Dataset Viewer
name
stringlengths 10
31
| # Layers
int64 30
35
| # Effective Params (B)
float64 1.91
3.98
| MMLU PT accuracy
stringlengths 6
6
| FFN Hidden Dims
stringlengths 330
385
| Layers Skipped
stringclasses 1
value |
|---|---|---|---|---|---|
Main model
| 35 | 3.98 |
62.30%
|
[2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8]
| null |
Config for official E2B Model
| 30 | 1.91 |
50.90%
|
[2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4]
|
[20, 21, 22, 23, 24]
|
Config for E1.96B (layer-level)
| 30 | 1.96 |
53.40%
|
[2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 8, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4]
|
[20, 21, 22, 23, 24]
|
Config for E2.54B (layer-level)
| 35 | 2.54 |
55.40%
|
[2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 8, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4]
| null |
Config for E2.69B (layer-level)
| 35 | 2.69 |
57.70%
|
[2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 8, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 8, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 8, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4]
| null |
Config for E2.98B (layer-level)
| 35 | 2.98 |
59.50%
|
[2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8]
| null |
Config for E3.18B (layer-level)
| 35 | 3.18 |
61.80%
|
[2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 8, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 8, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 8]
| null |
Config for E3.39B (layer-level)
| 35 | 3.39 |
63.00%
|
[2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 8, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 8]
| null |
Config for E3.59B (layer-level)
| 35 | 3.59 |
63.40%
|
[2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 8]
| null |
Config for E3.79B (layer-level)
| 35 | 3.79 |
63.40%
|
[2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8]
| null |
Config for E2.49B (block-level)
| 35 | 2.49 |
54.50%
|
[2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4]
| null |
Config for E2.73B (block-level)
| 35 | 2.73 |
57.10%
|
[2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4]
| null |
Config for E2.98B (block-level)
| 35 | 2.98 |
59.50%
|
[2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4]
| null |
Config for E3.24B (block-level)
| 35 | 3.24 |
60.70%
|
[2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4]
| null |
Config for E3.49B (block-level)
| 35 | 3.49 |
61.40%
|
[2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4]
| null |
Config for E3.79B (block-level)
| 35 | 3.74 |
62.00%
|
[2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 4, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8, 2_048 * 8]
| null |
This repository contains configurations to slice Gemma 3n E4B, which is enabled thanks to it being a MatFormer. The E4B model can be sliced into small models, trading off quality and latency/compute requirements. We recommend exploring the [MatFormer Lab](TODO: add link) to getting started with slicing Gemma 3n E4B yourself.
For each configuration, we calculate the MMLU accuracy. Although these are not the only configurations possible, they are optimal configurations identified by calculating the accuracy of the pre-trained model
To learn more about MatFormers, please review the and generate your own submodels with the [MatFormer Lab](TODO: add link).
This chart show’s MMLU performance vs model size of Gemma 3n Mix-n-Match (pretrained) capability.
Some additional resources:
- Downloads last month
- 51