Instructions to use SJTU-DENG-Lab/MBD-Code-LLaDA2-mini-DMax-16B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SJTU-DENG-Lab/MBD-Code-LLaDA2-mini-DMax-16B with Transformers:

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

pipe = pipeline("text-generation", model="SJTU-DENG-Lab/MBD-Code-LLaDA2-mini-DMax-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("SJTU-DENG-Lab/MBD-Code-LLaDA2-mini-DMax-16B", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use SJTU-DENG-Lab/MBD-Code-LLaDA2-mini-DMax-16B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SJTU-DENG-Lab/MBD-Code-LLaDA2-mini-DMax-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": "SJTU-DENG-Lab/MBD-Code-LLaDA2-mini-DMax-16B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/SJTU-DENG-Lab/MBD-Code-LLaDA2-mini-DMax-16B

SGLang

How to use SJTU-DENG-Lab/MBD-Code-LLaDA2-mini-DMax-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 "SJTU-DENG-Lab/MBD-Code-LLaDA2-mini-DMax-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": "SJTU-DENG-Lab/MBD-Code-LLaDA2-mini-DMax-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 "SJTU-DENG-Lab/MBD-Code-LLaDA2-mini-DMax-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": "SJTU-DENG-Lab/MBD-Code-LLaDA2-mini-DMax-16B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use SJTU-DENG-Lab/MBD-Code-LLaDA2-mini-DMax-16B with Docker Model Runner:
```
docker model run hf.co/SJTU-DENG-Lab/MBD-Code-LLaDA2-mini-DMax-16B
```

Add model card metadata, links to paper, code, and project page

by nielsr HF Staff - opened 5 days ago

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+31

-1

nielsr

5 days ago

This PR improves the model card by:

Adding YAML metadata including license: mit, library_name: transformers, and pipeline_tag: text-generation.
Linking the model to the paper: Multi-Block Diffusion Language Models.
Providing links to the official code repository, project page, and citation block.

Add model card metadata, links to paper, code, and project page7c13e28e

DrewJin0827 changed pull request status to merged 5 days ago

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