Instructions to use jonprompter/rechly-de-0.5b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use jonprompter/rechly-de-0.5b 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 jonprompter/rechly-de-0.5b:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf jonprompter/rechly-de-0.5b:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf jonprompter/rechly-de-0.5b:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf jonprompter/rechly-de-0.5b:Q4_K_M

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf jonprompter/rechly-de-0.5b:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf jonprompter/rechly-de-0.5b:Q4_K_M

Use Docker

docker model run hf.co/jonprompter/rechly-de-0.5b:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use jonprompter/rechly-de-0.5b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jonprompter/rechly-de-0.5b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jonprompter/rechly-de-0.5b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jonprompter/rechly-de-0.5b:Q4_K_M

Ollama
How to use jonprompter/rechly-de-0.5b with Ollama:
```
ollama run hf.co/jonprompter/rechly-de-0.5b:Q4_K_M
```
Unsloth Desktop

How to use jonprompter/rechly-de-0.5b with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf jonprompter/rechly-de-0.5b:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "jonprompter/rechly-de-0.5b:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use jonprompter/rechly-de-0.5b with Docker Model Runner:
```
docker model run hf.co/jonprompter/rechly-de-0.5b:Q4_K_M
```

Lemonade

How to use jonprompter/rechly-de-0.5b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull jonprompter/rechly-de-0.5b:Q4_K_M

Run and chat with the model

lemonade run user.rechly-de-0.5b-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use jonprompter/rechly-de-0.5b with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf jonprompter/rechly-de-0.5b: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 jonprompter/rechly-de-0.5b:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use jonprompter/rechly-de-0.5b with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf jonprompter/rechly-de-0.5b: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 "jonprompter/rechly-de-0.5b: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"

🇩🇪 Rechly-DE-0.5B (Nano): Ultra-Lightweight German Accounting AI for Edge & 500 MB RAM

The pocket-sized 0.5-Billion parameter Nano Language Model for German Invoicing, the 2025/2026 E-Rechnung Mandate (EN 16931, XRechnung 3.0), and DATEV SKR03/04 Bookkeeping.

Runs 100% on-device inside ~500 MB RAM on Raspberry Pi 4/5, budget 512MB RAM instances, or edge IoT appliances with near-instantaneous 0.3s response times.

🏛️ The Rechly Model Family

⚡ Rechly-DE-3B (Flagship) — 3B params · 1.84 GB · Desktop & Production Server
🪶 Rechly-DE-1.5B (Slim) — 1.5B params · 940 MB · 1 GB VPS & Cloud Run
🔬 Rechly-DE-0.5B (Nano) — 0.5B params · 379 MB · Raspberry Pi & 512MB RAM

⚡ 10-Second Quickstart

1. Run with Ollama:

# Clone and build local Ollama model
huggingface-cli download jonprompter/rechly-de-0.5b rechly-de-0.5b-q4_k_m.gguf Modelfile --local-dir .
ollama create rechly-de-0.5b -f Modelfile
ollama run rechly-de-0.5b

2. Test an invoice in one line:

ollama run rechly-de-0.5b "Rechnung an Blumenladen Rose: 5 Stunden Schaufensterdeko à 55€, Kleinunternehmer ohne USt."

3. Python 4-Liner:

import requests

resp = requests.post("http://localhost:11434/api/chat", json={
    "model": "rechly-de-0.5b",
    "messages": [{"role": "user", "content": "Kategorisiere für SKR03: 'Google Workspace Cloud monatlich 72€'"}],
    "stream": False,
    "options": {"temperature": 0.0}
})
print(resp.json()["message"]["content"])

⚡ Specifications

Parameter	Specification
Base Architecture	`Qwen/Qwen2.5-0.5B-Instruct` (Apache 2.0 open-weights)
Quantization	GGUF `Q4_K_M` (~379 MB file size)
RAM Footprint	~500 MB RAM (Runs comfortably on Raspberry Pi, 512MB RAM devices, Edge IoT)
Inference Speed	180+ tokens/sec / 0.3s per query
Zero-Footprint Guarantee	Hybrid ZUGFeRD 2.3 PDF/A-3, DATEV EXTF 700 Batch Exporter, and Cent-Perfect Reconciler run deterministically in Python with 0 MB LLM memory overhead

🎯 Supported Accounting Domains

Edge Text-to-Invoice Extraction: Fast extraction of client name, prices, quantities, and due dates.
DATEV Account Matching: Quick lookups for SKR03 and SKR04 chart accounts.
GoBD Pre-Auditing: Edge validation of incoming supplier receipts.
B2B Position Text Formalizer: Rewrites colloquial notes into standard invoice descriptions.

⚖️ Legal, GDPR & Benchmark Disclaimers

1. No Tax Advice (Keine Steuerberatung)

Rechly-DE-0.5B provides automated text extraction and schema formatting based on public statutory standards. It is an open-source productivity tool and does not replace certified tax advice by an authorized Steuerberater (gem. StBerG / RDG).

2. GDPR / DSGVO Notice

*The model runs locally on-device with zero network egress, supporting Data Protection by Design & Default (Art. 25 GDPR). However, legal GDPR compliance remains the responsibility of the entity acting as the Data Controller (Art. 4(7) GDPR).*

3. Internal Synthetic Benchmark Notice

Reported performance figures represent internal, synthetic test cases developed within this project. They do not constitute an official government audit or certified third-party endorsement.

4. Trademarks & Third-Party Notice

DATEV® is a registered trademark of DATEV eG. ZUGFeRD is a registered specification of FeRD / AWV e.V. This project is an independent community project and is not affiliated with or endorsed by DATEV eG or FeRD.

🔒 License & Open Source

Licensed under the Apache 2.0 License. Built by the Rechly team for privacy-first, on-premise German accounting.

Downloads last month: 25

GGUF

Model size

0.5B params

Architecture

qwen2

Hardware compatibility

4-bit

Model tree for jonprompter/rechly-de-0.5b

Base model

Qwen/Qwen2.5-0.5B

Finetuned

Qwen/Qwen2.5-0.5B-Instruct

Quantized

(371)

this model