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

Libraries

How to use Vortex5/Astral-Arcanist-12B with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Vortex5/Astral-Arcanist-12B")
model = AutoModelForCausalLM.from_pretrained("Vortex5/Astral-Arcanist-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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Vortex5/Astral-Arcanist-12B with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Vortex5/Astral-Arcanist-12B

SGLang

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

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

Overview

Astral-Arcanist-12B was created through a multi-stage merge combining Stellar-Witch-12B, Red-Synthesis-12B, Aurora-Mirage-12B, and Darklit-Maiden-12B.

Multi-stage merge configuration

name: First
base_model: Vortex5/Stellar-Witch-12B
models:
  - model: Vortex5/Red-Synthesis-12B
merge_method: nearswap
parameters:
  t: 0.0008
dtype: float32
tokenizer:
  source: Vortex5/Stellar-Witch-12B
---
name: Second
base_model: Vortex5/Aurora-Mirage-12B
models:
  - model: Vortex5/Darklit-Maiden-12B
merge_method: nearswap
parameters:
  t: 0.0008
dtype: float32
tokenizer:
  source: Vortex5/Aurora-Mirage-12B
---
base_model: Second
models:
  - model: First
  - model: Second
merge_method: arcee_fusion
chat_template: auto
dtype: float32
out_dtype: bfloat16
tokenizer:
  source: Vortex5/Stellar-Witch-12B