---
library_name: transformers
datasets:
- WebOrganizer/Corpus-200B
---
# WebOrganizer/LM-1b_1x-Sampling_over_KMeans_for_MMLU

[[Paper](https://arxiv.org/abs/2502.10341)] [[Website](https://weborganizer.allenai.org)] [[GitHub](https://github.com/CodeCreator/WebOrganizer)]

A 1.4B parameter model trained for 29B tokens from [WebOrganizer/Corpus-200B](https://huggingface.co/datasets/WebOrganizer/Corpus-200B). 

The training data for this model was selected via:
1. **Selection method**: Random sampling
2. **Domain definition**: 24 KMeans Clusters
3. **Domain mixture**: MMLU


## Repository Contents

Besides the HuggingFace model and tokenizer, the repository contains:
- `open_lm/`: Contains the OpenLM config and final checkpoint
- `evals/`: Evaluation results for various benchmarks
  - `core_9mcqa/`: Results of 9 multiple choice QA tasks with the OLMES evaluation framework
  - `mmlu/`: MMLU results with the OLMES evaluation framework
  - `dclm/`: Results using the DCLM evaluation framework
  - `perplexity/`: Perplexity results using the huggingface trainer
- `indices.tar.zst`: The indices for the selected documents in each shard of the Corpus-200B dataset used for training. The indices can be extracted with `tar --use-compress-program "zstd" -xf indices.tar.zst`.

## Usage

To use this model, you need to install the [open_lm](https://github.com/mlfoundations/open_lm) library and add `from open_lm.hf import *` before loading the model with `AutoModel.from_pretrained(...)`.


## Citation
```bibtex
@article{wettig2025organize,
  title={Organize the Web: Constructing Domains Enhances Pre-Training Data Curation},
  author={Alexander Wettig and Kyle Lo and Sewon Min and Hannaneh Hajishirzi and Danqi Chen and Luca Soldaini},
  journal={arXiv preprint arXiv:2502.10341},
  year={2025}
}
```