Instructions to use Vortex5/Luminous-Shadow-12B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Vortex5/Luminous-Shadow-12B with Transformers:

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

pipe = pipeline("text-generation", model="Vortex5/Luminous-Shadow-12B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Vortex5/Luminous-Shadow-12B")
model = AutoModelForCausalLM.from_pretrained("Vortex5/Luminous-Shadow-12B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Vortex5/Luminous-Shadow-12B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Vortex5/Luminous-Shadow-12B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Vortex5/Luminous-Shadow-12B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Vortex5/Luminous-Shadow-12B

SGLang

How to use Vortex5/Luminous-Shadow-12B 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 "Vortex5/Luminous-Shadow-12B" \
    --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": "Vortex5/Luminous-Shadow-12B",
		"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 "Vortex5/Luminous-Shadow-12B" \
        --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": "Vortex5/Luminous-Shadow-12B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Vortex5/Luminous-Shadow-12B with Docker Model Runner:
```
docker model run hf.co/Vortex5/Luminous-Shadow-12B
```

Quick review

by MrDevolver - opened Oct 29, 2025

Discussion

MrDevolver

Oct 29, 2025

Hello,

I've been testing this model with one of my favorite characters and it's a beast. Took me some time to bend it to my will with parameters, but it seems to be a very stable model even if you go wild with certain parameters to push creativity. It feels pretty stable even with far fetched twists without losing track of what's what and I love it. I wish you could push it even further to kinda make it more obedient in terms of following the user's instructions and expectations in more creative ways, maybe further training and/or merging would do the trick in that department, but given the model's stability when dealing with extreme scenarios, I believe there's still room for allowing more creativity.

Good job so far!

Vortex5

Owner Oct 29, 2025

Hello,

I've been testing this model with one of my favorite characters and it's a beast. Took me some time to bend it to my will with parameters, but it seems to be a very stable model even if you go wild with certain parameters to push creativity. It feels pretty stable even with far fetched twists without losing track of what's what and I love it. I wish you could push it even further to kinda make it more obedient in terms of following the user's instructions and expectations in more creative ways, maybe further training and/or merging would do the trick in that department, but given the model's stability when dealing with extreme scenarios, I believe there's still room for allowing more creativity.

Good job so far!

Thanks for the feedback, try setting the temp to around 0.8 that could help.

MrDevolver

Oct 29, 2025

Hello,

I've been testing this model with one of my favorite characters and it's a beast. Took me some time to bend it to my will with parameters, but it seems to be a very stable model even if you go wild with certain parameters to push creativity. It feels pretty stable even with far fetched twists without losing track of what's what and I love it. I wish you could push it even further to kinda make it more obedient in terms of following the user's instructions and expectations in more creative ways, maybe further training and/or merging would do the trick in that department, but given the model's stability when dealing with extreme scenarios, I believe there's still room for allowing more creativity.

Good job so far!

Thanks for the feedback, try setting the temp to around 0.8 that could help.

I started with temp 0.78 and ended up with temp 0.86. Yes, higher temperatures are helpful here.

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