Instructions to use SanjiWatsuki/Kunoichi-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SanjiWatsuki/Kunoichi-7B with Transformers:

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

pipe = pipeline("text-generation", model="SanjiWatsuki/Kunoichi-7B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("SanjiWatsuki/Kunoichi-7B")
model = AutoModelForCausalLM.from_pretrained("SanjiWatsuki/Kunoichi-7B")

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use SanjiWatsuki/Kunoichi-7B with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/SanjiWatsuki/Kunoichi-7B

SGLang

How to use SanjiWatsuki/Kunoichi-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 "SanjiWatsuki/Kunoichi-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": "SanjiWatsuki/Kunoichi-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 "SanjiWatsuki/Kunoichi-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": "SanjiWatsuki/Kunoichi-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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

Description

This repository hosts Kunoichi-7B, an general purpose model capable of RP. In both my testing and the benchmarks, Kunoichi is an extremely strong model, keeping the advantages of my previous models but gaining more intelligence. Kunoichi scores extremely well on all benchmarks which correlate closely with ChatBot Arena Elo.

Model	MT Bench	EQ Bench	MMLU	Logic Test
GPT-4-Turbo	9.32	-	-	-
GPT-4	8.99	62.52	86.4	0.86
Kunoichi-7B	8.14	44.32	64.9	0.58
Starling-7B	8.09	-	63.9	0.51
Claude-2	8.06	52.14	78.5	-
Silicon-Maid-7B	7.96	40.44	64.7	0.54
Loyal-Macaroni-Maid-7B	7.95	38.66	64.9	0.57
GPT-3.5-Turbo	7.94	50.28	70	0.57
Claude-1	7.9	-	77	-
Openchat-3.5	7.81	37.08	64.3	0.39
Dolphin-2.6-DPO	7.74	42.88	61.9	0.53
Zephyr-7B-beta	7.34	38.71	61.4	0.30
Llama-2-70b-chat-hf	6.86	51.56	63	-
Neural-chat-7b-v3-1	6.84	43.61	62.4	0.30

The model is intended to be used with up to an 8k context window. Using a NTK RoPE alpha of 2.6, the model can be used experimentally up to a 16k context window.

Prompt template: Custom format, or Alpaca

Alpaca:

Below is an instruction that describes a task. Write a response that appropriately completes the request.

### Instruction:
{prompt}

### Response:

SillyTavern format:

I found the best SillyTavern results from using the Noromaid template.

SillyTavern config files: Context, Instruct.

Additionally, here is my highly recommended Text Completion preset. You can tweak this by adjusting temperature up or dropping min p to boost creativity or raise min p to increase stability. You shouldn't need to touch anything else!

WTF is Kunoichi-7B?

Kunoichi-7B is a SLERP merger between my previous RP model, Silicon-Maid-7B, and an unreleased model that I had dubbed "Ninja-7B". This model is the result of me attempting to merge an RP focused model which maintained the strengths of Silicon-Maid-7B but further increased the model's brain power. I sought to increase both MT-Bench and EQ-Bench without losing Silicon Maid's strong ability to follow SillyTavern character cards.

Ninja-7B was born from an attempt to turn jan-hq/stealth-v1.2 into a viable model through mergers. Although none of the Ninja prototype models developed to a point where I was happy, it turned out to be a strong model to merge. Combined with Silicon-Maid-7B, this appeared to be a strong merger.

Other Benchmarks

Model	Average	AGIEval	GPT4All	TruthfulQA	Bigbench
Kunoichi-7B	57.54	44.99	74.86	63.72	46.58
OpenPipe/mistral-ft-optimized-1218	56.85	44.74	75.6	59.89	47.17
Silicon-Maid-7B	56.45	44.74	74.26	61.5	45.32
mlabonne/NeuralHermes-2.5-Mistral-7B	53.51	43.67	73.24	55.37	41.76
teknium/OpenHermes-2.5-Mistral-7B	52.42	42.75	72.99	52.99	40.94
openchat/openchat_3.5	51.34	42.67	72.92	47.27	42.51
berkeley-nest/Starling-LM-7B-alpha	51.16	42.06	72.72	47.33	42.53
HuggingFaceH4/zephyr-7b-beta	50.99	37.33	71.83	55.1	39.7

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Safetensors

Model size

7B params

Tensor type

BF16

Model tree for SanjiWatsuki/Kunoichi-7B

Merges

32 models

Quantizations

7 models

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Kunoichi-7B