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

Libraries

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

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

pipe = pipeline("text-generation", model="yresearch/DMax-Coder-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-Coder-MMD", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

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

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "yresearch/DMax-Coder-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-Coder-MMD",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

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

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

free001style commited on 3 days ago

Commit

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Add license, paper and code badges to model card

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@@ -15,6 +15,8 @@ tags:
 # DMax-Coder-MMD
 **Representation-Space MMD for Diffusion Language Models**
 DMax-Coder-MMD is a 16B diffusion language model for code generation,
@@ -27,9 +29,6 @@ model samples and reference responses, measured in the representation space of
 a frozen diffusion language model. The project reports increased tokens per
 forward pass while maintaining or improving accuracy on the benchmarks below.
-[Project code and documentation](https://github.com/yandex-research/dlm-mmd)
-· [DMax-Math-MMD](https://huggingface.co/yresearch/DMax-Math-MMD)
 ## Reference results
 Results reported in the project README, with decoding threshold **0.9**.

 # DMax-Coder-MMD
+[![License: Apache-2.0](https://img.shields.io/badge/License-Apache--2.0-blue.svg)](https://www.apache.org/licenses/LICENSE-2.0) ![Paper: coming soon](https://img.shields.io/badge/Paper-coming%20soon-b31b1b.svg) [![Code: GitHub](https://img.shields.io/badge/Code-GitHub-181717.svg?logo=github)](https://github.com/yandex-research/dlm-mmd)
 **Representation-Space MMD for Diffusion Language Models**
 DMax-Coder-MMD is a 16B diffusion language model for code generation,
 a frozen diffusion language model. The project reports increased tokens per
 forward pass while maintaining or improving accuracy on the benchmarks below.
 ## Reference results
 Results reported in the project README, with decoding threshold **0.9**.