Instructions to use Zigeng/DMax-Math-16B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Zigeng/DMax-Math-16B with Transformers:

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

pipe = pipeline("text-generation", model="Zigeng/DMax-Math-16B", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("Zigeng/DMax-Math-16B", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Zigeng/DMax-Math-16B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Zigeng/DMax-Math-16B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Zigeng/DMax-Math-16B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Zigeng/DMax-Math-16B

SGLang

How to use Zigeng/DMax-Math-16B 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 "Zigeng/DMax-Math-16B" \
    --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": "Zigeng/DMax-Math-16B",
		"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 "Zigeng/DMax-Math-16B" \
        --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": "Zigeng/DMax-Math-16B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Zigeng/DMax-Math-16B with Docker Model Runner:
```
docker model run hf.co/Zigeng/DMax-Math-16B
```

Zigeng commited on 5 days ago

Commit

8e2572f

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1 Parent(s): 3eb6b5b

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@@ -23,9 +23,7 @@ pipeline_tag: text-generation
 </div>
 </div>
-This repository contains the weights for **DMax-Math-16B**, presented in the paper [DMax: Aggressive Parallel Decoding for dLLMs](https://huggingface.co/papers/2604.08302).
-DMax is a new paradigm for efficient diffusion language models (dLLMs) that mitigates error accumulation in parallel decoding, enabling aggressive decoding parallelism while preserving generation quality.
 ## 💪 Highlights

 </div>
 </div>
+DMax is a new paradigm for efficient diffusion language models (dLLMs) that enables aggressive decoding parallelism while preserving generation quality. This repository contains the **DMax-Math-16B** model, specialized for highly parallel math and reasoning tasks.
 ## 💪 Highlights