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- name: data
sequence: float16
- name: train_loss
dtype: float16
splits:
- name: '0'
num_bytes: 310611816
num_examples: 124
- name: '1'
num_bytes: 310611816
num_examples: 124
- name: '2'
num_bytes: 310611816
num_examples: 124
download_size: 874816124
dataset_size: 931835448
configs:
- config_name: c10-16
data_files:
- split: '0'
path: c10-16/0-*
- split: '1'
path: c10-16/1-*
- split: '2'
path: c10-16/2-*
- config_name: default
data_files:
- split: '0'
path: data/0-*
- split: '1'
path: data/1-*
- split: '2'
path: data/2-*
- config_name: fm-16
data_files:
- split: '0'
path: fm-16/0-*
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path: fm-16/1-*
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path: fm-16/2-*
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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.
This Table is from the Accelerating Training with Neuron Interaction and Nowcasting Networks paper, see https://arxiv.org/abs/2409.04434 for details.