Instructions to use grimjim/kunoichi-lemon-royale-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use grimjim/kunoichi-lemon-royale-7B with Transformers:

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

pipe = pipeline("text-generation", model="grimjim/kunoichi-lemon-royale-7B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("grimjim/kunoichi-lemon-royale-7B")
model = AutoModelForMultimodalLM.from_pretrained("grimjim/kunoichi-lemon-royale-7B")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use grimjim/kunoichi-lemon-royale-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "grimjim/kunoichi-lemon-royale-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "grimjim/kunoichi-lemon-royale-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/grimjim/kunoichi-lemon-royale-7B

SGLang

How to use grimjim/kunoichi-lemon-royale-7B 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 "grimjim/kunoichi-lemon-royale-7B" \
    --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": "grimjim/kunoichi-lemon-royale-7B",
		"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 "grimjim/kunoichi-lemon-royale-7B" \
        --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": "grimjim/kunoichi-lemon-royale-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use grimjim/kunoichi-lemon-royale-7B with Docker Model Runner:
```
docker model run hf.co/grimjim/kunoichi-lemon-royale-7B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

kunoichi-lemon-royale-7B

Lightly tested with both Alpaca and ChatML prompts. Works with temperature 1.0 and minP 0.01, but feel free to vary it up. Tested to 8K context.

This model has a tendency to lean into revealing character interiority when generating narrative, which some people might find interesting. I found the model good with not only following the character card but also taking strong hints from the first message. This experimental model may occasionally reveal context, unfortunately.

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

Available quants: GGUF exl2

Merge Details

Merge Method

This model was merged using the DARE TIES merge method using SanjiWatsuki/Kunoichi-7B as a base. Each of the models had strengths I liked to varying degrees, leading to weights and densities being adjusted in aesthetic proportion.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: SanjiWatsuki/Kunoichi-7B
    # no parameters necessary for base model
  - model: KatyTheCutie/LemonadeRP-4.5.3
    parameters:
      weight: 0.3
      density: 0.4
  - model: core-3/kuno-royale-v2-7b
    parameters:
      weight: 0.3
      density: 0.4
  - model: SanjiWatsuki/Kunoichi-DPO-v2-7B
    parameters:
      weight: 0.4
      density: 0.8
merge_method: dare_ties
base_model: SanjiWatsuki/Kunoichi-7B
parameters:
  int8_mask: true
  normalize: true
dtype: bfloat16