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

Libraries

How to use yresearch/DMax-Math-MMD with Transformers:

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

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

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("yresearch/DMax-Math-MMD", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use yresearch/DMax-Math-MMD with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/yresearch/DMax-Math-MMD

SGLang

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

Docker Model Runner
How to use yresearch/DMax-Math-MMD with Docker Model Runner:
```
docker model run hf.co/yresearch/DMax-Math-MMD
```

Add community evaluation results for GSM8K

by nielsr HF Staff - opened 2 days ago

base: refs/heads/main

←

from: refs/pr/2

Discussion Files changed

-0

nielsr

2 days ago

•

edited 2 days ago

This PR adds community-provided evaluation results for the following benchmarks:

GSM8K

These results were extracted from the model card. This is based on the new evaluation results feature.

Note: This is an automated PR. Please review the evaluation results before merging.

Add community evaluation results for GSM8Kff52ae92

nielsr changed pull request title from Add community evaluation results for GSM8K to Add community evaluation results for GSM8K 2 days ago

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