openai-gpt
Find this model in the GPT model summary
openai-gpt Model Summary Plots
openai-gpt Model Selected Details
layer_type
N
M
Q
alpha
D
alpha-hat
log_SN
% Rand
num_traps
num_fingers
rank_loss
layer_id
1
EMBEDDING
40478
768
52.71
3.00
0.04
15.02
5.01
63.52
0
0
0
2
EMBEDDING
768
512
1.50
1.38
0.07
3.14
2.27
26.70
0
0
0
7
CONV1D
2304
768
3.00
2.04
0.05
6.52
3.20
50.78
0
0
0
8
CONV1D
768
768
1.00
2.32
0.06
2.69
1.16
81.02
0
0
1
13
CONV1D
3072
768
4.00
4.01
0.06
10.67
2.66
84.48
0
0
0
14
CONV1D
3072
768
4.00
2.65
0.04
6.77
2.56
87.08
20
0
0
20
CONV1D
2304
768
3.00
4.77
0.05
9.48
1.99
90.13
0
0
0
21
CONV1D
768
768
1.00
4.51
0.04
4.99
1.11
90.66
0
0
1
26
CONV1D
3072
768
4.00
3.47
0.04
9.52
2.74
82.90
1
0
0
27
CONV1D
3072
768
4.00
3.37
0.05
10.29
3.05
83.44
24
0
0
32
CONV1D
2304
768
3.00
4.23
0.05
8.94
2.11
89.68
0
0
0
33
CONV1D
768
768
1.00
5.88
0.07
5.79
0.98
92.53
0
0
1
38
CONV1D
3072
768
4.00
3.76
0.04
9.93
2.64
84.72
1
0
0
39
CONV1D
3072
768
4.00
3.95
0.04
10.60
2.69
90.90
5
0
0
44
CONV1D
2304
768
3.00
4.98
0.04
9.33
1.87
91.46
0
0
0
45
CONV1D
768
768
1.00
7.09
0.04
6.49
0.92
94.37
0
0
1
50
CONV1D
3072
768
4.00
4.18
0.04
10.81
2.59
86.25
1
0
0
51
CONV1D
3072
768
4.00
3.81
0.05
11.61
3.05
87.68
4
0
0
56
CONV1D
2304
768
3.00
4.34
0.03
8.17
1.88
88.29
0
0
0
57
CONV1D
768
768
1.00
6.03
0.07
4.91
0.81
93.59
0
0
1
62
CONV1D
3072
768
4.00
4.19
0.03
10.85
2.59
86.52
1
0
0
63
CONV1D
3072
768
4.00
3.93
0.03
9.76
2.48
88.55
2
0
0
68
CONV1D
2304
768
3.00
4.16
0.04
7.64
1.84
87.33
0
0
0
69
CONV1D
768
768
1.00
6.06
0.09
4.56
0.75
92.31
0
0
1
74
CONV1D
3072
768
4.00
4.00
0.04
9.96
2.49
88.22
1
0
0
75
CONV1D
3072
768
4.00
3.89
0.03
9.58
2.46
87.21
5
0
0
80
CONV1D
2304
768
3.00
3.56
0.04
6.96
1.96
86.41
0
0
0
81
CONV1D
768
768
1.00
5.48
0.11
4.25
0.77
93.56
0
1
1
86
CONV1D
3072
768
4.00
4.07
0.03
9.88
2.42
87.79
1
0
0
87
CONV1D
3072
768
4.00
3.55
0.04
8.05
2.27
86.89
4
0
0
92
CONV1D
2304
768
3.00
3.82
0.03
7.03
1.84
88.14
0
0
0
93
CONV1D
768
768
1.00
3.50
0.13
3.24
0.93
92.69
0
4
1
98
CONV1D
3072
768
4.00
4.19
0.02
9.83
2.35
87.93
1
0
0
99
CONV1D
3072
768
4.00
3.89
0.04
10.86
2.79
83.15
3
0
0
104
CONV1D
2304
768
3.00
4.96
0.05
9.59
1.93
88.59
0
0
0
105
CONV1D
768
768
1.00
3.16
0.12
2.72
0.86
93.14
0
1
1
110
CONV1D
3072
768
4.00
4.09
0.02
9.69
2.37
90.29
1
0
0
111
CONV1D
3072
768
4.00
3.81
0.04
11.10
2.92
79.48
4
0
0
116
CONV1D
2304
768
3.00
4.50
0.03
8.71
1.94
89.28
0
0
0
117
CONV1D
768
768
1.00
8.26
0.13
6.61
0.80
95.02
0
1
1
122
CONV1D
3072
768
4.00
4.06
0.02
9.38
2.31
91.01
1
0
0
123
CONV1D
3072
768
4.00
4.16
0.04
9.69
2.33
91.10
1
0
0
128
CONV1D
2304
768
3.00
4.33
0.04
8.19
1.89
89.22
0
0
0
129
CONV1D
768
768
1.00
6.23
0.12
5.48
0.88
96.21
0
3
1
134
CONV1D
3072
768
4.00
4.10
0.02
8.90
2.17
90.36
1
0
0
135
CONV1D
3072
768
4.00
4.18
0.05
9.80
2.34
90.88
3
0
0
140
CONV1D
2304
768
3.00
3.55
0.03
7.17
2.02
87.02
0
0
0
141
CONV1D
768
768
1.00
4.17
0.11
6.75
1.62
88.45
1
0
0
146
CONV1D
3072
768
4.00
3.19
0.02
7.12
2.23
83.94
1
0
0
147
CONV1D
3072
768
4.00
4.13
0.02
8.82
2.14
88.94
11
0
0
openai-gpt Layer Plots
Layer 1
Layer=1 | N=40478 | M=768 | Q=52.71 | alpha=3.00 | D_ks=0.04 | alpha-hat=15.02 | num traps=0
Layer 2
Layer=2 | N=768 | M=512 | Q=1.50 | alpha=1.38 | D_ks=0.07 | alpha-hat=3.14 | num traps=0
Layer 7
Layer=7 | N=2304 | M=768 | Q=3.00 | alpha=2.04 | D_ks=0.05 | alpha-hat=6.52 | num traps=0
Layer 8
Layer=8 | N=768 | M=768 | Q=1.00 | alpha=2.32 | D_ks=0.06 | alpha-hat=2.69 | num traps=0
Layer 13
Layer=13 | N=3072 | M=768 | Q=4.00 | alpha=4.01 | D_ks=0.06 | alpha-hat=10.67 | num traps=0
Layer 14
Layer=14 | N=3072 | M=768 | Q=4.00 | alpha=2.65 | D_ks=0.04 | alpha-hat=6.77 | num traps=20
Layer 20
Layer=20 | N=2304 | M=768 | Q=3.00 | alpha=4.77 | D_ks=0.05 | alpha-hat=9.48 | num traps=0
Layer 21
Layer=21 | N=768 | M=768 | Q=1.00 | alpha=4.51 | D_ks=0.04 | alpha-hat=4.99 | num traps=0
Layer 26
Layer=26 | N=3072 | M=768 | Q=4.00 | alpha=3.47 | D_ks=0.04 | alpha-hat=9.52 | num traps=1
Layer 27
Layer=27 | N=3072 | M=768 | Q=4.00 | alpha=3.37 | D_ks=0.05 | alpha-hat=10.29 | num traps=24
Layer 32
Layer=32 | N=2304 | M=768 | Q=3.00 | alpha=4.23 | D_ks=0.05 | alpha-hat=8.94 | num traps=0
Layer 33
Layer=33 | N=768 | M=768 | Q=1.00 | alpha=5.88 | D_ks=0.07 | alpha-hat=5.79 | num traps=0
Layer 38
Layer=38 | N=3072 | M=768 | Q=4.00 | alpha=3.76 | D_ks=0.04 | alpha-hat=9.93 | num traps=1
Layer 39
Layer=39 | N=3072 | M=768 | Q=4.00 | alpha=3.95 | D_ks=0.04 | alpha-hat=10.60 | num traps=5
Layer 44
Layer=44 | N=2304 | M=768 | Q=3.00 | alpha=4.98 | D_ks=0.04 | alpha-hat=9.33 | num traps=0
Layer 45
Layer=45 | N=768 | M=768 | Q=1.00 | alpha=7.09 | D_ks=0.04 | alpha-hat=6.49 | num traps=0
Layer 50
Layer=50 | N=3072 | M=768 | Q=4.00 | alpha=4.18 | D_ks=0.04 | alpha-hat=10.81 | num traps=1
Layer 51
Layer=51 | N=3072 | M=768 | Q=4.00 | alpha=3.81 | D_ks=0.05 | alpha-hat=11.61 | num traps=4
Layer 56
Layer=56 | N=2304 | M=768 | Q=3.00 | alpha=4.34 | D_ks=0.03 | alpha-hat=8.17 | num traps=0
Layer 57
Layer=57 | N=768 | M=768 | Q=1.00 | alpha=6.03 | D_ks=0.07 | alpha-hat=4.91 | num traps=0
Layer 62
Layer=62 | N=3072 | M=768 | Q=4.00 | alpha=4.19 | D_ks=0.03 | alpha-hat=10.85 | num traps=1
Layer 63
Layer=63 | N=3072 | M=768 | Q=4.00 | alpha=3.93 | D_ks=0.03 | alpha-hat=9.76 | num traps=2
Layer 68
Layer=68 | N=2304 | M=768 | Q=3.00 | alpha=4.16 | D_ks=0.04 | alpha-hat=7.64 | num traps=0
Layer 69
Layer=69 | N=768 | M=768 | Q=1.00 | alpha=6.06 | D_ks=0.09 | alpha-hat=4.56 | num traps=0
Layer 74
Layer=74 | N=3072 | M=768 | Q=4.00 | alpha=4.00 | D_ks=0.04 | alpha-hat=9.96 | num traps=1
Layer 75
Layer=75 | N=3072 | M=768 | Q=4.00 | alpha=3.89 | D_ks=0.03 | alpha-hat=9.58 | num traps=5
Layer 80
Layer=80 | N=2304 | M=768 | Q=3.00 | alpha=3.56 | D_ks=0.04 | alpha-hat=6.96 | num traps=0
Layer 81
Layer=81 | N=768 | M=768 | Q=1.00 | alpha=5.48 | D_ks=0.11 | alpha-hat=4.25 | num traps=0
Layer 86
Layer=86 | N=3072 | M=768 | Q=4.00 | alpha=4.07 | D_ks=0.03 | alpha-hat=9.88 | num traps=1
Layer 87
Layer=87 | N=3072 | M=768 | Q=4.00 | alpha=3.55 | D_ks=0.04 | alpha-hat=8.05 | num traps=4
Layer 92
Layer=92 | N=2304 | M=768 | Q=3.00 | alpha=3.82 | D_ks=0.03 | alpha-hat=7.03 | num traps=0
Layer 93
Layer=93 | N=768 | M=768 | Q=1.00 | alpha=3.50 | D_ks=0.13 | alpha-hat=3.24 | num traps=0
Layer 98
Layer=98 | N=3072 | M=768 | Q=4.00 | alpha=4.19 | D_ks=0.02 | alpha-hat=9.83 | num traps=1
Layer 99
Layer=99 | N=3072 | M=768 | Q=4.00 | alpha=3.89 | D_ks=0.04 | alpha-hat=10.86 | num traps=3
Layer 104
Layer=104 | N=2304 | M=768 | Q=3.00 | alpha=4.96 | D_ks=0.05 | alpha-hat=9.59 | num traps=0
Layer 105
Layer=105 | N=768 | M=768 | Q=1.00 | alpha=3.16 | D_ks=0.12 | alpha-hat=2.72 | num traps=0
Layer 110
Layer=110 | N=3072 | M=768 | Q=4.00 | alpha=4.09 | D_ks=0.02 | alpha-hat=9.69 | num traps=1
Layer 111
Layer=111 | N=3072 | M=768 | Q=4.00 | alpha=3.81 | D_ks=0.04 | alpha-hat=11.10 | num traps=4
Layer 116
Layer=116 | N=2304 | M=768 | Q=3.00 | alpha=4.50 | D_ks=0.03 | alpha-hat=8.71 | num traps=0
Layer 117
Layer=117 | N=768 | M=768 | Q=1.00 | alpha=8.26 | D_ks=0.13 | alpha-hat=6.61 | num traps=0
Layer 122
Layer=122 | N=3072 | M=768 | Q=4.00 | alpha=4.06 | D_ks=0.02 | alpha-hat=9.38 | num traps=1
Layer 123
Layer=123 | N=3072 | M=768 | Q=4.00 | alpha=4.16 | D_ks=0.04 | alpha-hat=9.69 | num traps=1
Layer 128
Layer=128 | N=2304 | M=768 | Q=3.00 | alpha=4.33 | D_ks=0.04 | alpha-hat=8.19 | num traps=0
Layer 129
Layer=129 | N=768 | M=768 | Q=1.00 | alpha=6.23 | D_ks=0.12 | alpha-hat=5.48 | num traps=0
Layer 134
Layer=134 | N=3072 | M=768 | Q=4.00 | alpha=4.10 | D_ks=0.02 | alpha-hat=8.90 | num traps=1
Layer 135
Layer=135 | N=3072 | M=768 | Q=4.00 | alpha=4.18 | D_ks=0.05 | alpha-hat=9.80 | num traps=3
Layer 140
Layer=140 | N=2304 | M=768 | Q=3.00 | alpha=3.55 | D_ks=0.03 | alpha-hat=7.17 | num traps=0
Layer 141
Layer=141 | N=768 | M=768 | Q=1.00 | alpha=4.17 | D_ks=0.11 | alpha-hat=6.75 | num traps=1
Layer 146
Layer=146 | N=3072 | M=768 | Q=4.00 | alpha=3.19 | D_ks=0.02 | alpha-hat=7.12 | num traps=1
Layer 147
Layer=147 | N=3072 | M=768 | Q=4.00 | alpha=4.13 | D_ks=0.02 | alpha-hat=8.82 | num traps=11
openai-gpt Model Summary Plots
openai-gpt Model Selected Details
| layer_type | N | M | Q | alpha | D | alpha-hat | log_SN | % Rand | num_traps | num_fingers | rank_loss | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| layer_id | ||||||||||||
| 1 | EMBEDDING | 40478 | 768 | 52.71 | 3.00 | 0.04 | 15.02 | 5.01 | 63.52 | 0 | 0 | 0 |
| 2 | EMBEDDING | 768 | 512 | 1.50 | 1.38 | 0.07 | 3.14 | 2.27 | 26.70 | 0 | 0 | 0 |
| 7 | CONV1D | 2304 | 768 | 3.00 | 2.04 | 0.05 | 6.52 | 3.20 | 50.78 | 0 | 0 | 0 |
| 8 | CONV1D | 768 | 768 | 1.00 | 2.32 | 0.06 | 2.69 | 1.16 | 81.02 | 0 | 0 | 1 |
| 13 | CONV1D | 3072 | 768 | 4.00 | 4.01 | 0.06 | 10.67 | 2.66 | 84.48 | 0 | 0 | 0 |
| 14 | CONV1D | 3072 | 768 | 4.00 | 2.65 | 0.04 | 6.77 | 2.56 | 87.08 | 20 | 0 | 0 |
| 20 | CONV1D | 2304 | 768 | 3.00 | 4.77 | 0.05 | 9.48 | 1.99 | 90.13 | 0 | 0 | 0 |
| 21 | CONV1D | 768 | 768 | 1.00 | 4.51 | 0.04 | 4.99 | 1.11 | 90.66 | 0 | 0 | 1 |
| 26 | CONV1D | 3072 | 768 | 4.00 | 3.47 | 0.04 | 9.52 | 2.74 | 82.90 | 1 | 0 | 0 |
| 27 | CONV1D | 3072 | 768 | 4.00 | 3.37 | 0.05 | 10.29 | 3.05 | 83.44 | 24 | 0 | 0 |
| 32 | CONV1D | 2304 | 768 | 3.00 | 4.23 | 0.05 | 8.94 | 2.11 | 89.68 | 0 | 0 | 0 |
| 33 | CONV1D | 768 | 768 | 1.00 | 5.88 | 0.07 | 5.79 | 0.98 | 92.53 | 0 | 0 | 1 |
| 38 | CONV1D | 3072 | 768 | 4.00 | 3.76 | 0.04 | 9.93 | 2.64 | 84.72 | 1 | 0 | 0 |
| 39 | CONV1D | 3072 | 768 | 4.00 | 3.95 | 0.04 | 10.60 | 2.69 | 90.90 | 5 | 0 | 0 |
| 44 | CONV1D | 2304 | 768 | 3.00 | 4.98 | 0.04 | 9.33 | 1.87 | 91.46 | 0 | 0 | 0 |
| 45 | CONV1D | 768 | 768 | 1.00 | 7.09 | 0.04 | 6.49 | 0.92 | 94.37 | 0 | 0 | 1 |
| 50 | CONV1D | 3072 | 768 | 4.00 | 4.18 | 0.04 | 10.81 | 2.59 | 86.25 | 1 | 0 | 0 |
| 51 | CONV1D | 3072 | 768 | 4.00 | 3.81 | 0.05 | 11.61 | 3.05 | 87.68 | 4 | 0 | 0 |
| 56 | CONV1D | 2304 | 768 | 3.00 | 4.34 | 0.03 | 8.17 | 1.88 | 88.29 | 0 | 0 | 0 |
| 57 | CONV1D | 768 | 768 | 1.00 | 6.03 | 0.07 | 4.91 | 0.81 | 93.59 | 0 | 0 | 1 |
| 62 | CONV1D | 3072 | 768 | 4.00 | 4.19 | 0.03 | 10.85 | 2.59 | 86.52 | 1 | 0 | 0 |
| 63 | CONV1D | 3072 | 768 | 4.00 | 3.93 | 0.03 | 9.76 | 2.48 | 88.55 | 2 | 0 | 0 |
| 68 | CONV1D | 2304 | 768 | 3.00 | 4.16 | 0.04 | 7.64 | 1.84 | 87.33 | 0 | 0 | 0 |
| 69 | CONV1D | 768 | 768 | 1.00 | 6.06 | 0.09 | 4.56 | 0.75 | 92.31 | 0 | 0 | 1 |
| 74 | CONV1D | 3072 | 768 | 4.00 | 4.00 | 0.04 | 9.96 | 2.49 | 88.22 | 1 | 0 | 0 |
| 75 | CONV1D | 3072 | 768 | 4.00 | 3.89 | 0.03 | 9.58 | 2.46 | 87.21 | 5 | 0 | 0 |
| 80 | CONV1D | 2304 | 768 | 3.00 | 3.56 | 0.04 | 6.96 | 1.96 | 86.41 | 0 | 0 | 0 |
| 81 | CONV1D | 768 | 768 | 1.00 | 5.48 | 0.11 | 4.25 | 0.77 | 93.56 | 0 | 1 | 1 |
| 86 | CONV1D | 3072 | 768 | 4.00 | 4.07 | 0.03 | 9.88 | 2.42 | 87.79 | 1 | 0 | 0 |
| 87 | CONV1D | 3072 | 768 | 4.00 | 3.55 | 0.04 | 8.05 | 2.27 | 86.89 | 4 | 0 | 0 |
| 92 | CONV1D | 2304 | 768 | 3.00 | 3.82 | 0.03 | 7.03 | 1.84 | 88.14 | 0 | 0 | 0 |
| 93 | CONV1D | 768 | 768 | 1.00 | 3.50 | 0.13 | 3.24 | 0.93 | 92.69 | 0 | 4 | 1 |
| 98 | CONV1D | 3072 | 768 | 4.00 | 4.19 | 0.02 | 9.83 | 2.35 | 87.93 | 1 | 0 | 0 |
| 99 | CONV1D | 3072 | 768 | 4.00 | 3.89 | 0.04 | 10.86 | 2.79 | 83.15 | 3 | 0 | 0 |
| 104 | CONV1D | 2304 | 768 | 3.00 | 4.96 | 0.05 | 9.59 | 1.93 | 88.59 | 0 | 0 | 0 |
| 105 | CONV1D | 768 | 768 | 1.00 | 3.16 | 0.12 | 2.72 | 0.86 | 93.14 | 0 | 1 | 1 |
| 110 | CONV1D | 3072 | 768 | 4.00 | 4.09 | 0.02 | 9.69 | 2.37 | 90.29 | 1 | 0 | 0 |
| 111 | CONV1D | 3072 | 768 | 4.00 | 3.81 | 0.04 | 11.10 | 2.92 | 79.48 | 4 | 0 | 0 |
| 116 | CONV1D | 2304 | 768 | 3.00 | 4.50 | 0.03 | 8.71 | 1.94 | 89.28 | 0 | 0 | 0 |
| 117 | CONV1D | 768 | 768 | 1.00 | 8.26 | 0.13 | 6.61 | 0.80 | 95.02 | 0 | 1 | 1 |
| 122 | CONV1D | 3072 | 768 | 4.00 | 4.06 | 0.02 | 9.38 | 2.31 | 91.01 | 1 | 0 | 0 |
| 123 | CONV1D | 3072 | 768 | 4.00 | 4.16 | 0.04 | 9.69 | 2.33 | 91.10 | 1 | 0 | 0 |
| 128 | CONV1D | 2304 | 768 | 3.00 | 4.33 | 0.04 | 8.19 | 1.89 | 89.22 | 0 | 0 | 0 |
| 129 | CONV1D | 768 | 768 | 1.00 | 6.23 | 0.12 | 5.48 | 0.88 | 96.21 | 0 | 3 | 1 |
| 134 | CONV1D | 3072 | 768 | 4.00 | 4.10 | 0.02 | 8.90 | 2.17 | 90.36 | 1 | 0 | 0 |
| 135 | CONV1D | 3072 | 768 | 4.00 | 4.18 | 0.05 | 9.80 | 2.34 | 90.88 | 3 | 0 | 0 |
| 140 | CONV1D | 2304 | 768 | 3.00 | 3.55 | 0.03 | 7.17 | 2.02 | 87.02 | 0 | 0 | 0 |
| 141 | CONV1D | 768 | 768 | 1.00 | 4.17 | 0.11 | 6.75 | 1.62 | 88.45 | 1 | 0 | 0 |
| 146 | CONV1D | 3072 | 768 | 4.00 | 3.19 | 0.02 | 7.12 | 2.23 | 83.94 | 1 | 0 | 0 |
| 147 | CONV1D | 3072 | 768 | 4.00 | 4.13 | 0.02 | 8.82 | 2.14 | 88.94 | 11 | 0 | 0 |
openai-gpt Layer Plots
Layer 1
Layer=1 | N=40478 | M=768 | Q=52.71 | alpha=3.00 | D_ks=0.04 | alpha-hat=15.02 | num traps=0
Layer 2
Layer=2 | N=768 | M=512 | Q=1.50 | alpha=1.38 | D_ks=0.07 | alpha-hat=3.14 | num traps=0
Layer 7
Layer=7 | N=2304 | M=768 | Q=3.00 | alpha=2.04 | D_ks=0.05 | alpha-hat=6.52 | num traps=0
Layer 8
Layer=8 | N=768 | M=768 | Q=1.00 | alpha=2.32 | D_ks=0.06 | alpha-hat=2.69 | num traps=0
Layer 13
Layer=13 | N=3072 | M=768 | Q=4.00 | alpha=4.01 | D_ks=0.06 | alpha-hat=10.67 | num traps=0
Layer 14
Layer=14 | N=3072 | M=768 | Q=4.00 | alpha=2.65 | D_ks=0.04 | alpha-hat=6.77 | num traps=20
Layer 20
Layer=20 | N=2304 | M=768 | Q=3.00 | alpha=4.77 | D_ks=0.05 | alpha-hat=9.48 | num traps=0
Layer 21
Layer=21 | N=768 | M=768 | Q=1.00 | alpha=4.51 | D_ks=0.04 | alpha-hat=4.99 | num traps=0
Layer 26
Layer=26 | N=3072 | M=768 | Q=4.00 | alpha=3.47 | D_ks=0.04 | alpha-hat=9.52 | num traps=1
Layer 27
Layer=27 | N=3072 | M=768 | Q=4.00 | alpha=3.37 | D_ks=0.05 | alpha-hat=10.29 | num traps=24
Layer 32
Layer=32 | N=2304 | M=768 | Q=3.00 | alpha=4.23 | D_ks=0.05 | alpha-hat=8.94 | num traps=0
Layer 33
Layer=33 | N=768 | M=768 | Q=1.00 | alpha=5.88 | D_ks=0.07 | alpha-hat=5.79 | num traps=0
Layer 38
Layer=38 | N=3072 | M=768 | Q=4.00 | alpha=3.76 | D_ks=0.04 | alpha-hat=9.93 | num traps=1
Layer 39
Layer=39 | N=3072 | M=768 | Q=4.00 | alpha=3.95 | D_ks=0.04 | alpha-hat=10.60 | num traps=5
Layer 44
Layer=44 | N=2304 | M=768 | Q=3.00 | alpha=4.98 | D_ks=0.04 | alpha-hat=9.33 | num traps=0
Layer 45
Layer=45 | N=768 | M=768 | Q=1.00 | alpha=7.09 | D_ks=0.04 | alpha-hat=6.49 | num traps=0
Layer 50
Layer=50 | N=3072 | M=768 | Q=4.00 | alpha=4.18 | D_ks=0.04 | alpha-hat=10.81 | num traps=1
Layer 51
Layer=51 | N=3072 | M=768 | Q=4.00 | alpha=3.81 | D_ks=0.05 | alpha-hat=11.61 | num traps=4
Layer 56
Layer=56 | N=2304 | M=768 | Q=3.00 | alpha=4.34 | D_ks=0.03 | alpha-hat=8.17 | num traps=0
Layer 57
Layer=57 | N=768 | M=768 | Q=1.00 | alpha=6.03 | D_ks=0.07 | alpha-hat=4.91 | num traps=0
Layer 62
Layer=62 | N=3072 | M=768 | Q=4.00 | alpha=4.19 | D_ks=0.03 | alpha-hat=10.85 | num traps=1
Layer 63
Layer=63 | N=3072 | M=768 | Q=4.00 | alpha=3.93 | D_ks=0.03 | alpha-hat=9.76 | num traps=2
Layer 68
Layer=68 | N=2304 | M=768 | Q=3.00 | alpha=4.16 | D_ks=0.04 | alpha-hat=7.64 | num traps=0
Layer 69
Layer=69 | N=768 | M=768 | Q=1.00 | alpha=6.06 | D_ks=0.09 | alpha-hat=4.56 | num traps=0
Layer 74
Layer=74 | N=3072 | M=768 | Q=4.00 | alpha=4.00 | D_ks=0.04 | alpha-hat=9.96 | num traps=1
Layer 75
Layer=75 | N=3072 | M=768 | Q=4.00 | alpha=3.89 | D_ks=0.03 | alpha-hat=9.58 | num traps=5
Layer 80
Layer=80 | N=2304 | M=768 | Q=3.00 | alpha=3.56 | D_ks=0.04 | alpha-hat=6.96 | num traps=0
Layer 81
Layer=81 | N=768 | M=768 | Q=1.00 | alpha=5.48 | D_ks=0.11 | alpha-hat=4.25 | num traps=0
Layer 86
Layer=86 | N=3072 | M=768 | Q=4.00 | alpha=4.07 | D_ks=0.03 | alpha-hat=9.88 | num traps=1
Layer 87
Layer=87 | N=3072 | M=768 | Q=4.00 | alpha=3.55 | D_ks=0.04 | alpha-hat=8.05 | num traps=4
Layer 92
Layer=92 | N=2304 | M=768 | Q=3.00 | alpha=3.82 | D_ks=0.03 | alpha-hat=7.03 | num traps=0
Layer 93
Layer=93 | N=768 | M=768 | Q=1.00 | alpha=3.50 | D_ks=0.13 | alpha-hat=3.24 | num traps=0
Layer 98
Layer=98 | N=3072 | M=768 | Q=4.00 | alpha=4.19 | D_ks=0.02 | alpha-hat=9.83 | num traps=1
Layer 99
Layer=99 | N=3072 | M=768 | Q=4.00 | alpha=3.89 | D_ks=0.04 | alpha-hat=10.86 | num traps=3
Layer 104
Layer=104 | N=2304 | M=768 | Q=3.00 | alpha=4.96 | D_ks=0.05 | alpha-hat=9.59 | num traps=0
Layer 105
Layer=105 | N=768 | M=768 | Q=1.00 | alpha=3.16 | D_ks=0.12 | alpha-hat=2.72 | num traps=0
Layer 110
Layer=110 | N=3072 | M=768 | Q=4.00 | alpha=4.09 | D_ks=0.02 | alpha-hat=9.69 | num traps=1
Layer 111
Layer=111 | N=3072 | M=768 | Q=4.00 | alpha=3.81 | D_ks=0.04 | alpha-hat=11.10 | num traps=4
Layer 116
Layer=116 | N=2304 | M=768 | Q=3.00 | alpha=4.50 | D_ks=0.03 | alpha-hat=8.71 | num traps=0
Layer 117
Layer=117 | N=768 | M=768 | Q=1.00 | alpha=8.26 | D_ks=0.13 | alpha-hat=6.61 | num traps=0
Layer 122
Layer=122 | N=3072 | M=768 | Q=4.00 | alpha=4.06 | D_ks=0.02 | alpha-hat=9.38 | num traps=1
Layer 123
Layer=123 | N=3072 | M=768 | Q=4.00 | alpha=4.16 | D_ks=0.04 | alpha-hat=9.69 | num traps=1
Layer 128
Layer=128 | N=2304 | M=768 | Q=3.00 | alpha=4.33 | D_ks=0.04 | alpha-hat=8.19 | num traps=0
Layer 129
Layer=129 | N=768 | M=768 | Q=1.00 | alpha=6.23 | D_ks=0.12 | alpha-hat=5.48 | num traps=0
Layer 134
Layer=134 | N=3072 | M=768 | Q=4.00 | alpha=4.10 | D_ks=0.02 | alpha-hat=8.90 | num traps=1
Layer 135
Layer=135 | N=3072 | M=768 | Q=4.00 | alpha=4.18 | D_ks=0.05 | alpha-hat=9.80 | num traps=3
Layer 140
Layer=140 | N=2304 | M=768 | Q=3.00 | alpha=3.55 | D_ks=0.03 | alpha-hat=7.17 | num traps=0
Layer 141
Layer=141 | N=768 | M=768 | Q=1.00 | alpha=4.17 | D_ks=0.11 | alpha-hat=6.75 | num traps=1
Layer 146
Layer=146 | N=3072 | M=768 | Q=4.00 | alpha=3.19 | D_ks=0.02 | alpha-hat=7.12 | num traps=1
Layer 147
Layer=147 | N=3072 | M=768 | Q=4.00 | alpha=4.13 | D_ks=0.02 | alpha-hat=8.82 | num traps=11