Text Generation
Transformers
Safetensors
NeMo
mistral
Merge
mergekit
model_stock
conversational
text-generation-inference
Instructions to use OccultAI/Musecuilo-12B-Model_Stock with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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.
- Inference
- 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
Create README.md
Browse files
README.md
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---
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library_name: transformers
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license: apache-2.0
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tags:
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- merge
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- mergekit
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- mistral
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- nemo
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- model_stock
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base_model:
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- mistralai/Mistral-Nemo-Instruct-2407
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- LatitudeGames/Muse-12B
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- allura-org/Tlacuilo-12B
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---
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# 🐈 Musecuilo 12B Model_Stock
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> [!NOTE]
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> <span style="color:red; font-weight:bold">Note:</span> Use **Mistral Tekken** (recommended) or **ChatML** chat template for best results. The model has some refusals but can be jailbroken or ablated as needed.
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>
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This model was merged using the [`model_stock`](https://arxiv.org/abs/2403.19522) merge method.
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Musecuilo is a merge of the following models using [mergekit](https://github.com/cg123/mergekit):
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* [mistralai/Mistral-Nemo-Instruct-2407](https://huggingface.co/mistralai/Mistral-Nemo-Instruct-2407)
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* [LatitudeGames/Muse-12B](https://huggingface.co/LatitudeGames/Muse-12B)
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* [allura-org/Tlacuilo-12B](https://huggingface.co/allura-org/Tlacuilo-12BS)
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## 🧩 Configuration
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```yaml
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architecture: MistralForCausalLM
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base_model: B:/12B/mistralai--Mistral-Nemo-Instruct-2407
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models:
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- model: B:/12B/allura-org--Tlacuilo-12B
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- model: B:/12B/LatitudeGames--Muse-12B
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merge_method: model_stock
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parameters:
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filter_wise: true
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dtype: float32
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out_dtype: bfloat16
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tokenizer:
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source: B:/12B/LatitudeGames--Muse-12B
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name: Musecuilo-12B-Model_Stock
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```
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