Instructions to use Akira-Papa/akira-papa-1.0-e2b-jp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use Akira-Papa/akira-papa-1.0-e2b-jp with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir akira-papa-1.0-e2b-jp Akira-Papa/akira-papa-1.0-e2b-jp
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Akira-Papa/akira-papa-1.0-e2b-jp 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 Akira-Papa/akira-papa-1.0-e2b-jp:Q4_K_M # Run inference directly in the terminal: llama cli -hf Akira-Papa/akira-papa-1.0-e2b-jp:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Akira-Papa/akira-papa-1.0-e2b-jp:Q4_K_M # Run inference directly in the terminal: llama cli -hf Akira-Papa/akira-papa-1.0-e2b-jp: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 Akira-Papa/akira-papa-1.0-e2b-jp:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Akira-Papa/akira-papa-1.0-e2b-jp: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 Akira-Papa/akira-papa-1.0-e2b-jp:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Akira-Papa/akira-papa-1.0-e2b-jp:Q4_K_M
Use Docker
docker model run hf.co/Akira-Papa/akira-papa-1.0-e2b-jp:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use Akira-Papa/akira-papa-1.0-e2b-jp with Ollama:
ollama run hf.co/Akira-Papa/akira-papa-1.0-e2b-jp:Q4_K_M
- Unsloth Desktop
- Pi
How to use Akira-Papa/akira-papa-1.0-e2b-jp with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "Akira-Papa/akira-papa-1.0-e2b-jp"
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "mlx-lm": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "Akira-Papa/akira-papa-1.0-e2b-jp" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use Akira-Papa/akira-papa-1.0-e2b-jp with Docker Model Runner:
docker model run hf.co/Akira-Papa/akira-papa-1.0-e2b-jp:Q4_K_M
- Lemonade
How to use Akira-Papa/akira-papa-1.0-e2b-jp with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Akira-Papa/akira-papa-1.0-e2b-jp:Q4_K_M
Run and chat with the model
lemonade run user.akira-papa-1.0-e2b-jp-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use Akira-Papa/akira-papa-1.0-e2b-jp with Hermes Agent:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "Akira-Papa/akira-papa-1.0-e2b-jp"
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default Akira-Papa/akira-papa-1.0-e2b-jp
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use Akira-Papa/akira-papa-1.0-e2b-jp with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "Akira-Papa/akira-papa-1.0-e2b-jp"
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "Akira-Papa/akira-papa-1.0-e2b-jp" \ --custom-provider-id mlx-lm \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
akira-papa-1.0-e2b-jp
akira-papa-1.0-e2b-jp は、Gemma 4 E2B-it を土台に、Akira-Papa が作成・整理した日本語データ、teacher-guided distill、route-aware tuning を重ねた日本語 compact release です。
実務返信、告知文、短いレビュー、初学者向けの短い説明を主目的に整えています。
位置づけ
- 現在の系統:
E2B compact - Hugging Face repo:
https://huggingface.co/Akira-Papa/akira-papa-1.0-e2b-jp Gemma 4 E2B-itベースの日本語 compact ライン
主な用途
- 実務返信
- 告知文
- 短いレビューコメント
- 初学者向けの短い説明
- 軽い code explain / code fix
強み
- 実務の短い返答
- 告知文の下書き
- 初学者向けの短い説明
同梱される主なファイル
- merged model 一式
akira-papa-1.0-E2B-jp-Q8_0.ggufakira-papa-1.0-E2B-jp-Q4_K_M.ggufNOTICEGEMMA_TERMS.mdMODIFICATIONS.md
使い分け
Q8_0: 品質優先Q4_K_M: 容量優先- merged model: MLX 系ワークフロー向け
注意点
- 長文 writer や heavy coding の完全代替ではありません
- quality は weight 単体ではなく、preset / cleanup / route-aware 運用も前提です
ベースモデルと利用条件
このモデルのベースは google/gemma-4-E2B-it です。
利用前に次を確認してください。
NOTICEGEMMA_TERMS.mdMODIFICATIONS.md
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Model size
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Tensor type
BF16
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