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

Libraries

How to use wubingheng/Doge-20M-Chinese with Transformers:

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

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

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("wubingheng/Doge-20M-Chinese", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("wubingheng/Doge-20M-Chinese", trust_remote_code=True, 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use wubingheng/Doge-20M-Chinese with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/wubingheng/Doge-20M-Chinese

SGLang

How to use wubingheng/Doge-20M-Chinese 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 "wubingheng/Doge-20M-Chinese" \
    --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": "wubingheng/Doge-20M-Chinese",
		"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 "wubingheng/Doge-20M-Chinese" \
        --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": "wubingheng/Doge-20M-Chinese",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

wubingheng commited on Apr 15, 2025

Commit

0af1488

verified ·

1 Parent(s): da08ea2

Update README.md

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README.md CHANGED Viewed

@@ -42,7 +42,7 @@ Doge uses Dynamic Mask Attention as sequence transformation and can use Multi-La
 >>> tokenizer = AutoTokenizer.from_pretrained("wubingheng/Doge-20M-Chinese")
 >>> model = AutoModelForCausalLM.from_pretrained("wubingheng/Doge-20M-Chinese", 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))

 >>> 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))