Instructions to use SJTU-DENG-Lab/MBD-Code-SDAR-8B-Chat-b32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SJTU-DENG-Lab/MBD-Code-SDAR-8B-Chat-b32 with Transformers:

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

pipe = pipeline("text-generation", model="SJTU-DENG-Lab/MBD-Code-SDAR-8B-Chat-b32", trust_remote_code=True, device_map="auto")
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-SDAR-8B-Chat-b32", trust_remote_code=True, dtype="auto", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use SJTU-DENG-Lab/MBD-Code-SDAR-8B-Chat-b32 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-SDAR-8B-Chat-b32"
# 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-SDAR-8B-Chat-b32",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/SJTU-DENG-Lab/MBD-Code-SDAR-8B-Chat-b32

SGLang

How to use SJTU-DENG-Lab/MBD-Code-SDAR-8B-Chat-b32 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-SDAR-8B-Chat-b32" \
    --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-SDAR-8B-Chat-b32",
		"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-SDAR-8B-Chat-b32" \
        --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-SDAR-8B-Chat-b32",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use SJTU-DENG-Lab/MBD-Code-SDAR-8B-Chat-b32 with Docker Model Runner:
```
docker model run hf.co/SJTU-DENG-Lab/MBD-Code-SDAR-8B-Chat-b32
```

Add model card, metadata, and paper/code links

by nielsr HF Staff - opened 16 days ago

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+32

-1

nielsr

16 days ago

This PR improves the model card for the Multi-Block Diffusion Language Model by:

Adding YAML metadata for license: mit, library_name: transformers, and pipeline_tag: text-generation.
Providing direct links to the official paper, project page, and code repositories (the training repository as well as the Diffulex inference engine).
Adding a brief summary of MBD-LMs and a BibTeX citation block.

Add model card, metadata, and paper/code linksfe6338aa

DrewJin0827 changed pull request status to merged 16 days ago

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