---
license: mit
datasets:
- sail/regmix-data
- sail/regmix-data-sample
language:
- en
tags:
- regmix
---


# Models Trained with Human Selection

This is a collection of the language models trained using Pile-CC, each with approximately 1B parameters, trained on different seeds. 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**: 5 separate models trained with different seeds, each with ~1B parameters
- **Training Data**: The pile-cc only data mixture on the [RegMix-Data](https://huggingface.co/datasets/sail/regmix-data) dataset

## Dataset

The models were trained using the [RegMix-Data](https://huggingface.co/datasets/sail/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:

```python
from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("sail/data-mixture-pile-cc-1b", revision="seed-1")
tokenizer = AutoTokenizer.from_pretrained("sail/data-mixture-pile-cc-1b", revision="seed-1")
```

## Data Mixture

The specific data mixture used for training this 1B model is as follows, which can be also found in [our code](https://github.com/sail-sg/regmix/blob/main/mixture_config/config_1b/human.yaml):

```yaml
train:
  train_the_pile_pile_cc: 1.0
valid:
  valid_the_pile_pile_cc: 1.0
model_name: tinyllama_1_1b
```

## Model Variants

To access different model variants, simply change the `revision` parameter in the `from_pretrained` method to the desired seed (e.g., "seed-2", "seed-3"), and the maxium seed is 5.

## Model Performance

We evaluated each model using [lm-evaluation-harness](https://github.com/EleutherAI/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.

| Seed | PIQA | LAMBADA | MultiRC | LogiQA | SocialIQA | Winogrande | RACE | OpenBookQA | COPA | HellaSwag | SciQ | ARC Easy | QQP | Average |
|------|------|---------|---------|--------|-----------|------------|------|------------|------|-----------|------|----------|-----|---------|
| 1    | 69.23 | 33.16 | 50.33 | 27.57 | 33.22 | 52.10 | 31.80 | 31.07 | 65.83 | 44.15 | 81.77 | 51.80 | 57.04 | 48.39 |
| 2    | 68.62 | 33.69 | 53.15 | 25.13 | 32.96 | 51.24 | 31.06 | 30.84 | 69.80 | 43.28 | 83.18 | 52.00 | 58.06 | 48.69 |
| 3    | 69.04 | 35.68 | 52.38 | 26.36 | 33.45 | 51.95 | 30.83 | 30.16 | 66.80 | 42.80 | 83.32 | 51.57 | 57.69 | 48.62 |
| 4    | 69.35 | 33.56 | 50.01 | 26.24 | 33.62 | 50.99 | 31.81 | 30.44 | 65.60 | 43.00 | 83.00 | 52.33 | 56.14 | 48.16 |
| 5    | 67.91 | 35.09 | 49.93 | 27.50 | 33.90 | 52.85 | 31.77 | 30.04 | 69.40 | 42.62 | 80.94 | 51.25 | 61.03 | 48.79 |


## 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:

```
@misc{liu2024regmix,
      title={RegMix: Data Mixture as Regression for Language Model Pre-training}, 
      author={Qian Liu and Xiaosen Zheng and Niklas Muennighoff and Guangtao Zeng and Longxu Dou and Tianyu Pang and Jing Jiang and Min Lin},
      year={2024},
      eprint={2407.01492},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2407.01492}, 
}
```

For more information about the RegMix methodology and its applications, please refer to the [original paper](https://huggingface.co/papers/2407.01492).