Instructions to use decula/sd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use decula/sd with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3.5-9B")
model = PeftModel.from_pretrained(base_model, "decula/sd")

Transformers

How to use decula/sd with Transformers:

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

pipe = pipeline("text-generation", model="decula/sd")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("decula/sd", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use decula/sd with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "decula/sd"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "decula/sd",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/decula/sd

SGLang

How to use decula/sd 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 "decula/sd" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "decula/sd",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "decula/sd" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "decula/sd",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use decula/sd with Docker Model Runner:
```
docker model run hf.co/decula/sd
```

sd / 7b_rag.py

Commit History

changed 2 gpu t4

43735ce

decula commited on Jun 30, 2025

Added agent head

4c45781

decula commited on Jun 30, 2025

Added stop button

4f26428

decula commited on Jun 30, 2025

add rag serach

48f4118

decula commited on Jun 29, 2025

7b_rag.py

0396b0d

decula commited on Jun 29, 2025

Commit History

changed 2 gpu t4 43735ce

Added agent head 4c45781

Added stop button 4f26428

add rag serach 48f4118

7b_rag.py 0396b0d

changed 2 gpu t4

43735ce

Added agent head

4c45781

Added stop button

4f26428

add rag serach

48f4118

7b_rag.py

0396b0d