01 Roleplay
Character-driven interaction, personas, dialogue, emotional scenes, and long-form roleplay.
How to use Cyclone-Labs/Lunar-Trace-31B with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("image-text-to-text", model="Cyclone-Labs/Lunar-Trace-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) # pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM
processor = AutoProcessor.from_pretrained("Cyclone-Labs/Lunar-Trace-31B")
model = AutoModelForMultimodalLM.from_pretrained("Cyclone-Labs/Lunar-Trace-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=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))How to use Cyclone-Labs/Lunar-Trace-31B with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Cyclone-Labs/Lunar-Trace-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/Lunar-Trace-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 run hf.co/Cyclone-Labs/Lunar-Trace-31B
How to use Cyclone-Labs/Lunar-Trace-31B with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "Cyclone-Labs/Lunar-Trace-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/Lunar-Trace-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 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/Lunar-Trace-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/Lunar-Trace-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"
}
}
]
}
]
}'How to use Cyclone-Labs/Lunar-Trace-31B with Docker Model Runner:
docker model run hf.co/Cyclone-Labs/Lunar-Trace-31B
Lunar-Trace-31B was created by combining gemma-4-31B-it, Starlight-Synthesis-31B, G4-LuneLight-31B, and gemma-4-31B-it-Claude-Opus-Distill-v2 using a custom merge method.
base_model: google/gemma-4-31B-it
merge_method: mdr
models:
- model: Cyclone-Labs/Starlight-Synthesis-31B
parameters:
weight:
- filter: vision_tower
value: 0.0
- filter: embed_vision
value: 0.0
- filter: self_attn.q_proj
value: [0.46, 0.47, 0.48, 0.49, 0.50, 0.51, 0.52, 0.53]
- filter: self_attn.k_proj
value: [0.47, 0.48, 0.49, 0.50, 0.51, 0.52, 0.53, 0.54]
- filter: self_attn.v_proj
value: [0.50, 0.52, 0.54, 0.56, 0.58, 0.60, 0.62, 0.64]
- filter: self_attn.o_proj
value: [0.52, 0.54, 0.56, 0.59, 0.61, 0.64, 0.66, 0.68]
- filter: mlp.gate_proj
value: [0.64, 0.67, 0.70, 0.72, 0.74, 0.76, 0.78, 0.80]
- filter: mlp.up_proj
value: [0.63, 0.65, 0.68, 0.71, 0.74, 0.76, 0.78, 0.80]
- filter: mlp.down_proj
value: [0.62, 0.64, 0.67, 0.70, 0.72, 0.75, 0.78, 0.80]
- filter: self_attn.q_norm
value: 0.60
- filter: self_attn.k_norm
value: 0.60
- filter: layernorm
value: 0.65
- filter: layer_scalar
value: 0.70
- filter: language_model.norm
value: 0.65
- value: 0.33
- model: zerofata/G4-LuneLight-31B
parameters:
weight:
- filter: vision_tower
value: 0.0
- filter: embed_vision
value: 0.0
- filter: self_attn.q_proj
value: [0.23, 0.23, 0.23, 0.22, 0.21, 0.20, 0.19, 0.18]
- filter: self_attn.k_proj
value: [0.24, 0.24, 0.23, 0.22, 0.21, 0.20, 0.19, 0.18]
- filter: self_attn.v_proj
value: [0.34, 0.33, 0.32, 0.31, 0.30, 0.29, 0.28, 0.27]
- filter: self_attn.o_proj
value: [0.35, 0.34, 0.33, 0.30, 0.28, 0.25, 0.23, 0.21]
- filter: mlp.gate_proj
value: [0.26, 0.23, 0.20, 0.18, 0.16, 0.14, 0.12, 0.10]
- filter: mlp.up_proj
value: [0.27, 0.25, 0.22, 0.19, 0.16, 0.14, 0.12, 0.10]
- filter: mlp.down_proj
value: [0.28, 0.26, 0.23, 0.20, 0.18, 0.15, 0.12, 0.10]
- filter: self_attn.q_norm
value: 0.40
- filter: self_attn.k_norm
value: 0.40
- filter: layernorm
value: 0.35
- filter: layer_scalar
value: 0.30
- filter: language_model.norm
value: 0.35
- value: 0.33
- model: TeichAI/gemma-4-31B-it-Claude-Opus-Distill-v2
parameters:
weight:
- filter: vision_tower
value: 0.0
- filter: embed_vision
value: 0.0
- filter: self_attn.q_proj
value: [0.31, 0.30, 0.29, 0.29, 0.29, 0.29, 0.29, 0.29]
- filter: self_attn.k_proj
value: [0.29, 0.28, 0.28, 0.28, 0.28, 0.28, 0.28, 0.28]
- filter: self_attn.v_proj
value: [0.16, 0.15, 0.14, 0.13, 0.12, 0.11, 0.10, 0.09]
- filter: self_attn.o_proj
value: [0.13, 0.12, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11]
- filter: mlp
value: 0.10
- value: 0.05
parameters:
gain: 0.7
inheritance: 0.45
anchor: 0.60
prior_strength: 0.95
containment: 1.10
dtype: float32
out_dtype: bfloat16
tokenizer:
source: base
chat_template: auto
In story-writing tests, the model showed a tendency toward dark psychological themes, even when the prompts did not specifically request them.
Character-driven interaction, personas, dialogue, emotional scenes, and long-form roleplay.
Fiction, dialogue, atmosphere, descriptive writing, stylistic drafting, and imaginative prose.
Long-form narratives, worldbuilding, continuity, evolving characters, and multi-character plots.
Branching narratives, scenario play, character interaction, and continuously evolving stories.