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Models Trained with Random Mixture

This is a collection of 64 language models, each with approximately 1B parameters, trained on different random mixtures of data. This project aims to validate the generalization capabilities of the RegMix approach (https://huggingface.co/papers/2407.01492) from small-scale (e.g., 1M parameters) to large-scale (e.g., 1B parameters) models.

Key Features

  • Model Size: 64 separate models, each with ~1B parameters
  • Training Data: Random data mixtures on the RegMix-Data dataset
  • Purpose: To validate the effectiveness of RegMix on identifying high-performing data mixture

Dataset

The models were trained using the RegMix-Data dataset, which is split into different domains from The Pile dataset.

Training Hyperparameters

Hyperparameter Value
Batch Size 1M tokens
Learning Rate 4e-4
Minimum Learning Rate 1e-5
Learning Rate Schedule Cosine
Warmup Ratio 4%
Total Tokens 25B

How to Load a Model

You can load any model using the corresponding branch with the Hugging Face Transformers library:

from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("sail/data-mixture-random-1b", revision="model-index-1")
tokenizer = AutoTokenizer.from_pretrained("sail/data-mixture-random-1b", revision="model-index-1")

Data Mixture

The specific data mixture used for training each 1B model can be found in the file train_config.yaml in each corresponding model branch.

Model Variants

To access different model variants, simply change the revision parameter in the from_pretrained method to the desired model index (e.g., "model-index-2", "model-index-3"), and the maxium index is 64.

Usage Notes

  • These models are primarily intended for research purposes.
  • Performance may vary depending on the specific task and domain.

Citation

If you use these models in your research, please cite the RegMix paper:

@article{liu2024regmix,
  title={RegMix: Data Mixture as Regression for Language Model Pre-training},
  author={Liu, Qian and Zheng, Xiaosen and Muennighoff, Niklas and Zeng, Guangtao and Dou, Longxu and Pang, Tianyu and Jiang, Jing and Lin, Min},
  journal={arXiv preprint arXiv:2407.01492},
  year={2024}
}

For more information about the RegMix methodology and its applications, please refer to the original paper.

Performance

We evaluated each model using lm-evaluation-harness. The performance metric for each task is the average of 0-shot to 5-shot accnorm (accuracy normalized, if available) or acc (accuracy) scores.

Table 1: Model Index 1-8

Task Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8
Social IQA 33.27 33.33 33.62 33.53 33.49 33.56 33.62 33.55
HellaSwag 40.58 36.86 40.58 36.06 40.07 37.85 37.93 39.59
PiQA 67.29 65.14 67.97 64.66 67.03 65.36 66.00 66.55
OpenBookQA 28.63 27.87 29.33 29.10 29.23 28.33 29.13 28.73
Lambada 29.17 26.86 31.55 27.11 29.16 28.92 31.53 30.92
SciQ 80.68 79.98 81.05 80.80 82.40 79.88 78.67 79.70
COPA 70.50 63.83 69.17 65.00 67.50 66.00 66.67 68.67
RACE 29.47 30.00 32.11 28.82 31.13 30.06 29.90 30.75
ARC Easy 50.03 48.72 50.01 46.64 51.06 47.46 46.75 48.39
LogiQA 23.76 24.17 25.29 25.29 24.55 25.96 25.45 26.32
QQP 55.71 55.90 54.84 56.52 54.01 56.34 52.35 54.20
WinoGrande 51.54 51.59 51.39 50.91 53.13 52.26 51.26 51.45
MultiRC 52.65 53.39 51.89 50.92 49.03 53.09 53.64 50.23
Average 47.18 45.97 47.60 45.80 47.06 46.54 46.38 46.85

Table 2: Model Index 9-16

Task Model 9 Model 10 Model 11 Model 12 Model 13 Model 14 Model 15 Model 16
Social IQA 33.43 33.21 33.31 33.17 33.28 32.43 33.57 33.70
HellaSwag 40.05 35.89 39.55 39.89 38.63 36.18 39.52 35.94
PiQA 66.60 64.74 66.29 66.27 66.90 64.05 66.70 64.51
OpenBookQA 28.87 26.60 29.33 28.73 29.40 27.87 29.67 27.83
Lambada 31.39 27.37 30.32 30.31 31.38 26.25 29.86 26.95
SciQ 81.10 79.12 79.97 82.85 79.42 81.40 81.38 81.23
COPA 67.00 64.50 66.83 69.50 67.33 65.83 69.50 66.33
RACE 30.57 29.63 30.49 30.85 30.35 28.66 31.21 29.57
ARC Easy 50.66 47.74 47.47 50.18 49.92 49.52 50.73 48.65
LogiQA 23.60 25.65 26.37 23.81 25.58 26.29 25.86 25.12
QQP 54.89 54.79 54.20 55.23 53.69 57.09 53.95 54.24
WinoGrande 50.83 51.84 51.05 51.83 52.12 52.00 51.01 51.82
MultiRC 54.18 54.48 50.17 52.12 51.42 52.69 51.87 53.48
Average 47.17 45.81 46.57 47.29 46.88 46.17 47.30 46.11

Table 3: Model Index 17-24

Task Model 17 Model 18 Model 19 Model 20 Model 21 Model 22 Model 23 Model 24
Social IQA 33.89 33.31 33.53 33.38 33.75 33.24 33.56 33.71
HellaSwag 38.68 39.90 34.67 37.12 37.44 36.07 42.15 34.67
PiQA 66.83 67.39 63.33 64.83 65.00 63.68 67.80 62.99
OpenBookQA 28.13 30.67 28.03 29.40 27.67 27.77 29.37 25.83
Lambada 28.78 28.56 24.13 29.41 27.67 28.03 33.47 24.04
SciQ 79.60 78.83 77.42 78.98 78.95 78.72 81.83 79.12
COPA 65.17 68.17 65.33 67.33 67.67 62.67 69.83 65.83
RACE 28.74 30.03 29.76 29.49 30.77 29.76 31.21 27.91
ARC Easy 48.86 49.42 47.90 48.30 47.88 46.68 50.92 45.24
LogiQA 25.91 26.34 26.24 25.76 26.11 26.24 24.17 25.91
QQP 53.35 53.18 50.61 51.49 54.27 54.99 52.77 55.19
WinoGrande 52.54 51.17 52.01 51.09 52.13 52.03 52.50 50.28
MultiRC 51.49 52.45 55.40 54.87 51.73 49.49 50.61 50.29
Average 46.30 46.88 45.26 46.27 46.23 45.34 47.71 44.69

Table 4: Model Index 25-32

Task Model 25 Model 26 Model 27 Model 28 Model 29 Model 30 Model 31 Model 32
Social IQA 33.51 33.40 33.59 33.52 33.53 33.49 33.16 33.56
HellaSwag 36.75 36.97 40.81 38.25 40.28 35.71 37.37 37.39
PiQA 64.09 64.74 67.97 66.15 66.88 63.84 64.47 65.05
OpenBookQA 29.47 28.70 29.57 29.77 29.50 29.13 29.47 28.00
Lambada 26.69 33.00 31.60 33.08 31.49 27.69 26.99 29.54
SciQ 80.03 79.17 80.12 80.22 81.92 78.23 77.42 80.87
COPA 67.67 65.50 69.00 65.67 68.33 63.33 64.67 67.17
RACE 30.05 30.19 30.96 30.37 30.08 29.62 30.13 29.92
ARC Easy 47.50 46.90 50.26 48.57 50.55 46.96 48.77 48.79
LogiQA 27.24 25.55 25.86 24.37 25.32 25.12 26.40 24.30
QQP 49.68 55.43 50.94 50.91 51.99 53.53 49.53 51.36
WinoGrande 51.68 52.12 51.93 51.50 52.32 51.67 52.13 52.63
MultiRC 51.24 51.91 50.33 52.42 52.52 54.04 52.05 53.04
Average 45.82 46.43 47.15 46.52 47.29 45.57 45.58 46.28

Table 5: Model Index 33-40

Task Model 33 Model 34 Model 35 Model 36 Model 37 Model 38 Model 39 Model 40
Social IQA 33.48 33.28 33.35 33.29 33.63 33.61 33.21 33.61
HellaSwag 38.00 40.18 43.37 37.69 32.96 32.98 37.31 37.79
PiQA 65.30 66.68 69.04 66.46 62.25 60.17 65.24 65.32
OpenBookQA 29.43 30.37 30.43 27.63 26.43 26.83 27.97 28.70
Lambada 26.59 31.46 31.71 30.21 18.92 20.29 28.10 28.58
SciQ 79.82 80.58 82.13 80.83 76.73 77.90 79.12 79.60
COPA 64.33 69.33 67.00 67.83 61.50 62.67 64.67 66.00
RACE 30.03 30.16 32.47 30.49 29.27 28.12 30.11 30.21
ARC Easy 48.86 49.88 52.22 48.32 44.86 45.54 48.15 48.86
LogiQA 25.91 24.30 23.35 24.96 26.19 27.68 25.47 25.37
QQP 56.06 56.56 52.57 56.70 52.54 48.04 49.81 57.12
WinoGrande 50.92 50.97 52.39 52.70 52.30 51.68 51.42 52.80
MultiRC 53.09 49.97 52.18 49.05 53.78 52.27 51.45 55.68
Average 46.29 47.21 47.86 46.63 43.95 43.67 45.54 46.90

Table 6: Model Index 41-48

Task Model 41 Model 42 Model 43 Model 44 Model 45 Model 46 Model 47 Model 48
Social IQA 33.49 33.43 33.07 33.28 33.44 33.08 33.78 33.17
HellaSwag 34.51 37.59 42.69 37.37 38.31 38.30 39.67 41.07
PiQA 62.24 65.58 68.05 66.62 66.54 65.52 66.98 67.21
OpenBookQA 27.10 28.77 28.90 28.07 28.07 27.60 31.17 29.73
Lambada 22.78 26.99 31.34 29.51 27.87 29.47 30.34 32.71
SciQ 77.78 80.25 79.47 80.25 80.70 79.72 81.35 81.77
COPA 64.00 66.33 67.00 67.00 67.33 68.33 67.17 67.67
RACE 28.33 28.82 30.78 30.80 30.08 30.24 30.24 30.67
ARC Easy 45.48 48.64 51.49 46.99 48.79 48.05 49.58 49.49
LogiQA 24.83 24.96 24.76 23.25 26.06 25.55 24.32 24.68
QQP 50.27 54.73 53.96 57.00 53.73 51.19 57.52 56.91
WinoGrande 51.79 51.63 51.32 50.76 53.18 52.45 50.72 52.24
MultiRC 54.03 53.96 48.91 50.74 53.01 50.89 47.63 53.84
Average 44.35 46.28 47.06 46.28 46.70 46.18 46.96 47.78

Table 7: Model Index 49-56

Task Model 49 Model 50 Model 51 Model 52 Model 53 Model 54 Model 55 Model 56
Social IQA 33.53 33.74 33.37 33.41 32.96 33.88 33.75 33.79
HellaSwag 39.09 35.65 38.68 36.07 37.68 38.53 35.40 40.50
PiQA 66.81 64.58 65.68 63.99 65.85 65.76 64.51 66.89
OpenBookQA 29.13 27.57 28.27 29.10 29.43 28.73 28.30 29.87
Lambada 30.23 26.19 30.29 30.84 29.76 29.03 28.63 30.74
SciQ 79.90 80.83 78.40 80.03 81.38 80.92 77.75 82.07
COPA 68.17 61.83 67.00 66.00 66.17 63.17 66.33 64.00
RACE 31.42 29.35 30.41 31.08 30.77 29.73 30.80 31.42
ARC Easy 49.54 47.71 49.02 47.64 48.38 49.36 46.96 51.22
LogiQA 24.99 24.58 25.32 24.91 25.17 26.22 24.63 24.91
QQP 54.06 56.48 50.96 56.62 56.45 53.86 53.85 53.26
WinoGrande 50.51 50.26 51.83 51.33 52.18 51.89 51.59 50.50
MultiRC 50.25 54.37 50.94 52.38 51.21 55.34 54.52 50.50
Average 46.74 45.63 46.17 46.42 46.72 46.65 45.92 46.90

Table 8: Model Index 57-64

Task Model 57 Model 58 Model 59 Model 60 Model 61 Model 62 Model 63 Model 64
Social IQA 33.24 33.30 33.56 33.54 33.42 33.84 33.32 33.55
HellaSwag 41.74 39.63 35.36 38.83 38.53 36.46 38.80 36.43
PiQA 68.07 67.31 64.44 66.38 66.50 64.74 66.54 64.87
OpenBookQA 29.20 29.50 28.10 27.97 27.83 27.37 28.83 27.87
Lambada 31.79 31.11 27.32 30.17 28.75 26.22 30.38 26.25
SciQ 80.42 79.83 80.85 79.60 78.93 80.05 79.50 78.65
COPA 66.17 69.00 64.00 64.83 67.00 64.00 66.00 66.83
RACE 31.39 29.82 29.67 30.08 29.98 29.46 30.37 29.19
ARC Easy 51.14 49.24 47.13 47.88 48.20 47.09 49.09 46.90
LogiQA 25.19 25.93 23.68 25.17 25.70 25.52 26.50 26.65
QQP 55.37 54.46 52.73 53.17 59.65 58.15 57.50 55.31
WinoGrande 53.21 51.46 50.83 52.16 52.37 51.41 51.63 51.85
MultiRC 53.58 52.31 52.22 53.03 50.41 52.17 52.27 51.50
Average 47.73 47.15 45.38 46.37 46.71 45.88 46.98 45.84
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Datasets used to train sail/data-mixture-random-1b

Collection including sail/data-mixture-random-1b