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

Libraries

How to use Vortex5/LunaMaid-12B with Transformers:

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

pipe = pipeline("text-generation", model="Vortex5/LunaMaid-12B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Vortex5/LunaMaid-12B")
model = AutoModelForCausalLM.from_pretrained("Vortex5/LunaMaid-12B")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Vortex5/LunaMaid-12B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Vortex5/LunaMaid-12B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Vortex5/LunaMaid-12B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Vortex5/LunaMaid-12B

SGLang

How to use Vortex5/LunaMaid-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/LunaMaid-12B" \
    --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": "Vortex5/LunaMaid-12B",
		"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 "Vortex5/LunaMaid-12B" \
        --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": "Vortex5/LunaMaid-12B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Vortex5/LunaMaid-12B with Docker Model Runner:
```
docker model run hf.co/Vortex5/LunaMaid-12B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

🩵 LunaMaid-12B

This is a multi-stage merge of pre-trained language models created using mergekit.

🧬 Merge Overview

LunaMaid-12B was produced through a two-stage multi-model merge using MergeKit.
Each stage fuses models with complementary linguistic and stylistic traits to create a cohesive, emotionally nuanced personality.

🩵 Stage 1 — Slerp Merge (Intermediate Model `First`)

Base Model: Vortex5/Vermilion-Sage-12B
Merged With: yamatazen/NeonMaid-12B-v2
Method: Spherical Linear Interpolation (Slerp)

Stage 1 Configuration

name: First
base_model: Vortex5/Vermilion-Sage-12B
models:
- model: yamatazen/NeonMaid-12B-v2
merge_method: slerp
dtype: bfloat16
parameters:
normalize: true
t: [0.25, 0.35, 0.45, 0.55, 0.65, 0.75, 0.6, 0.5, 0.6, 0.6]

🌑 Merge Method — Karcher Mean Merge (Final Model)

Base Model: Intermediate output from Stage 1 ./intermediates/First
Merged With: Vortex5/Moonlit-Shadow-12B
Method: Karcher Mean (Riemannian Barycenter)

Stage 2 Configuration

dtype: bfloat16
merge_method: karcher
modules:
  default:
    slices:
      - sources:
          - layer_range: [0, 40]
            model: ./intermediates/First
          - layer_range: [0, 40]
            model: Vortex5/Moonlit-Shadow-12B
parameters:
  max_iter: 9999
  tol: 1e-9