Instructions to use QuantFactory/Kunoichi-DPO-v2-7B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/Kunoichi-DPO-v2-7B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/Kunoichi-DPO-v2-7B-GGUF",
	filename="Kunoichi-DPO-v2-7B.Q2_K.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use QuantFactory/Kunoichi-DPO-v2-7B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantFactory/Kunoichi-DPO-v2-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Kunoichi-DPO-v2-7B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantFactory/Kunoichi-DPO-v2-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Kunoichi-DPO-v2-7B-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf QuantFactory/Kunoichi-DPO-v2-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/Kunoichi-DPO-v2-7B-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf QuantFactory/Kunoichi-DPO-v2-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/Kunoichi-DPO-v2-7B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/Kunoichi-DPO-v2-7B-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use QuantFactory/Kunoichi-DPO-v2-7B-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/Kunoichi-DPO-v2-7B-GGUF:Q4_K_M
```

Unsloth Studio new

How to use QuantFactory/Kunoichi-DPO-v2-7B-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for QuantFactory/Kunoichi-DPO-v2-7B-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for QuantFactory/Kunoichi-DPO-v2-7B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for QuantFactory/Kunoichi-DPO-v2-7B-GGUF to start chatting

Docker Model Runner
How to use QuantFactory/Kunoichi-DPO-v2-7B-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/Kunoichi-DPO-v2-7B-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/Kunoichi-DPO-v2-7B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/Kunoichi-DPO-v2-7B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Kunoichi-DPO-v2-7B-GGUF-Q4_K_M

List all available models

lemonade list

QuantFactory/Kunoichi-DPO-v2-7B-GGUF

This is quantized version of SanjiWatsuki/Kunoichi-DPO-v2-7B created using llama.cpp

Original Model Card

Model	MT Bench	EQ Bench	MMLU	Logic Test
GPT-4-Turbo	9.32	-	-	-
GPT-4	8.99	62.52	86.4	0.86
Kunoichi-DPO-v2-7B	8.51	42.18	64.94	0.58
Mixtral-8x7B-Instruct	8.30	44.81	70.6	0.75
Kunoichi-DPO-7B	8.29	41.60	64.83	0.59
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

Model	Average	AGIEval	GPT4All	TruthfulQA	Bigbench
Kunoichi-DPO-7B	58.4	45.08	74	66.99	47.52
Kunoichi-DPO-v2-7B	58.31	44.85	75.05	65.69	47.65
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

Model	AlpacaEval2	Length
GPT-4	23.58%	1365
GPT-4 0314	22.07%	1371
Mistral Medium	21.86%	1500
Mixtral 8x7B v0.1	18.26%	1465
Kunoichi-DPO-v2	17.19%	1785
Claude 2	17.19%	1069
Claude	16.99%	1082
Gemini Pro	16.85%	1315
GPT-4 0613	15.76%	1140
Claude 2.1	15.73%	1096
Mistral 7B v0.2	14.72%	1676
GPT 3.5 Turbo 0613	14.13%	1328
LLaMA2 Chat 70B	13.87%	1790
LMCocktail-10.7B-v1	13.15%	1203
WizardLM 13B V1.1	11.23%	1525
Zephyr 7B Beta	10.99%	1444
OpenHermes-2.5-Mistral (7B)	10.34%	1107
GPT 3.5 Turbo 0301	9.62%	827
Kunoichi-7B	9.38%	1492
GPT 3.5 Turbo 1106	9.18%	796
GPT-3.5	8.56%	1018
Phi-2 DPO	7.76%	1687
LLaMA2 Chat 13B	7.70%	1513

Downloads last month: 2

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

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