OUTPUT of Python run 4 of at most 300:
```
Correct: 33 repetitive: 199 length quantiles: [  842.   7866.5 32768.  32768.  32768. ]
Correct but repetitive: 2
Layer, contrast norm, leading neuron, activation difference, standardized difference, output magnitude, largest column/median
(1, 0.19, 14, 0.058, 0.95, 0.018, 1.17)
(2, 0.24, 15, -0.066, -1.14, 0.015, 1.27)
(3, 0.26, 3, 0.049, 0.8, 0.012, 1.25)
(4, 0.29, 1036, 0.075, 1.04, 0.022, 1.45)
(5, 0.27, 77, 0.049, 1.03, 0.013, 1.34)
(6, 0.34, 44, 0.096, 0.83, 0.031, 1.74)
(7, 0.37, 67, 0.09, 1.1, 0.022, 1.44)
(8, 0.34, 38, -0.047, -1.07, 0.015, 1.41)
(9, 0.38, 18, 0.08, 0.95, 0.023, 1.58)
(10, 0.45, 3, -0.108, -1.07, 0.025, 1.89)
(11, 0.47, 11, 0.162, 0.9, 0.035, 1.76)
(12, 0.49, 1226, 0.172, 1.16, 0.043, 1.52)
(13, 0.57, 7, -0.161, -0.82, 0.035, 1.77)
(14, 0.77, 6, -0.143, -1.26, 0.033, 1.77)
(15, 0.82, 0, 0.238, 1.01, 0.061, 1.81)
(16, 0.65, 98, -0.129, -0.86, 0.027, 1.69)
(17, 0.69, 20, 0.232, 0.69, 0.052, 1.35)
(18, 0.82, 8, -0.452, -0.97, 0.133, 1.3)
(19, 0.86, 11, -0.56, -0.74, 0.154, 1.46)
(20, 0.61, 89, 0.116, 0.58, 0.038, 1.36)
(21, 0.71, 30, -0.213, -0.7, 0.057, 1.57)
(22, 1.15, 59, 0.357, 0.69, 0.103, 1.52)
(23, 2.05, 22, 0.728, 0.67, 0.174, 2.04)
Top output-weighted neuron contrasts:
(0.1744, 23, 22, 0.7284, 0.67, 0.2394)
(0.1545, 19, 11, -0.5597, -0.7361, 0.276)
(0.1517, 23, 46, 0.4838, 0.7105, 0.3136)
(0.1367, 23, 40, -0.6361, -0.6669, 0.2149)
(0.1328, 18, 8, -0.4516, -0.973, 0.2941)
(0.1251, 23, 94, 0.4179, 0.7323, 0.2993)
(0.1137, 23, 15, -0.4006, -0.7836, 0.2839)
(0.1087, 23, 19, 0.3984, 0.7868, 0.2728)
(0.1033, 22, 59, 0.3574, 0.6855, 0.2889)
(0.0777, 22, 11, -0.4714, -1.0369, 0.1648)
(0.074, 23, 350, -0.2404, -0.79, 0.3079)
(0.0732, 23, 64, -0.2027, -0.6037, 0.3612)
(0.0693, 22, 89, -0.2526, -0.6679, 0.2742)
(0.0663, 22, 5, 0.2431, 1.2133, 0.2729)
(0.0606, 15, 0, 0.2382, 1.0075, 0.2546)
(0.0571, 21, 30, -0.2129, -0.6973, 0.2682)
(0.0515, 17, 20, 0.2321, 0.6935, 0.222)
(0.0501, 22, 7, 0.1874, 0.558, 0.2672)
(0.0494, 18, 29, 0.1821, 0.7004, 0.2713)
(0.0482, 22, 4, 0.179, 0.7632, 0.2694)
Attention and norm names in layers 0 and 3:
model.language_model.layers.0.input_layernorm.weight (1024,) mean/std/max 0.2376 0.1222 1.0
model.language_model.layers.0.linear_attn.A_log (16,) mean/std/max -1.9795 1.4952 6.5938
model.language_model.layers.0.linear_attn.conv1d.weight (6144, 1, 4) mean/std/max 0.0006 0.0728 1.7578
model.language_model.layers.0.linear_attn.dt_bias (16,) mean/std/max -1.8965 5.9508 13.0625
model.language_model.layers.0.linear_attn.in_proj_a.weight (16, 1024) mean/std/max -0.0007 0.0377 0.1816
model.language_model.layers.0.linear_attn.in_proj_b.weight (16, 1024) mean/std/max 0.0003 0.0175 0.1357
model.language_model.layers.0.linear_attn.in_proj_qkv.weight (6144, 1024) mean/std/max -0.0 0.0166 0.3906
model.language_model.layers.0.linear_attn.in_proj_z.weight (2048, 1024) mean/std/max 0.0 0.0173 0.4609
model.language_model.layers.0.linear_attn.norm.weight (128,) mean/std/max 0.9516 0.0858 1.0596
model.language_model.layers.0.linear_attn.out_proj.weight (1024, 2048) mean/std/max 0.0 0.0124 0.2832
model.language_model.layers.0.post_attention_layernorm.weight (1024,) mean/std/max 0.0853 0.0957 0.3574
model.language_model.layers.3.input_layernorm.weight (1024,) mean/std/max 0.3441 0.0894 0.8398
model.language_model.layers.3.post_attention_layernorm.weight (1024,) mean/std/max 0.2216 0.1087 0.4629
model.language_model.layers.3.self_attn.k_norm.weight (256,) mean/std/max 0.4167 0.2227 1.6328
model.language_model.layers.3.self_attn.k_proj.weight (512, 1024) mean/std/max 0.0 0.0124 0.1138
model.language_model.layers.3.self_attn.o_proj.weight (1024, 2048) mean/std/max 0.0 0.0122 0.3262
model.language_model.layers.3.self_attn.q_norm.weight (256,) mean/std/max 0.4269 0.1815 1.2266
model.language_model.layers.3.self_attn.q_proj.weight (4096, 1024) mean/std/max -0.0002 0.0198 0.25
model.language_model.layers.3.self_attn.v_proj.weight (512, 1024) mean/std/max 0.0 0.0128 0.1348

```