Instructions to use AuriAetherwiing/MN-12B-Starsong-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AuriAetherwiing/MN-12B-Starsong-v1 with Transformers:

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

pipe = pipeline("text-generation", model="AuriAetherwiing/MN-12B-Starsong-v1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("AuriAetherwiing/MN-12B-Starsong-v1")
model = AutoModelForCausalLM.from_pretrained("AuriAetherwiing/MN-12B-Starsong-v1")
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 AuriAetherwiing/MN-12B-Starsong-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AuriAetherwiing/MN-12B-Starsong-v1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AuriAetherwiing/MN-12B-Starsong-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AuriAetherwiing/MN-12B-Starsong-v1

SGLang

How to use AuriAetherwiing/MN-12B-Starsong-v1 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 "AuriAetherwiing/MN-12B-Starsong-v1" \
    --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": "AuriAetherwiing/MN-12B-Starsong-v1",
		"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 "AuriAetherwiing/MN-12B-Starsong-v1" \
        --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": "AuriAetherwiing/MN-12B-Starsong-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use AuriAetherwiing/MN-12B-Starsong-v1 with Docker Model Runner:
```
docker model run hf.co/AuriAetherwiing/MN-12B-Starsong-v1
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Mistral Nemo 12B Starsong

This is a merge of pre-trained language models created using mergekit. Just messing around, thought this one turned out distinct enough? YMMV on that, but I kinda liked it. It's definitely better for SFW than for NSFW tho. Seems to be stabler with Mistral formatting? Weird, both merged models were trained with ChatML. I guess we chalk it up to another merging anomaly, heh.
Unlicensed bc I got enlightened (lmao) and don't want to license machine output anymore.
Thanks to Sao for Lyra btw, really liking the direction where those experimental models are heading. Keep it up!

Static GGUF (by Mradermacher)
EXL2 (by kingbri of RoyalLab)

Merge Details

Merge Method

This model was merged using the TIES merge method using nothingiisreal/MN-12B-Celeste-V1.9 as a base.

Merge Fodder

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: Sao10K/MN-12B-Lyra-v1
    parameters:
      density: 0.45
      weight: 0.5
  - model: nothingiisreal/MN-12B-Celeste-V1.9
    parameters:
      density: 0.65
      weight: 0.5

merge_method: ties
base_model: nothingiisreal/MN-12B-Celeste-V1.9
parameters:
  normalize: true
  int8_mask: true
dtype: bfloat16