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--- |
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license: mit |
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size_categories: |
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pretty_name: n |
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path: fm-16/162-* |
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path: fm-16/163-* |
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- split: '297' |
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path: fm-16/297-* |
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- split: '298' |
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- split: '299' |
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path: fm-16/299-* |
|
- config_name: lm1b-2-32 |
|
data_files: |
|
- split: '0' |
|
path: lm1b-2-32/0-* |
|
- split: '1' |
|
path: lm1b-2-32/1-* |
|
- split: '2' |
|
path: lm1b-2-32/2-* |
|
- config_name: lm1b-3-24 |
|
data_files: |
|
- split: '0' |
|
path: lm1b-3-24/0-* |
|
- split: '1' |
|
path: lm1b-3-24/1-* |
|
- split: '2' |
|
path: lm1b-3-24/2-* |
|
--- |
|
|
|
**The dataset is being prepared and uploaded** |
|
|
|
This is the dataset of trained neural network checkpoints used to meta-train the NiNo model from https://github.com/SamsungSAILMontreal/nino/. |
|
|
|
It contains 1000 models in total: |
|
- 300 small convnets with 3 layers and 16, 32 and 32 channels (14,378 parameters in each model), trained on FashionMNIST (**FM-16**) |
|
- 300 small convnets with 3 layers and 16, 32 and 32 channels (14,666 parameters in each model), trained on CIFAR10 (**C10-16**) |
|
- 200 small GPT2-based transformers with 3 layers, 24 hidden units and 3 heads (1,252,464 parameters in each model), trained on LM1B (**LM1B-3-24**) |
|
- 200 small GPT2-based transformers with 2 layers, 32 hidden units and 2 heads (1,666,464 parameters in each model), trained on LM1B (**LM1B-2-32**) |
|
|
|
Each model contains multiple checkpoints: |
|
- 2688 checkpoints per each model in FM-16 (corresponding to every 4 steps of Adam) |
|
- 2513 checkpoints per each model in C10-16 (corresponding to every 4 steps of Adam) |
|
- 124 checkpoints per each model in LM1B-3-24 (corresponding to every 200 steps of Adam) |
|
- 124 checkpoints per each model in LM1B-2-32 (corresponding to every 200 steps of Adam) |
|
|
|
In total, there are 1,609,900 model checkpoints. |
|
|
|
The dataset also contains the training loss for each checkpoint, for FM-16 and C10-16 it also contains training accuracy, test loss, test accuracy. |
|
|
|
The dataset corresponds to the first 4 columns (in-distribution tasks) in Table 1 below. |
|
<img src="https://cdn-uploads.huggingface.co/production/uploads/63e16165f0039731dfdd442a/GNPCk3RCZqsar_eTOo388.png" width="800"> |
|
|
|
This Table is from the `Accelerating Training with Neuron Interaction and Nowcasting Networks` paper, see https://arxiv.org/abs/2409.04434 for details. |
|
|
|
|
