Instructions to use Cyclone-Labs/Lucid-Shadow-31B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Cyclone-Labs/Lucid-Shadow-31B with Transformers:

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

pipe = pipeline("image-text-to-text", model="Cyclone-Labs/Lucid-Shadow-31B")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("Cyclone-Labs/Lucid-Shadow-31B")
model = AutoModelForMultimodalLM.from_pretrained("Cyclone-Labs/Lucid-Shadow-31B", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Cyclone-Labs/Lucid-Shadow-31B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Cyclone-Labs/Lucid-Shadow-31B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Cyclone-Labs/Lucid-Shadow-31B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/Cyclone-Labs/Lucid-Shadow-31B

SGLang

How to use Cyclone-Labs/Lucid-Shadow-31B 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 "Cyclone-Labs/Lucid-Shadow-31B" \
    --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": "Cyclone-Labs/Lucid-Shadow-31B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "Cyclone-Labs/Lucid-Shadow-31B" \
        --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": "Cyclone-Labs/Lucid-Shadow-31B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use Cyclone-Labs/Lucid-Shadow-31B with Docker Model Runner:
```
docker model run hf.co/Cyclone-Labs/Lucid-Shadow-31B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Overview

Lucid-Shadow-31B was created by combining gemma-4-31B-it, Artemis-31B-v1.1, Gemopus-4-31B-it, and Melinoe-Gemma4-31B-VL using a custom merge method.

Merge Configuration

base_model: google/gemma-4-31B-it
models:
  - model: TheDrummer/Artemis-31B-v1.1
  - model: Jackrong/Gemopus-4-31B-it
  - model: bgg1996/Melinoe-Gemma4-31B-VL
merge_method: hasp
chat_template: auto
parameters:
  steps: 12
dtype: float32
out_dtype: bfloat16
tokenizer:
  source: union

Intended Use

Roleplay

Character-driven interaction, personas, dialogue, emotional scenes, and long-form roleplay.

Creative Writing

Fiction, dialogue, atmosphere, descriptive writing, stylistic drafting, and imaginative prose.

Storytelling

Long-form narratives, worldbuilding, continuity, evolving characters, and multi-character plots.

Interactive Fiction

Branching narratives, scenario play, character interaction, and continuously evolving stories.

Downloads last month: 64

Safetensors

Model size

31B params

Tensor type

BF16

Model tree for Cyclone-Labs/Lucid-Shadow-31B

Jackrong/Gemopus-4-31B-it

TheDrummer/Artemis-31B-v1.1

bgg1996/Melinoe-Gemma4-31B-VL

google/gemma-4-31B-it

Merge model

this model

Quantizations

3 models