Instructions to use SmallDoge/Doge-20M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SmallDoge/Doge-20M with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="SmallDoge/Doge-20M")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("SmallDoge/Doge-20M")
model = AutoModelForCausalLM.from_pretrained("SmallDoge/Doge-20M", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use SmallDoge/Doge-20M with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SmallDoge/Doge-20M"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SmallDoge/Doge-20M",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/SmallDoge/Doge-20M

SGLang

How to use SmallDoge/Doge-20M with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "SmallDoge/Doge-20M" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SmallDoge/Doge-20M",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "SmallDoge/Doge-20M" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SmallDoge/Doge-20M",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use SmallDoge/Doge-20M with Docker Model Runner:
```
docker model run hf.co/SmallDoge/Doge-20M
```

Doge-20M / README.md

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	---
	library_name: transformers
	license: apache-2.0
	datasets:
	- HuggingFaceTB/smollm-corpus
	language:
	- en
	pipeline_tag: text-generation
	tags:
	- pt
	- doge
	---


	# Doge 20M

	<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("SmallDoge/Doge-20M")
	>>> model = AutoModelForCausalLM.from_pretrained("SmallDoge/Doge-20M", trust_remote_code=True)
	>>> inputs = tokenizer("Hey how are you doing?", return_tensors="pt")

	>>> out = model.generate(**inputs, max_new_tokens=100)
	>>> print(tokenizer.batch_decode(out))
	```


	## Model Details

	We build the Doge by doing Per-Training on [Smollm-Corpus](https://huggingface.co/datasets/HuggingFaceTB/smollm-corpus). If you want to continue pre-training this model, you can find the unconverged checkpoint [here](https://huggingface.co/SmallDoge/Doge-60M-checkpoint). These models has not been fine-tuned for instruction, the instruction model is [here](https://huggingface.co/SmallDoge/Doge-60M-Instruct).


	Pre-Training:

	\| Model \| Training Data \| Steps \| Content Length \| Tokens \| LR \| Batch Size \| Precision \| RTX 4090 GPU hours \|
	\|---\|---\|---\|---\|---\|---\|---\|---\|---\|
	\| [Doge-20M](https://huggingface.co/SmallDoge/Doge-20M) \| [smollm-corpus](https://huggingface.co/datasets/HuggingFaceTB/smollm-corpus) \| 8k \| 2048 \| 4B \| 8e-3 \| 0.5M \| bfloat16 \| 14 \|
	\| [Doge-60M](https://huggingface.co/SmallDoge/Doge-60M) \| [smollm-corpus](https://huggingface.co/datasets/HuggingFaceTB/smollm-corpus) \| 16k \| 2048 \| 16B \| 6e-3 \| 1M \| bfloat16 \| 128 \|
	\| [Doge-160M](https://huggingface.co/SmallDoge/Doge-160M) \| [smollm-corpus](https://huggingface.co/datasets/HuggingFaceTB/smollm-corpus) \| 24k \| 2048 \| 32B \| 4e-3 \| 1.5M \| bfloat16 \| 522 \|
	\| [Doge-320M](https://huggingface.co/SmallDoge/Doge-320M) \| [smollm-corpus](https://huggingface.co/datasets/HuggingFaceTB/smollm-corpus) \| 32k \| 2048 \| 64B \| 2e-3 \| 2M \| bfloat16 \| 1856 \|

	Evaluation:

	\| Model \| MMLU \| TriviaQA \| ARC \| PIQA \| HellaSwag \| OBQA \| Winogrande \| tokens / s on i7-11 CPU \|
	\|---\|---\|---\|---\|---\|---\|---\|---\|---\|
	\| [Doge-20M](https://huggingface.co/SmallDoge/Doge-20M) \| 25.4 \| 0.03 \| 29.8 \| 58.4 \| 27.3 \| 25.6 \| 50.2 \| 142 \|
	\| [Doge-60M](https://huggingface.co/SmallDoge/Doge-60M) \| 26.4 \| 0.2 \| 37.9 \| 61.4 \| 31.5 \| 28.0 \| 50.8 \| 62 \|
	\| [Doge-160M](https://huggingface.co/SmallDoge/Doge-160M) \| 29.2 \| 4.8 \| 44.4 \| 70.1 \| 43.4 \| 34.4 \| 52.2 \| 28 \|
	\| [Doge-320M](https://huggingface.co/SmallDoge/Doge-320M) \| 35.6 \| 9.4 \| 55.4 \| 73.9 \| 52.7 \| 37.9 \| 59.3 \| 16 \|

	Procedure:

	[<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}
	}
	```