Instructions to use jonprompter/rechly-de-1.5b 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-1.5b 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-1.5b:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf jonprompter/rechly-de-1.5b: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-1.5b:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf jonprompter/rechly-de-1.5b: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-1.5b:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf jonprompter/rechly-de-1.5b:Q4_K_M

Use Docker

docker model run hf.co/jonprompter/rechly-de-1.5b:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use jonprompter/rechly-de-1.5b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jonprompter/rechly-de-1.5b"
# 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-1.5b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jonprompter/rechly-de-1.5b:Q4_K_M

Ollama
How to use jonprompter/rechly-de-1.5b with Ollama:
```
ollama run hf.co/jonprompter/rechly-de-1.5b:Q4_K_M
```
Unsloth Desktop

How to use jonprompter/rechly-de-1.5b 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-1.5b: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-1.5b:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use jonprompter/rechly-de-1.5b with Docker Model Runner:
```
docker model run hf.co/jonprompter/rechly-de-1.5b:Q4_K_M
```

Lemonade

How to use jonprompter/rechly-de-1.5b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull jonprompter/rechly-de-1.5b:Q4_K_M

Run and chat with the model

lemonade run user.rechly-de-1.5b-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use jonprompter/rechly-de-1.5b 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-1.5b: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-1.5b:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use jonprompter/rechly-de-1.5b 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-1.5b: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-1.5b: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-1.5B (Slim): 100% Local German Accounting AI for 1 GB VPS

The ultra-lightweight 1.5-Billion parameter Small Language Model (SLM) for German Invoicing, the 2025/2026 E-Rechnung Mandate (EN 16931, XRechnung 3.0, ZUGFeRD 2.3), GoBD Pre-Audit, and DATEV SKR03/04 Bookkeeping.

Runs 100% on-device inside ~1.0 GB RAM on budget 1 GB VPS micro-instances, Cloud Run, or low-cost edge containers with sub-second latency.

🏛️ 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-1.5b rechly-de-1.5b-q4_k_m.gguf Modelfile --local-dir .
ollama create rechly-de-1.5b -f Modelfile
ollama run rechly-de-1.5b

2. Test an invoice in one line:

ollama run rechly-de-1.5b "Rechnung an Kanzlei Schmidt: 10 Std. IT-Forensik à 140€ und 1x Anfahrt Pauschal 80€, zahlbar in 14 Tagen."

3. Python 4-Liner:

import requests

resp = requests.post("http://localhost:11434/api/chat", json={
    "model": "rechly-de-1.5b",
    "messages": [{"role": "user", "content": "Kategorisiere für SKR03 und SKR04: 'Geschäftsessen im Restaurant 140€ mit Kunde'"}],
    "stream": False,
    "options": {"temperature": 0.0}
})
print(resp.json()["message"]["content"])

⚡ Specifications

Parameter	Specification
Base Architecture	`Qwen/Qwen2.5-1.5B-Instruct` (Apache 2.0 open-weights)
Quantization	GGUF `Q4_K_M` (~940 MB file size)
RAM Footprint	~1.0 GB RAM (Runs comfortably on 1 GB VPS, Cloud Run, Micro Instances)
Inference Speed	120+ tokens/sec on Apple Silicon Metal / 0.7s per query
Internal Test Suite	100.0% Pass (77/77 internal tests) on synthetic E-Rechnung XRechnung 3.0 & DATEV SKR edge cases
Zero-Footprint Utilities	Hybrid ZUGFeRD 2.3 PDF/A-3 generator, DATEV EXTF 700 batch utility, and reconciler run deterministically in Python with 0 MB LLM memory overhead

🎯 Supported Accounting Domains

Text-to-Invoice (Strict JSON Extraction): Automatic detection of Kleinunternehmer (§ 19 UStG), Reverse Charge (§ 13b UStG, § 3a UStG), and Export (§ 4 Nr. 1a UStG).
E-Rechnung EN 16931 / XRechnung 3.0: Validated BT-10 Leitweg-ID extraction and UBL 2.1 XML serialization.
DATEV SKR03 / SKR04 Mapping: 30+ chart accounts with statutory 70% Bewirtung limits.
GoBD § 14 Abs. 4 Pre-Audit: Pre-checks incoming invoices to assist in identifying missing mandatory items.
Multi-Stage Mahnwesen: Dunning letters for Stufe 1, 2, and 3.
B2B Line Item Rewriter: Formalizes informal notes into professional position texts.

⚖️ Legal, GDPR & Benchmark Disclaimers

1. No Tax Advice (Keine Steuerberatung)

Rechly-DE-1.5B 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 benchmark figures represent internal, synthetic test suites 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.

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GGUF

Model size

2B params

Architecture

qwen2

Hardware compatibility

4-bit

Model tree for jonprompter/rechly-de-1.5b

Base model

Qwen/Qwen2.5-1.5B

Finetuned

Qwen/Qwen2.5-1.5B-Instruct

Quantized

(374)

this model