Instructions to use OccultAI/Musecuilo-12B-Model_Stock with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OccultAI/Musecuilo-12B-Model_Stock with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("OccultAI/Musecuilo-12B-Model_Stock")
model = AutoModelForCausalLM.from_pretrained("OccultAI/Musecuilo-12B-Model_Stock")
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]:]))

NeMo
How to use OccultAI/Musecuilo-12B-Model_Stock with NeMo:
```
# tag did not correspond to a valid NeMo domain.
```
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use OccultAI/Musecuilo-12B-Model_Stock with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/OccultAI/Musecuilo-12B-Model_Stock

SGLang

How to use OccultAI/Musecuilo-12B-Model_Stock 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 "OccultAI/Musecuilo-12B-Model_Stock" \
    --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": "OccultAI/Musecuilo-12B-Model_Stock",
		"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 "OccultAI/Musecuilo-12B-Model_Stock" \
        --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": "OccultAI/Musecuilo-12B-Model_Stock",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

🐈 Musecuilo 12B Model_Stock

Note: Use Mistral Tekken (recommended) or ChatML chat template for best results. The model has some refusals but can be jailbroken or ablated as needed.

This model was merged using the model_stock merge method.

Musecuilo is a merge of the following models using mergekit:

🧩 Configuration

architecture: MistralForCausalLM
base_model: B:/12B/mistralai--Mistral-Nemo-Instruct-2407
models:
  - model: B:/12B/allura-org--Tlacuilo-12B
  - model: B:/12B/LatitudeGames--Muse-12B
merge_method: model_stock
parameters:  
  filter_wise: true
dtype: float32
out_dtype: bfloat16
tokenizer:
  source: B:/12B/LatitudeGames--Muse-12B
name: Musecuilo-12B-Model_Stock