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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