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

Libraries

How to use Vortex5/Celestial-Queen-12B with Transformers:

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

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

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Vortex5/Celestial-Queen-12B")
model = AutoModelForCausalLM.from_pretrained("Vortex5/Celestial-Queen-12B", device_map="auto")
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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Vortex5/Celestial-Queen-12B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Vortex5/Celestial-Queen-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/Celestial-Queen-12B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Vortex5/Celestial-Queen-12B

SGLang

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

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

Celestial-Queen-12B

Overview

Celestial-Queen-12B was created through a multi-stage merge combining Crimson-Constellation-12B, Strawberry_Smoothie-12B-Model_Stock, MN-12B-Mag-Mell-R1, LunaMaid-12B, Mahou-1.5-mistral-nemo-12B, MN-12B-Celeste-V1.9, Omega-Darker_The-Final-Directive-12B, MegaMoon-Karcher-12B, and MN-12B-Mag-Mell-R1.

Multi-stage merge configuration

name: First
models:
  - model: Vortex5/Crimson-Constellation-12B
  - model: DreadPoor/Strawberry_Smoothie-12B-Model_Stock
  - model: inflatebot/MN-12B-Mag-Mell-R1
  - model: Vortex5/LunaMaid-12B
merge_method: saef
parameters:
  paradox: 0.40
  strength: 0.88
  boost: 0.28
  modes: 2
dtype: float32
tokenizer:
  source: Vortex5/LunaMaid-12B
---
name: Second
models:
  - model: flammenai/Mahou-1.5-mistral-nemo-12B
  - model: nothingiisreal/MN-12B-Celeste-V1.9
  - model: ReadyArt/Omega-Darker_The-Final-Directive-12B
merge_method: saef
parameters:
  paradox: 0.54
  strength: 0.9
  boost: 0.6
  modes: 2
dtype: float32
tokenizer:
  source: union
---
name: Nearswap1
models:
  - model: Vortex5/MegaMoon-Karcher-12B
merge_method: nearswap
base_model: First
parameters:
  t: 0.0008
dtype: float32
tokenizer:
  source: First
---
name: Nearswap2
models:
  - model: inflatebot/MN-12B-Mag-Mell-R1
merge_method: nearswap
base_model: Second
parameters:
  t: 0.0008
dtype: float32
tokenizer:
  source: Second
---
models:
  - model: Nearswap1
  - model: Nearswap2
merge_method: karcher
chat_template: auto
dtype: float32
out_dtype: bfloat16
parameters:
  tol: 1e-9
  max_iter: 1000
tokenizer:
  source: Vortex5/LunaMaid-12B