Instructions to use AuriAetherwiing/MN-12B-Starcannon-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AuriAetherwiing/MN-12B-Starcannon-v3 with Transformers:

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

pipe = pipeline("text-generation", model="AuriAetherwiing/MN-12B-Starcannon-v3")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("AuriAetherwiing/MN-12B-Starcannon-v3")
model = AutoModelForCausalLM.from_pretrained("AuriAetherwiing/MN-12B-Starcannon-v3")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use AuriAetherwiing/MN-12B-Starcannon-v3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AuriAetherwiing/MN-12B-Starcannon-v3"
# 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-Starcannon-v3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AuriAetherwiing/MN-12B-Starcannon-v3

SGLang

How to use AuriAetherwiing/MN-12B-Starcannon-v3 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-Starcannon-v3" \
    --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-Starcannon-v3",
		"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-Starcannon-v3" \
        --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-Starcannon-v3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use AuriAetherwiing/MN-12B-Starcannon-v3 with Docker Model Runner:
```
docker model run hf.co/AuriAetherwiing/MN-12B-Starcannon-v3
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Mistral Nemo 12B Starcannon v3

This is a merge of pre-trained language models created using mergekit. Artbitrary update, because I know that people would request it. Didn't have much time to test it, tbh, but feels nice enough? It's up to y'all to decide if it's an upgrade, sidegrade or downgrade. At least now both models have ChatML trained, there's that.
Static GGUF (by Mradermacher)
Imatrix 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: anthracite-org/magnum-12b-v2
      parameters:
        density: 0.3
        weight: 0.5
    - model: nothingiisreal/MN-12B-Celeste-V1.9
      parameters:
        density: 0.7
        weight: 0.5

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

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Model tree for AuriAetherwiing/MN-12B-Starcannon-v3

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Merge model

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Paper for AuriAetherwiing/MN-12B-Starcannon-v3

Resolving Interference When Merging Models

Paper • 2306.01708 • Published Jun 2, 2023 • 19