Instructions to use rinna/nekomata-7b-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use rinna/nekomata-7b-gguf with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf rinna/nekomata-7b-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf rinna/nekomata-7b-gguf:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf rinna/nekomata-7b-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf rinna/nekomata-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 rinna/nekomata-7b-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf rinna/nekomata-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 rinna/nekomata-7b-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf rinna/nekomata-7b-gguf:Q4_K_M

Use Docker

docker model run hf.co/rinna/nekomata-7b-gguf:Q4_K_M

LM Studio
Jan

vLLM

How to use rinna/nekomata-7b-gguf with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/rinna/nekomata-7b-gguf:Q4_K_M

Ollama
How to use rinna/nekomata-7b-gguf with Ollama:
```
ollama run hf.co/rinna/nekomata-7b-gguf:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use rinna/nekomata-7b-gguf with Docker Model Runner:
```
docker model run hf.co/rinna/nekomata-7b-gguf:Q4_K_M
```

Lemonade

How to use rinna/nekomata-7b-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull rinna/nekomata-7b-gguf:Q4_K_M

Run and chat with the model

lemonade run user.nekomata-7b-gguf-Q4_K_M

List all available models

lemonade list

Atomic Chat

`rinna/nekomata-7b-gguf`

Overview

The model is the GGUF version of rinna/nekomata-7b. It can be used with llama.cpp for lightweight inference.

Quantization of this model may cause stability issue in GPTQ, AWQ and GGUF q4_0. We recommend GGUF q4_K_M for 4-bit quantization.

See rinna/nekomata-7b for details about model architecture and data.

Contributors
Release date

December 22, 2023

How to use the model

See llama.cpp for more usage details.

git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp
make

MODEL_PATH=/path/to/nekomata-7b-gguf/nekomata-7b.Q4_K_M.gguf
MAX_N_TOKENS=128
PROMPT="西田幾多郎は、"

./main -m ${MODEL_PATH} -n ${MAX_N_TOKENS} -p "${PROMPT}"

Tokenization

Please refer to rinna/nekomata-7b for tokenization details.

How to cite

@misc{rinna-nekomata-7b-gguf,
    title = {rinna/nekomata-7b-gguf},
    author = {Wakatsuki, Toshiaki and Zhao, Tianyu and Sawada, Kei},
    url = {https://huggingface.co/rinna/nekomata-7b-gguf}
}

@inproceedings{sawada2024release,
    title = {Release of Pre-Trained Models for the {J}apanese Language},
    author = {Sawada, Kei and Zhao, Tianyu and Shing, Makoto and Mitsui, Kentaro and Kaga, Akio and Hono, Yukiya and Wakatsuki, Toshiaki and Mitsuda, Koh},
    booktitle = {Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)},
    month = {5},
    year = {2024},
    pages = {13898--13905},
    url = {https://aclanthology.org/2024.lrec-main.1213},
    note = {\url{https://arxiv.org/abs/2404.01657}}
}