Instructions to use SJTU-DENG-Lab/MBD-Code-SDAR-8B-Chat-b4 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-b4 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-b4", 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-b4", 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-b4 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-b4"
# 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-b4",
		"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-b4

SGLang

How to use SJTU-DENG-Lab/MBD-Code-SDAR-8B-Chat-b4 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-b4" \
    --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-b4",
		"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-b4" \
        --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-b4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use SJTU-DENG-Lab/MBD-Code-SDAR-8B-Chat-b4 with Docker Model Runner:
```
docker model run hf.co/SJTU-DENG-Lab/MBD-Code-SDAR-8B-Chat-b4
```

Improve model card, add metadata and 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

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

This PR improves the model card for your repository by adding:

Metadata tags (pipeline_tag, library_name, and license) to make the model more discoverable on the Hub.
Direct links to your project page, GitHub repository, and Diffulex inference engine.
A brief introduction and bibtex citation information.

Please let me know if you'd like to make any adjustments!

Improve model card, add metadata and links018c1fb8

DrewJin0827 changed pull request status to merged 16 days ago

Upload images, audio, and videos by dragging in the text input, pasting, or clicking here.

Tap or paste here to upload images

· Sign up or log in to comment