---
datasets:
- togethercomputer/RedPajama-Data-1T
language:
- en
library_name: transformers
license: apache-2.0
pipeline_tag: text-generation
---

## PDS-1.7B

[paper](https://arxiv.org/abs/2410.07064) | [code](https://github.com/microsoft/LMOps/tree/main/data_selection)

**PDS-1.7B** is a 1.7B model with [Mistral](https://arxiv.org/abs/2310.06825) achitecture pre-trained from scratch on the data selected from the CC split of [Redpajama](https://github.com/togethercomputer/RedPajama-Data), using the PDS framework.

This work investigates the selection of high-quality pre-training data from massive corpora to enhance LMs' capabilities for downstream usage.
We formulate data selection as a generalized Optimal Control problem, which can be solved theoretically by Pontryagin's Maximum Principle (PMP), yielding a set of necessary conditions that characterize the relationship between optimal data selection and LM training dynamics. Based on these theoretical results, we introduce PMP-based Data Selection (PDS), a framework that approximates optimal data selection by solving the PMP conditions.

Please refer to our [paper](https://arxiv.org/abs/2410.07064) for more details.

### Overview of the theory:

<p align='left'>
    <img src="https://cdn-uploads.huggingface.co/production/uploads/624ac662102fcdff87be51b9/Hdw83Vsb305GRlsqB7c34.png" width="700">
</p>

### Overview of the PDS framework:

<p align='left'>
    <img src="https://cdn-uploads.huggingface.co/production/uploads/624ac662102fcdff87be51b9/YPwluLyZGK7DACH1WqDUN.png" width="700">
</p>

### Evaluation

PDS-selected data improves the performance of language models pre-trained from scratch and saves pre-training comptation. The improvement scales up to large model sizes.

<p align='left'>
    <img src="https://cdn-uploads.huggingface.co/production/uploads/624ac662102fcdff87be51b9/6undIr37d10qD73TDiPDK.png" width="600">
</p>

### Baseline

[Conventional Pre-training](https://huggingface.co/Data-Selection/BSL-1.7B)

### Sample Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "Data-Selection/PDS-1.7B"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

inputs = tokenizer("Hello, my name is", return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

### Citation

```bibtex
@article{gu2024data,
  title={Data Selection via Optimal Control for Language Models},
  author={Gu, Yuxian and Dong, Li and Wang, Hongning and Hao, Yaru and Dong, Qingxiu and Wei, Furu and Huang, Minlie},
  journal={arXiv preprint arXiv:2410.07064},
  year={2024}
}
```