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")
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

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
```

JingzeShi commited on Feb 1, 2025

Commit

1ccf95f

verified ·

1 Parent(s): 91a2823

Update README.md

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README.md CHANGED Viewed

@@ -19,18 +19,18 @@ pipeline_tag: text-generation
   <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/SamllDoge/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://huggingface.co/SmallDoge" target="_blank" style="margin: 2px;">
     <img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20HuggingFace-SmallDoge-ffc107?color=ffc107&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
   </a>
-  <a href="https://github.com/SamllDoge/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, please refer to [Wonderful Matrices](https://arxiv.org/abs/2412.11834), all training details and code are publicly available on the [small-doge](https://github.com/SamllDoge/small-doge) repository.
 ## Uses

   <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://huggingface.co/SmallDoge" target="_blank" style="margin: 2px;">
     <img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20HuggingFace-SmallDoge-ffc107?color=ffc107&logoColor=white" 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, please refer to [Wonderful Matrices](https://arxiv.org/abs/2412.11834), all training details and code are publicly available on the [small-doge](https://github.com/SmallDoges/small-doge) repository.
 ## Uses