How to use from
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?"
			}
		]
	}'
Quick Links

🐈 Musecuilo 12B Model_Stock

Musecuilo

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
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Model size
12B params
Tensor type
BF16
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