
| 64
| 0.083829
| 1,024
| 18
| 960
| 1
| 0.646687
| 0.004442
| 18.871141
|
success
| 0.06665
| 0.000001
| 960
| 0.004442
| 0.00123
|
|
207
|
model.layers.29.self_attn.o_proj
| 0.122168
| 4,096
| 4,096
| 1
| 2.673862
| -4.427946
| 1.495641
| true
| 0.022079
|
dense
| -4.392883
| -0.894836
| -1.656011
| 64
| 0.127398
| 4,096
| 8
| 4,032
| 1
| 0.5918
| 0.022079
| 5.769995
|
success
| 0.148592
| 0
| 4,032
| 0.022079
| 0.001842
|
|
208
|
model.layers.29.self_attn.q_proj
| 0.066318
| 4,096
| 4,096
| 1
| 4.574451
| -8.434781
| 1.533344
| true
| 0.014326
|
dense
| -8.289402
| -0.76285
| -1.843889
| 64
| 0.172644
| 4,096
| 52
| 4,032
| 1
| 0.495687
| 0.014326
| 12.051463
|
success
| 0.119689
| 0
| 4,032
| 0.014326
| 0.001952
|
|
209
|
model.layers.29.self_attn.v_proj
| 0.11421
| 1,024
| 4,096
| 4
| 7.529444
| -17.524871
| 1.123541
| true
| 0.004704
|
dense
| -17.522147
| -1.145954
| -2.327512
| 64
| 0.071457
| 1,024
| 64
| 960
| 1
| 0.816181
| 0.004704
| 15.19001
|
success
| 0.068587
| 0.000001
|
under-trained
| 960
| 0.004704
| 0.00092
|
210
|
model.layers.30.mlp.down_proj
| 0.156046
| 4,096
| 14,336
| 3.5
| 2.608906
| -4.179348
| 1.511715
| true
| 0.025006
|
dense
| -3.823854
| -0.581968
| -1.601954
| 64
| 0.261837
| 4,096
| 12
| 4,032
| 1
| 0.464451
| 0.025006
| 10.470941
|
success
| 0.158133
| 0.000001
| 4,032
| 0.025006
| 0.003647
|
|
211
|
model.layers.30.mlp.gate_proj
| 0.124557
| 4,096
| 14,336
| 3.5
| 2.782323
| -4.270743
| 1.530313
| true
| 0.029177
|
dense
| -3.933391
| -0.440372
| -1.534956
| 64
| 0.362767
| 4,096
| 13
| 4,032
| 1
| 0.494327
| 0.029177
| 12.433222
|
success
| 0.170813
| 0.000001
| 4,032
| 0.029177
| 0.005054
|
|
212
|
model.layers.30.mlp.up_proj
| 0.170626
| 4,096
| 14,336
| 3.5
| 2.554896
| -3.997181
| 1.5195
| true
| 0.027257
|
dense
| -3.611438
| -0.509766
| -1.564518
| 64
| 0.309196
| 4,096
| 12
| 4,032
| 1
| 0.44886
| 0.027257
| 11.343627
|
success
| 0.165098
| 0.000001
| 4,032
| 0.027257
| 0.004161
|
|
213
|
model.layers.30.self_attn.k_proj
| 0.101164
| 1,024
| 4,096
| 4
| 5.358292
| -12.778783
| 1.116238
| true
| 0.004122
|
dense
| -12.638126
| -1.159035
| -2.384861
| 64
| 0.069337
| 1,024
| 64
| 960
| 1
| 0.544786
| 0.004122
| 16.820019
|
success
| 0.064205
| 0.000001
| 960
| 0.004122
| 0.000805
|
|
214
|
model.layers.30.self_attn.o_proj
| 0.074305
| 4,096
| 4,096
| 1
| 2.579508
| -4.380893
| 1.48816
| true
| 0.020029
|
dense
| -4.329809
| -0.941632
| -1.698345
| 64
| 0.114385
| 4,096
| 11
| 4,032
| 1
| 0.476239
| 0.020029
| 5.711001
|
success
| 0.141523
| 0
| 4,032
| 0.020029
| 0.001496
|
|
215
|
model.layers.30.self_attn.q_proj
| 0.09806
| 4,096
| 4,096
| 1
| 2.834588
| -5.029225
| 1.517705
| true
| 0.016818
|
dense
| -4.804394
| -0.777677
| -1.774235
| 64
| 0.166849
| 4,096
| 13
| 4,032
| 1
| 0.508823
| 0.016818
| 9.921068
|
success
| 0.129683
| 0
| 4,032
| 0.016818
| 0.002495
|
|
216
|
model.layers.30.self_attn.v_proj
| 0.102737
| 1,024
| 4,096
| 4
| 7.731426
| -18.03403
| 1.120981
| true
| 0.00465
|
dense
| -18.03366
| -1.205279
| -2.332562
| 64
| 0.062333
| 1,024
| 64
| 960
| 1
| 0.841428
| 0.00465
| 13.405494
|
success
| 0.06819
| 0.000001
|
under-trained
| 960
| 0.00465
| 0.000802
|
217
|
model.layers.31.mlp.down_proj
| 0.079029
| 4,096
| 14,336
| 3.5
| 2.902343
| -4.850056
| 1.512488
| true
| 0.021326
|
dense
| -4.475008
| -0.611118
| -1.671083
| 64
| 0.24484
| 4,096
| 25
| 4,032
| 1
| 0.380469
| 0.021326
| 11.480594
|
success
| 0.146036
| 0.000001
| 4,032
| 0.021326
| 0.002777
|
|
218
|
model.layers.31.mlp.gate_proj
| 0.097624
| 4,096
| 14,336
| 3.5
| 3.230765
| -5.040881
| 1.538523
| true
| 0.027525
|
dense
| -4.779333
| -0.424897
| -1.560275
| 64
| 0.375927
| 4,096
| 17
| 4,032
| 1
| 0.54104
| 0.027525
| 13.657705
|
success
| 0.165906
| 0.000001
| 4,032
| 0.027525
| 0.005125
|
|
219
|
model.layers.31.mlp.up_proj
| 0.113964
| 4,096
| 14,336
| 3.5
| 2.912509
| -4.665717
| 1.536689
| true
| 0.025006
|
dense
| -4.422057
| -0.510184
| -1.601958
| 64
| 0.308899
| 4,096
| 13
| 4,032
| 1
| 0.530434
| 0.025006
| 12.353059
|
success
| 0.158132
| 0.000001
| 4,032
| 0.025006
| 0.004429
|
|
220
|
model.layers.31.self_attn.k_proj
| 0.049394
| 1,024
| 4,096
| 4
| 4.553209
| -11.118033
| 1.124065
| true
| 0.003616
|
dense
| -10.97038
| -1.167458
| -2.441802
| 64
| 0.068005
| 1,024
| 24
| 960
| 1
| 0.725296
| 0.003616
| 18.80805
|
success
| 0.060131
| 0.000001
| 960
| 0.003616
| 0.000974
|
|
221
|
model.layers.31.self_attn.o_proj
| 0.087925
| 4,096
| 4,096
| 1
| 2.453679
| -4.101816
| 1.485777
| true
| 0.021296
|
dense
| -4.023601
| -0.907667
| -1.6717
| 64
| 0.123689
| 4,096
| 11
| 4,032
| 1
| 0.438301
| 0.021296
| 5.808087
|
success
| 0.145932
| 0
| 4,032
| 0.021296
| 0.001541
|
|
222
|
model.layers.31.self_attn.q_proj
| 0.065747
| 4,096
| 4,096
| 1
| 4.085555
| -7.643018
| 1.530533
| true
| 0.013467
|
dense
| -7.501466
| -0.788933
| -1.870742
| 64
| 0.16258
| 4,096
| 49
| 4,032
| 1
| 0.440794
| 0.013467
| 12.072827
|
success
| 0.116046
| 0
| 4,032
| 0.013467
| 0.001781
|
|
223
|
model.layers.31.self_attn.v_proj
| 0.11723
| 1,024
| 4,096
| 4
| 7.89883
| -18.247252
| 1.122072
| true
| 0.004896
|
dense
| -18.247011
| -1.171979
| -2.310121
| 64
| 0.067301
| 1,024
| 64
| 960
| 1
| 0.862354
| 0.004896
| 13.744894
|
success
| 0.069974
| 0.000001
|
under-trained
| 960
| 0.004896
| 0.000872
|
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