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

Libraries

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

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("DoppelReflEx/MN-12B-Mimicore-GreenSnake")
model = AutoModelForCausalLM.from_pretrained("DoppelReflEx/MN-12B-Mimicore-GreenSnake", 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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

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

Install from pip and serve model

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

Use Docker

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

SGLang

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

Docker Model Runner
How to use DoppelReflEx/MN-12B-Mimicore-GreenSnake with Docker Model Runner:
```
docker model run hf.co/DoppelReflEx/MN-12B-Mimicore-GreenSnake
```

MN-12B-Mimicore-GreenSnake / README.md

DoppelReflEx

Update README.md

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metadata

license: cc-by-nc-4.0
base_model:
  - PocketDoc/Dans-PersonalityEngine-V1.1.0-12b
  - inflatebot/MN-12B-Mag-Mell-R1
library_name: transformers
tags:
  - mergekit
  - merge

Version: WhiteSnake - Orochi - GreenSnake

What is it?

Previous version of WhiteSnake, not too much different in OpenLLM LeaderBoard scores. Not too good to archiving 'human response', but still good enough.

This merge model is a gift for Lunar New Year, haha. Enjoy it.

Good for RP, ERP, Story Telling.

Chat Format? ChatML of course!

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: inflatebot/MN-12B-Mag-Mell-R1
  - model: PocketDoc/Dans-PersonalityEngine-V1.1.0-12b
merge_method: slerp
base_model: inflatebot/MN-12B-Mag-Mell-R1
parameters:
  t: [0.1, 0.2, 0.4, 0.6, 0.6, 0.4, 0.2, 0.1]
dtype: bfloat16
tokenizer_source: base