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TinyLlama-1.1B-ProXMath
gair-prox/TinyLlama-1.1B-ProXMath
TinyLlama-1.1B-ProXMath is a machine learning model from gair-prox. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.
<p align="center" <img src="prox-teaser.png" </p
All-time downloads
231
Public
Parameters
1.1B
8.8 GB on disk
Likes
2
Public
Repo makeup
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.bin4.4 GB · 50%
At a glance
- License
- apache-2.0
- Model type
- llama
- Access
- Public
- Created
- Sep 16, 2024
- Updated
- Oct 10, 2024
- SHA
- 48b8c71b
Base models
Datasets
Model card
From the Hugging Face model README
TinyLlama-1.1B-ProXMath
<p align="center"> <img src="prox-teaser.png"> </p>ArXiv | Data: OpenWebMath-Pro | Code
TinyLlama-1.1B-ProXMath is a math-adapted TinyLlama-1.1B model that is continually pre-trained on OpenWebMath-Pro (a refined version by ProX) for 15B tokens.
Evaluations
ProX models are evaluated on 9 common math reasoning benchmarks.
| Model | asdiv | gsm8k | mathqa | mawps | minerva_math | mmlu_stem | sat_math | svamp | tabmwp | average |
|---|---|---|---|---|---|---|---|---|---|---|
| TinyLlama-1.1B | 18.0 | 2.8 | 14.6 | 20.2 | 3.2 | 16.3 | 21.9 | 10.9 | 12.5 | 13.4 |
| TinyLlama-1.1B-ProXMath | 41.9 | 9.0 | 15.6 | 56.9 | 5.6 | 26.8 | 31.2 | 23.8 | 22.2 | 25.7 |
Citation
@article{zhou2024programming,
title={Programming Every Example: Lifting Pre-training Data Quality like Experts at Scale},
author={Zhou, Fan and Wang, Zengzhi and Liu, Qian and Li, Junlong and Liu, Pengfei},
journal={arXiv preprint arXiv:2409.17115},
year={2024}
}
- Type
- llama
- License
- apache-2.0
- Languages
- en
- Created
- Sep 16, 2024
- Updated
- Oct 10, 2024