Instructions to use tokimoa/jp-invoice-reader-2b-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 tokimoa/jp-invoice-reader-2b-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 tokimoa/jp-invoice-reader-2b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf tokimoa/jp-invoice-reader-2b-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 tokimoa/jp-invoice-reader-2b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf tokimoa/jp-invoice-reader-2b-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 tokimoa/jp-invoice-reader-2b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf tokimoa/jp-invoice-reader-2b-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 tokimoa/jp-invoice-reader-2b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf tokimoa/jp-invoice-reader-2b-GGUF:Q4_K_M

Use Docker

docker model run hf.co/tokimoa/jp-invoice-reader-2b-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use tokimoa/jp-invoice-reader-2b-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tokimoa/jp-invoice-reader-2b-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tokimoa/jp-invoice-reader-2b-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/tokimoa/jp-invoice-reader-2b-GGUF:Q4_K_M

Ollama
How to use tokimoa/jp-invoice-reader-2b-GGUF with Ollama:
```
ollama run hf.co/tokimoa/jp-invoice-reader-2b-GGUF:Q4_K_M
```

Unsloth Studio

How to use tokimoa/jp-invoice-reader-2b-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 tokimoa/jp-invoice-reader-2b-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 tokimoa/jp-invoice-reader-2b-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for tokimoa/jp-invoice-reader-2b-GGUF to start chatting

How to use tokimoa/jp-invoice-reader-2b-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf tokimoa/jp-invoice-reader-2b-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "tokimoa/jp-invoice-reader-2b-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use tokimoa/jp-invoice-reader-2b-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf tokimoa/jp-invoice-reader-2b-GGUF:Q4_K_M

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 "tokimoa/jp-invoice-reader-2b-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use tokimoa/jp-invoice-reader-2b-GGUF with Docker Model Runner:
```
docker model run hf.co/tokimoa/jp-invoice-reader-2b-GGUF:Q4_K_M
```

Lemonade

How to use tokimoa/jp-invoice-reader-2b-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull tokimoa/jp-invoice-reader-2b-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.jp-invoice-reader-2b-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use tokimoa/jp-invoice-reader-2b-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf tokimoa/jp-invoice-reader-2b-GGUF:Q4_K_M

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 tokimoa/jp-invoice-reader-2b-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

jp-invoice-reader-2b-GGUF — 日本語帳票リーダー

日本語帳票（請求書・見積書・納品書・領収書）の読み取りに特化した jp-invoice-reader-2b のGGUF版です。llama.cppでWindows/LinuxのCPU・GPUで動きます。

ファイル

ファイル	サイズ	用途
jp-invoice-reader-2b-Q8_0.gguf	1.7GB	推奨（抽出・QAとも）
jp-invoice-reader-2b-Q4_K_M.gguf	1.0GB	QA用途向け（下記注意）
jp-invoice-reader-2b-f16.gguf	3.2GB	参照用
mmproj-jp-invoice-reader-2b-f16.gguf	781MB	視覚エンコーダ（必須・どの量子化とも組み合わせ可）

実測（公開中のbf16版と同一プロトコル: 未見評価100枚・QA400問・抽出700フィールド）

指標	bf16（MLX公開版）	Q8_0	Q4_K_M
ja-docs QA（実写風6問）	6/6	6/6	6/6
帳票QA	93.5%	91.8%	90.5%
抽出フィールド一致（厳密）	90.1%	84.3%	67.3%

厳密一致の低下について実態を補足します（同一100枚で追加検証）:

Q8_0の低下分はほぼ形式ゆれです。 消費税額を{"rate": 10, "amount": 1449000}のようにネストで返す揺れが増え、厳密一致では×になりますが、税額の値ベースでは99/100が正解でした。金額の読み取り能力自体は維持されています。後段でJSONを受けるときはネスト形式も許容してください。
Q4_K_MはJSON構文の崩れが100枚中15件発生します。 抽出（長い構造化出力）には非推奨で、QA用途向けです。

使い方

llama-server -m jp-invoice-reader-2b-Q8_0.gguf --mmproj mmproj-jp-invoice-reader-2b-f16.gguf \
  --port 8080 --jinja -c 8192 --image-min-tokens 64 --image-max-tokens 4096

運用上の注意（いずれも実測に基づく）:

APIでは画像をテキストより先に置いてください（contentの並びを[image_url, text]にする）。逆順は精度が下がります。
-c 8192等でコンテキスト長を指定してください。 無指定はモデル宣言の262Kで確保しようとしてメモリ不足になります。
--image-max-tokens 4096を推奨します。 帳票は高解像度のため、既定では画像が過剰に縮小され数値の読み取りが落ちます。

プロンプト例: 抽出は「この帳票から主要項目を抽出してJSONで出力してください。」、QAは「この請求書の合計金額は？」等の自由質問です。

制約

合成帳票ドメインで学習しています。実写スキャン・手書き・特殊レイアウトは未検証で、写真由来の帳票では精度が下がる可能性があります。

ライセンスと帰属

ベース: Qwen/Qwen3-VL-2B-Instruct（Apache-2.0）
学習データ・評価方法の詳細は本体repoを参照してください

Developed by tokimoa — データを外に出さないAI活用を支援しています

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GGUF

Model size

2B params

Architecture

qwen3vl

Hardware compatibility

4-bit

8-bit

16-bit

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