Instructions to use mmnga/lightblue-suzume-llama-3-8B-japanese-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/lightblue-suzume-llama-3-8B-japanese-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/lightblue-suzume-llama-3-8B-japanese-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf mmnga/lightblue-suzume-llama-3-8B-japanese-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/lightblue-suzume-llama-3-8B-japanese-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf mmnga/lightblue-suzume-llama-3-8B-japanese-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/lightblue-suzume-llama-3-8B-japanese-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf mmnga/lightblue-suzume-llama-3-8B-japanese-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/lightblue-suzume-llama-3-8B-japanese-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mmnga/lightblue-suzume-llama-3-8B-japanese-gguf:Q4_K_M

Use Docker

docker model run hf.co/mmnga/lightblue-suzume-llama-3-8B-japanese-gguf:Q4_K_M

LM Studio
Jan
Ollama
How to use mmnga/lightblue-suzume-llama-3-8B-japanese-gguf with Ollama:
```
ollama run hf.co/mmnga/lightblue-suzume-llama-3-8B-japanese-gguf:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use mmnga/lightblue-suzume-llama-3-8B-japanese-gguf with Docker Model Runner:
```
docker model run hf.co/mmnga/lightblue-suzume-llama-3-8B-japanese-gguf:Q4_K_M
```

Lemonade

How to use mmnga/lightblue-suzume-llama-3-8B-japanese-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mmnga/lightblue-suzume-llama-3-8B-japanese-gguf:Q4_K_M

Run and chat with the model

lemonade run user.lightblue-suzume-llama-3-8B-japanese-gguf-Q4_K_M

List all available models

lemonade list

Atomic Chat

lightblue-suzume-llama-3-8B-japanese-gguf

lightblueさんが公開しているsuzume-llama-3-8B-japaneseのggufフォーマット変換版です。

imatrixのデータはTFMC/imatrix-dataset-for-japanese-llmを使用して作成しました。

他のモデル

mmnga/lightblue-Karasu-Mixtral-8x22B-v0.1-gguf
mmnga/lightblue-suzume-llama-3-8B-multilingual-gguf
mmnga/lightblue-suzume-llama-3-8B-japanese-gguf
mmnga/lightblue-ao-karasu-72B-gguf
mmnga/lightblue-karasu-1.1B-gguf
mmnga/lightblue-karasu-7B-chat-plus-unleashed-gguf
mmnga/lightblue-qarasu-14B-chat-plus-unleashed-gguf

Usage

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
make -j
./main -m 'lightblue-suzume-llama-3-8B-japanese-Q4_0.gguf' -p "<|begin_of_text|><|start_header_id|>user <|end_header_id|>\n\nこんにちわ<|eot_id|><|start_header_id|>assistant <|end_header_id|>\n\n" -n 128

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GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

1-bit

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8-bit

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lightblue-suzume-llama-3-8B-japanese-gguf

lightblue-suzume-llama-3-8B-japanese-gguf

他のモデル

Usage

Dataset used to train mmnga/lightblue-suzume-llama-3-8B-japanese-gguf