Instructions to use beyoru/Luna-Ethos with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use beyoru/Luna-Ethos with Transformers:

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

pipe = pipeline("text-generation", model="beyoru/Luna-Ethos")
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("beyoru/Luna-Ethos")
model = AutoModelForCausalLM.from_pretrained("beyoru/Luna-Ethos", 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 beyoru/Luna-Ethos with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "beyoru/Luna-Ethos"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "beyoru/Luna-Ethos",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/beyoru/Luna-Ethos

SGLang

How to use beyoru/Luna-Ethos 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 "beyoru/Luna-Ethos" \
    --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": "beyoru/Luna-Ethos",
		"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 "beyoru/Luna-Ethos" \
        --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": "beyoru/Luna-Ethos",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use beyoru/Luna-Ethos with Docker Model Runner:
```
docker model run hf.co/beyoru/Luna-Ethos
```

beyoru commited on Apr 28

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@@ -18,6 +18,9 @@ base_model:
 ## Overview
 Luna is a conversational AI model designed for immersive roleplay (RP) and natural chatting.
 It has been carefully fine-tuned to deliver responses that feel more engaging, expressive, and character-driven than those produced by standard instruction-tuned models. Beyond simply following prompts, it places greater emphasis on tone, personality, and narrative flow—making interactions feel more dynamic and human-like.

 ## Overview
+[![GitHub](https://img.shields.io/badge/GitHub-181717?style=flat-square&logo=github&logoColor=white)](https://github.com/Hert4)
+[![HuggingFace](https://img.shields.io/badge/HuggingFace-FFD21E?style=flat-square&logo=huggingface&logoColor=black)](https://huggingface.co/beyoru)
+[![BMC](https://img.shields.io/badge/buy_me_a_coffee-A78BFA?style=flat-square&logo=buy-me-a-coffee&logoColor=white)](https://buymeacoffee.com/ductransa0g)
 Luna is a conversational AI model designed for immersive roleplay (RP) and natural chatting.
 It has been carefully fine-tuned to deliver responses that feel more engaging, expressive, and character-driven than those produced by standard instruction-tuned models. Beyond simply following prompts, it places greater emphasis on tone, personality, and narrative flow—making interactions feel more dynamic and human-like.