Instructions to use caiyuchen/DAPO-step-21 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use caiyuchen/DAPO-step-21 with Transformers:

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

pipe = pipeline("text-generation", model="caiyuchen/DAPO-step-21")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("caiyuchen/DAPO-step-21")
model = AutoModelForCausalLM.from_pretrained("caiyuchen/DAPO-step-21", 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 caiyuchen/DAPO-step-21 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "caiyuchen/DAPO-step-21"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "caiyuchen/DAPO-step-21",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/caiyuchen/DAPO-step-21

SGLang

How to use caiyuchen/DAPO-step-21 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 "caiyuchen/DAPO-step-21" \
    --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": "caiyuchen/DAPO-step-21",
		"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 "caiyuchen/DAPO-step-21" \
        --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": "caiyuchen/DAPO-step-21",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use caiyuchen/DAPO-step-21 with Docker Model Runner:
```
docker model run hf.co/caiyuchen/DAPO-step-21
```

caiyuchen commited on Oct 3, 2025

Commit

c8c218b

verified ·

1 Parent(s): 6bb4c20

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README.md CHANGED Viewed

@@ -17,7 +17,8 @@ base_model:
 ## 🔧 Prompt Format (Chat Template)
-During RL training and inference, each question is formatted as:
 {question} Please reason step by step, and put your final answer within boxed{}.
 Then wrapped using the chat template:
@@ -28,8 +29,7 @@ prompt = tokenizer.apply_chat_template(
     tokenize=False,
     add_generation_prompt=True,
 )
 ## 🧪 Example Usage
@@ -53,7 +53,7 @@ prompt = tokenizer.apply_chat_template(
 inputs = tokenizer(prompt, return_tensors="pt")
 outputs = model.generate(**inputs, max_new_tokens=256)
 print(tokenizer.decode(outputs[0], skip_special_tokens=True))
 ## 📎 Reference

 ## 🔧 Prompt Format (Chat Template)
+During Inference, each question is formatted as:
 {question} Please reason step by step, and put your final answer within boxed{}.
 Then wrapped using the chat template:
     tokenize=False,
     add_generation_prompt=True,
 )
+```
 ## 🧪 Example Usage
 inputs = tokenizer(prompt, return_tensors="pt")
 outputs = model.generate(**inputs, max_new_tokens=256)
 print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
 ## 📎 Reference