Instructions to use DoppelReflEx/Mimicore-WhiteSnake-22B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DoppelReflEx/Mimicore-WhiteSnake-22B with Transformers:

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

pipe = pipeline("text-generation", model="DoppelReflEx/Mimicore-WhiteSnake-22B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("DoppelReflEx/Mimicore-WhiteSnake-22B")
model = AutoModelForCausalLM.from_pretrained("DoppelReflEx/Mimicore-WhiteSnake-22B")
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

vLLM

How to use DoppelReflEx/Mimicore-WhiteSnake-22B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DoppelReflEx/Mimicore-WhiteSnake-22B"
# 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/Mimicore-WhiteSnake-22B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/DoppelReflEx/Mimicore-WhiteSnake-22B

SGLang

How to use DoppelReflEx/Mimicore-WhiteSnake-22B 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/Mimicore-WhiteSnake-22B" \
    --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/Mimicore-WhiteSnake-22B",
		"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/Mimicore-WhiteSnake-22B" \
        --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/Mimicore-WhiteSnake-22B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use DoppelReflEx/Mimicore-WhiteSnake-22B with Docker Model Runner:
```
docker model run hf.co/DoppelReflEx/Mimicore-WhiteSnake-22B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

What is this?

Model merge, I tested with Q4_K_S, so maybe that not final result. Overall, decent model, not too good or too bad. Still good for play RP, ERP if you have 16-24GB VRAM.

Recommend CHATML, Mistral V3 instruct. Or you can find what is the best for you. Have fun!

Merge Detail

### Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

models:
 - model: knifeayumu/Cydonia-v1.2-Magnum-v4-22B
   parameters:
     density: 0.9
     weight: 1
 - model: Steelskull/MSM-MS-Cydrion-22B
   parameters:
     density: 0.6
     weight: 0.8
merge_method: dare_ties
base_model: TheDrummer/Cydonia-22B-v1.3
tokenizer_source: base