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

Libraries

How to use DoppelReflEx/MN-12B-WolFrame with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("DoppelReflEx/MN-12B-WolFrame")
model = AutoModelForCausalLM.from_pretrained("DoppelReflEx/MN-12B-WolFrame")
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 Settings

vLLM

How to use DoppelReflEx/MN-12B-WolFrame with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/DoppelReflEx/MN-12B-WolFrame

SGLang

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

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

What is this?

Previous name was WhiteSnake-V2, but the eval scores is not good, so I decide to rename it. Very good in creative writing and RP, ERP. Not good in Math.

It's main goal is to break the origin WhiteSnake in eval and real usecase, but nothing too good, just decent.

GGUF, thank mradermacher a lots: https://huggingface.co/mradermacher/MN-12B-Mimicore-WhiteSnake-v2-Experiment-4-GGUF

My own Q6_K: https://huggingface.co/DoppelReflEx/MN-12B-WolFrame-Q6_K-GGUF

Merge Details

### Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

models:
 - model: cgato/Nemo-12b-Humanize-KTO-Experimental-Latest
   parameters:
     density: 0.9
     weight: 1
 - model: DoppelReflEx/MN-12B-Mimicore-GreenSnake
   parameters:
     density: 0.6
     weight: 0.8
 - model: crestf411/MN-Slush
   parameters:
     density: 0.7
     weight: 0.5
merge_method: dare_ties
base_model: IntervitensInc/Mistral-Nemo-Base-2407-chatml
tokenizer_source: base