|
|
--- |
|
|
library_name: transformers |
|
|
license: apache-2.0 |
|
|
datasets: |
|
|
- wubingheng/Doge_PT_chinese |
|
|
language: |
|
|
- zh |
|
|
pipeline_tag: text-generation |
|
|
tags: |
|
|
- pt |
|
|
- doge |
|
|
--- |
|
|
|
|
|
|
|
|
# **Doge 20M CN** |
|
|
|
|
|
<div align="center"> |
|
|
<img src="https://huggingface.co/spaces/SmallDoge/README/resolve/main/org_icon.png" width="100%" alt="SmallDoge" /> |
|
|
</div> |
|
|
<hr> |
|
|
<div align="center"> |
|
|
<a href="https://discord.gg/P2yYH95N" target="_blank" style="margin: 2px;"> |
|
|
<img alt="Discord" src="https://img.shields.io/badge/Discord-Small%20Doges-7289da?logo=discord&logoColor=white&color=7289da" style="display: inline-block; vertical-align: middle;"/> |
|
|
</a> |
|
|
<!-- <a href="https://arxiv.org/abs/2412.11834" target="_blank" style="margin: 2px;"> |
|
|
<img alt="arXiv" src="https://img.shields.io/static/v1?label=arXiv&message=2412.11834&color=B31B1B&logo=arXiv" style="display: inline-block; vertical-align: middle;"/> |
|
|
</a> --> |
|
|
<a href="https://github.com/SmallDoges/small-doge" target="_blank" style="margin: 2px;"> |
|
|
<img alt="GitHub" src="https://img.shields.io/badge/GitHub-SmallDoge-181717?logo=github" style="display: inline-block; vertical-align: middle;"/> |
|
|
</a> |
|
|
<a href="https://github.com/SmallDoges/small-doge/blob/main/LICENSE" style="margin: 2px;"> |
|
|
<img alt="License" src="https://img.shields.io/badge/License-Apache--2.0-blue.svg" style="display: inline-block; vertical-align: middle;"/> |
|
|
</a> |
|
|
</div> |
|
|
|
|
|
Doge uses Dynamic Mask Attention as sequence transformation and can use Multi-Layer Perceptron or Cross Domain Mixture of Experts as state transformation. Dynamic Mask Attention allows the Transformer to use self-attention during training and state space during inference, and Cross Domain Mixture of Experts can directly inherit the weights of Multi-Layer Perceptron for further training. This model is trained by [SmallDoge](https://huggingface.co/SmallDoge) community, for detailed algorithm and model architecture, paper coming soon, all training details and code are available in the [small-doge](https://github.com/SmallDoges/small-doge) repository. |
|
|
|
|
|
## Uses |
|
|
|
|
|
```python |
|
|
>>> from transformers import AutoTokenizer, AutoModelForCausalLM |
|
|
|
|
|
>>> tokenizer = AutoTokenizer.from_pretrained("wubingheng/Doge-20M-Chinese") |
|
|
>>> model = AutoModelForCausalLM.from_pretrained("wubingheng/Doge-20M-Chinese", trust_remote_code=True) |
|
|
>>> inputs = tokenizer("你好", return_tensors="pt") |
|
|
|
|
|
>>> out = model.generate(**inputs, max_new_tokens=100) |
|
|
>>> print(tokenizer.batch_decode(out)) |
|
|
``` |
|
|
|
|
|
|
|
|
## Model Details |
|
|
|
|
|
|
|
|
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/loser_cheems/huggingface/runs/gopufefk?nw=nwuserbinghengwu) |
|
|
|
|
|
**Environment**: |
|
|
|
|
|
- Image: nvcr.io/nvidia/pytorch:24.12-py3 |
|
|
- Hardware: 1x NVIDIA RTX 4090 |
|
|
- Software: Transformers |
|
|
|
|
|
|
|
|
## Citation |
|
|
|
|
|
```bibtex |
|
|
@misc{smalldoges, |
|
|
title={SmallDoges: A Family of Dynamic UltraFast Small Language Models}, |
|
|
author={Jingze, Shi and Yifan, Wu and Bingheng, Wu and Yuyu, Luo}, |
|
|
year={2025}, |
|
|
month={March}, |
|
|
url={https://github.com/SmallDoges/small-doge} |
|
|
} |
|
|
``` |
