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

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]

hf download Akira-Papa/akira-papa-1.0-e2b-jp --local-dir 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

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.gguf
akira-papa-1.0-E2B-jp-Q4_K_M.gguf
NOTICE
GEMMA_TERMS.md
MODIFICATIONS.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。

NOTICE
GEMMA_TERMS.md
MODIFICATIONS.md

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位置づけ

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強み

同梱される主なファイル

使い分け

注意点

ベースモデルと利用条件

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