Datasets:
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layer
int64 0
31
| neuron
int64 0
11k
| gate+_in+_freq
float32 0
0.99
| gate+_in+_hook_post_max_values
listlengths 16
16
| gate+_in+_hook_post_max_indices
listlengths 16
16
| gate+_in+_hook_post_mean
float32 0
692
⌀ | gate+_in+_hook_pre_linear_max_values
listlengths 16
16
| gate+_in+_hook_pre_linear_max_indices
listlengths 16
16
| gate+_in+_hook_pre_linear_mean
float32 0.01
53.1
⌀ | gate+_in+_hook_pre_max_values
listlengths 16
16
| gate+_in+_hook_pre_max_indices
listlengths 16
16
| gate+_in+_hook_pre_mean
float32 0.01
29
⌀ | gate+_in+_swish_mean
float32 0.01
29
⌀ | gate+_in-_freq
float32 0
0.8
| gate+_in-_hook_post_max_values
listlengths 16
16
| gate+_in-_hook_post_max_indices
listlengths 16
16
| gate+_in-_hook_post_mean
float32 -46.38
-0
⌀ | gate+_in-_hook_pre_linear_max_values
listlengths 16
16
| gate+_in-_hook_pre_linear_max_indices
listlengths 16
16
| gate+_in-_hook_pre_linear_mean
float32 -19.77
-0
⌀ | gate+_in-_hook_pre_max_values
listlengths 16
16
| gate+_in-_hook_pre_max_indices
listlengths 16
16
| gate+_in-_hook_pre_mean
float32 0
10.4
⌀ | gate+_in-_swish_mean
float32 0
10.4
⌀ | gate-_in+_freq
float32 0
1
| gate-_in+_hook_post_max_values
listlengths 16
16
| gate-_in+_hook_post_max_indices
listlengths 16
16
| gate-_in+_hook_post_mean
float32 -1.42
-0
⌀ | gate-_in+_hook_pre_linear_max_values
listlengths 16
16
| gate-_in+_hook_pre_linear_max_indices
listlengths 16
16
| gate-_in+_hook_pre_linear_mean
float32 0.02
19.3
⌀ | gate-_in+_hook_pre_max_values
listlengths 16
16
| gate-_in+_hook_pre_max_indices
listlengths 16
16
| gate-_in+_hook_pre_mean
float32 -11.17
-0.02
⌀ | gate-_in+_swish_max_values
listlengths 16
16
| gate-_in+_swish_max_indices
listlengths 16
16
| gate-_in+_swish_mean
float32 -0.28
-0
⌀ | gate-_in-_freq
float32 0
1
| gate-_in-_hook_post_max_values
listlengths 16
16
| gate-_in-_hook_post_max_indices
listlengths 16
16
| gate-_in-_hook_post_mean
float32 0
2.51
⌀ | gate-_in-_hook_pre_linear_max_values
listlengths 16
16
| gate-_in-_hook_pre_linear_max_indices
listlengths 16
16
| gate-_in-_hook_pre_linear_mean
float32 -10.72
-0.02
⌀ | gate-_in-_hook_pre_max_values
listlengths 16
16
| gate-_in-_hook_pre_max_indices
listlengths 16
16
| gate-_in-_hook_pre_mean
float32 -23.01
-0.03
⌀ | gate-_in-_swish_max_values
listlengths 16
16
| gate-_in-_swish_max_indices
listlengths 16
16
| gate-_in-_swish_mean
float32 -0.28
-0
⌀ |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0
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End of preview. Expand
in Data Studio
This dataset contains activation data of neurons in OLMo-7B-0424. (We define a neuron as a hidden dimension in a MLP sublayer.) To create the dataset, the model was run on 20M tokens from Dolma.
Dataset Description
Each row corresponds to a neuron, identified by the columns "layer" and "neuron". (We use zero-based indexing).
The other columns are as follows:
- The first two elements of the name (e.g. "gate+_in+") indicate a sign combination of the activations. For example, "gate+_in+" means the cases in which both the "gate" ("hook_pre") and "in" ("hook_pre_linear") activations of the neuron were positive.
- For each of these sign combinations, we store:
- The relative frequency of the combination (e.g. "gate+_in+_freq")
- Summary statistics about activation values conditional on this sign combination, for each combination of:
- intermediate activation ("hook_pre_linear", "hook_pre", "swish", "hook_post") and
- type of summary statistics ("mean", "max_values", "max_indices"). Each of "max_values" and "max_indices" is a list of 16 elements. The indices are with respect to dolma-small. We use "max" as a shorthand for "max absolute values", so in many cases the values will actually be negative.
For example, "gate+_in-_hook_post_max_indices" means: "the indices of the 16 dolma-small entries for which this neuron had the smallest (strongest negative) hook_post activations, given that the gate value was positive and the in value was negative". Why "smallest / strongest negative"? Because when hook_pre>0 and hook_pre_linear<0, then hook_post=Swish(hook_pre)*hook_pre_linear is automatically negative.
Uses
This dataset is designed for interpretability research.
We created it for our GLUScope tool. The tool only shows visualizations for a small number of neurons, but with this dataset users can quickly create their own visualizations for neurons they are interested in.
It is also possible to use the dataset for more high-level exploration, such as looking for correlations between layers and certain summary statistics.
Citation
BibTeX:
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