Instructions to use SJTU-DENG-Lab/MBD-Code-LLaDA2-mini-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-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-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-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-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-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-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-16B

SGLang

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

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

Add model card metadata, links and citation

by nielsr HF Staff - opened 6 days ago

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+32

-1

nielsr

6 days ago

Hi! I'm Niels from the Hugging Face team.

I noticed this model repository is missing metadata and a complete model card. This PR aims to improve the model's discoverability and documentation on the Hugging Face Hub by:

Adding YAML metadata including pipeline_tag: text-generation, library_name: transformers, and license: mit.
Documenting the model with references and links to the paper, project page, and official GitHub repository.
Adding a BibTeX citation for the paper so users can easily cite your work.

Please let me know if you have any feedback or would like to make changes.

Add model card metadata, links and citation76324641

DrewJin0827 changed pull request status to merged 6 days ago

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