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

Libraries

How to use OccultAI/MN-Nazgul-12B-v1 with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("OccultAI/MN-Nazgul-12B-v1")
model = AutoModelForCausalLM.from_pretrained("OccultAI/MN-Nazgul-12B-v1", device_map="auto")
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/MN-Nazgul-12B-v1 with NeMo:
```
# tag did not correspond to a valid NeMo domain.
```
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use OccultAI/MN-Nazgul-12B-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OccultAI/MN-Nazgul-12B-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": "OccultAI/MN-Nazgul-12B-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/OccultAI/MN-Nazgul-12B-v1

SGLang

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

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

⚠️ Warning: This model can produce narratives and RP that contain violent and graphic erotic content. Adjust your system prompt accordingly, and use ChatML chat template.

🐉 MN Nazgul 12B v1

This is a merge of pre-trained language models created using mergekit.

🔀 Merge Details

Merge Method

This model was merged using the della merge method using Retreatcost/Mistral-Nemo-Base-2407-ChatML as a base.

The merge should be fully uncensored and have no refusals, although it may struggle with longer context.

Models Merged

The following models were included in the merge:

DarkArtsForge/MN-Raven-12B-v1
IggyLux/MN-VelvetCafe-RP-12B-V2
OccultAI/MN-Morpheus-12B-v1
Retreatcost/Mistral-Nemo-Base-2407-ChatML
shrugging-shoulders/Amberlight-Lux-12B
WokeAI/Tankie-DPE-12B-SFT-v2

⚙️ Configuration

The following YAML configuration was used to produce this model:

architecture: MistralForCausalLM
models:
  - model: B:\12B\MN-Morpheus-12B-v1_epoch2
    parameters:
      weight:
        - filter: "lm_head"
          value: 0.2
        - filter: "embed_tokens"
          value: 0.2
        - value: 0.4
      density: 0.9
      epsilon: 0.09
  - model: B:\12B\IggyLux--MN-VelvetCafe-RP-12B-V2
    parameters:
      weight:
        - filter: "lm_head"
          value: 0.2
        - filter: "embed_tokens"
          value: 0.2
        - value: 0.4
      density: 0.9
      epsilon: 0.09
  - model: A:\LLM\.cache\13B\WokeAI--Tankie-DPE-12B-SFT-v2
    parameters:
      weight:
        - filter: "lm_head"
          value: 0.2
        - filter: "embed_tokens"
          value: 0.2
        - value: 0.4
      density: 0.9
      epsilon: 0.09
  - model: B:\12B\MN-Raven-12B-v1
    parameters:
      weight:
        - filter: "lm_head"
          value: 0.2
        - filter: "embed_tokens"
          value: 0.2
        - value: 0.4
      density: 0.9
      epsilon: 0.09
  - model: B:\12B\shrugging-shoulders--Amberlight-Lux-12B
    parameters:
      weight:
        - filter: "lm_head"
          value: 0.2
        - filter: "embed_tokens"
          value: 0.2
        - value: 0.4
      density: 0.9
      epsilon: 0.09
merge_method: della
base_model: B:\12B\Retreatcost--Mistral-Nemo-Base-2407-ChatML
parameters:
  lambda: 1.0
  normalize: false
  int8_mask: false
  rescale: true
dtype: float32
out_dtype: bfloat16
tokenizer:
  source: B:\12B\shrugging-shoulders--Amberlight-Lux-12B
chat_template: "chatml"
name: 🐉 MN Nazgul 12B v1