Instructions to use kainatq/RP-king-12b-II with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kainatq/RP-king-12b-II with Transformers:

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

pipe = pipeline("text-generation", model="kainatq/RP-king-12b-II")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("kainatq/RP-king-12b-II")
model = AutoModelForCausalLM.from_pretrained("kainatq/RP-king-12b-II")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use kainatq/RP-king-12b-II with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kainatq/RP-king-12b-II"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kainatq/RP-king-12b-II",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/kainatq/RP-king-12b-II

SGLang

How to use kainatq/RP-king-12b-II 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 "kainatq/RP-king-12b-II" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kainatq/RP-king-12b-II",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "kainatq/RP-king-12b-II" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kainatq/RP-king-12b-II",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use kainatq/RP-king-12b-II with Docker Model Runner:
```
docker model run hf.co/kainatq/RP-king-12b-II
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

RP-king-12b-II

RP-king-12b-II is a merge of the following models using mergekit:

🧩 Configuration

merge_method: dare_ties

models:
  - model: MarinaraSpaghetti/NemoMix-Unleashed-12B
    parameters:
      density: 0.7
      weight: 1.0

  - model: inflatebot/MN-12B-Mag-Mell-R1
    parameters:
      density: 0.7
      weight: 1.1

  - model: elinas/Chronos-Gold-12B-1.0
    parameters:
      density: 1.0
      weight: 1.2

  - model: Retreatcost/KansenSakura-Eclipse-RP-12b
    parameters:
      density: 0.45
      weight: 0.65

  - model: redrix/patricide-12B-Unslop-Mell-v2
    parameters:
      density: 0.6
      weight: 1.0

parameters:
  normalize: true

dtype: bfloat16
tokenizer_source: base```

Downloads last month: 7

Safetensors

Model size

12B params

Tensor type

BF16

Model tree for kainatq/RP-king-12b-II

MarinaraSpaghetti/NemoMix-Unleashed-12B

Retreatcost/KansenSakura-Eclipse-RP-12b

elinas/Chronos-Gold-12B-1.0

inflatebot/MN-12B-Mag-Mell-R1

redrix/patricide-12B-Unslop-Mell-v2

Merge model

this model

Quantizations

3 models