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
```

fun

by malv-c - opened Apr 12

Discussion

malv-c

Apr 12

File "/2/llm/m/DD/dm", line 2, in
model = AutoModelForCausalLM.from_pretrained("Zigeng/DMax-Math-16B", trust_remote_code=True, dtype="auto")
File "/home/void/.pyenv/versions/3.13.9/lib/python3.13/site-packages/transformers/models/auto/auto_factory.py", line 356, in from_pretrained
model_class = get_class_from_dynamic_module(
class_ref, pretrained_model_name_or_path, code_revision=code_revision, **hub_kwargs, **kwargs
)
File "/home/void/.pyenv/versions/3.13.9/lib/python3.13/site-packages/transformers/dynamic_module_utils.py", line 583, in get_class_from_dynamic_module
return get_class_in_module(class_name, final_module, force_reload=force_download)
File "/home/void/.pyenv/versions/3.13.9/lib/python3.13/site-packages/transformers/dynamic_module_utils.py", line 309, in get_class_in_module
module_spec.loader.exec_module(module)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^
File "", line 1027, in exec_module
File "", line 488, in _call_with_frames_removed
File "/home/void/.cache/huggingface/modules/transformers_modules/Zigeng/DMax_hyphen_Math_hyphen_16B/75d957038b906c71ec8e8b12a257be471df8b10e/modeling_llada2_moe.py", line 53, in
from transformers.utils.import_utils import is_torch_fx_available
ImportError: cannot import name 'is_torch_fx_available' from 'transformers.utils.import_utils' (/home/void/.pyenv/versions/3.13.9/lib/python3.13/site-packages/transformers/utils/import_utils.py)
3.13.9.pyenv:/2/llm/m/DD
%

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