Instructions to use mmnga/shisa-7b-v1-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 mmnga/shisa-7b-v1-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 mmnga/shisa-7b-v1-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf mmnga/shisa-7b-v1-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 mmnga/shisa-7b-v1-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf mmnga/shisa-7b-v1-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 mmnga/shisa-7b-v1-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf mmnga/shisa-7b-v1-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 mmnga/shisa-7b-v1-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mmnga/shisa-7b-v1-gguf:Q4_K_M

Use Docker

docker model run hf.co/mmnga/shisa-7b-v1-gguf:Q4_K_M

LM Studio
Jan
Ollama
How to use mmnga/shisa-7b-v1-gguf with Ollama:
```
ollama run hf.co/mmnga/shisa-7b-v1-gguf:Q4_K_M
```

Unsloth Studio

How to use mmnga/shisa-7b-v1-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 mmnga/shisa-7b-v1-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 mmnga/shisa-7b-v1-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for mmnga/shisa-7b-v1-gguf to start chatting

Docker Model Runner
How to use mmnga/shisa-7b-v1-gguf with Docker Model Runner:
```
docker model run hf.co/mmnga/shisa-7b-v1-gguf:Q4_K_M
```

Lemonade

How to use mmnga/shisa-7b-v1-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mmnga/shisa-7b-v1-gguf:Q4_K_M

Run and chat with the model

lemonade run user.shisa-7b-v1-gguf-Q4_K_M

List all available models

lemonade list

Atomic Chat

shisa-7b-v1-gguf

augmxntさんが公開しているshisa-7b-v1のggufフォーマット変換版です。

Usage

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
make -j
./main -m 'shisa-7b-v1-q4_0.gguf' -n 512 -p '[INST] <<sys>> あなたは熱狂的なポケモンファンです。 \n<</sys>> ポケモンの中で1番強いのはどのキャラクターですか。最強の者をひとつだけ挙げて下さい。その選択理由を説明してください。[/INST]' --temp 0.5

./main -m 'shisa-7b-v1-q4_K_M.gguf' -n 512 -p '[INST] <<sys>> あなたは日本語を英語に直す翻訳者です。 \n<</sys>> ポケモンの中で1番強いのはどのキャラクターですか。最強の者をひとつだけ挙げて下さい。その選択理由を説明してください。[/INST]' --temp 0.5

変換スクリプト

convert_shisa.py

python3 convert_shisa.py <model_dir> --outtype f16 --vocabtype spm

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GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

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mmnga
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shisa-7b-v1-gguf

shisa-7b-v1-gguf

Usage

変換スクリプト

Datasets used to train mmnga/shisa-7b-v1-gguf