---
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 DoReMi data mxiture, each with approximately 1B parameters, trained on different random mixtures of data. This models aims to server as the strong baseline for our RegMix approach (https://huggingface.co/papers/2407.01492).

- **Model Size**: 5 separate models trained with different seeds, each with ~1B parameters
- **Training Data**: DoReMi 280M proxy model (Xie et al. 2023) data mixtures on the [RegMix-Data](https://huggingface.co/datasets/sail/regmix-data) dataset
- **Purpose**: The DoReMi is a flagship method for automatic data mxiture
- 
## 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-doremi-1b", revision="seed-1")
tokenizer = AutoTokenizer.from_pretrained("sail/data-mixture-doremi-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/doremi.yaml):

```yaml
train:
  train_the_pile_arxiv: 0.0036
  train_the_pile_freelaw: 0.0043
  train_the_pile_nih_exporter: 0.0063
  train_the_pile_pubmed_central: 0.0046
  train_the_pile_wikipedia_en: 0.0699
  train_the_pile_dm_mathematics: 0.0018
  train_the_pile_github: 0.0179
  train_the_pile_philpapers: 0.0274
  train_the_pile_stackexchange: 0.0153
  train_the_pile_enron_emails: 0.0070
  train_the_pile_gutenberg_pg_19: 0.0072
  train_the_pile_pile_cc: 0.6057
  train_the_pile_ubuntu_irc: 0.0093
  train_the_pile_europarl: 0.0062
  train_the_pile_hackernews: 0.0134
  train_the_pile_pubmed_abstracts: 0.0113
  train_the_pile_uspto_backgrounds: 0.0036
valid:
  valid_the_pile_pile_cc: 1.0
model_name: tinyllama_1_1b
```

> The domain weights will be renormalized in the code to make sure the sum of them to be 1.0.

## 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    | 68.27 | 32.08 | 53.82 | 26.42 | 33.35 | 52.17 | 31.31 | 30.33 | 68.50 | 43.41 | 81.63 | 50.60 | 56.57 | 48.34 |
| 2    | 68.07 | 32.93 | 51.34 | 26.02 | 33.12 | 52.58 | 31.23 | 30.16 | 70.60 | 43.73 | 84.30 | 52.69 | 59.68 | 48.96 |
| 3    | 68.79 | 33.26 | 52.03 | 24.70 | 33.18 | 52.04 | 30.87 | 29.72 | 65.80 | 43.09 | 84.56 | 53.53 | 56.67 | 48.33 |
| 4    | 68.80 | 31.45 | 54.03 | 25.16 | 33.14 | 51.63 | 31.06 | 29.68 | 72.80 | 43.19 | 85.20 | 52.68 | 56.24 | 48.85 |
| 5    | 68.88 | 32.51 | 53.17 | 25.22 | 33.58 | 52.15 | 31.27 | 30.08 | 71.00 | 43.15 | 81.02 | 51.96 | 57.57 | 48.58 |

## 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).