Models Trained with DoReMi Data Mixture
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 dataset
- Purpose: The DoReMi is a flagship method for automatic data mxiture
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-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:
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. 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:
@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.
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