Instructions to use AlexandruGirlea/precommiteu-models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use AlexandruGirlea/precommiteu-models 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 AlexandruGirlea/precommiteu-models
# Run inference directly in the terminal:
llama cli -hf AlexandruGirlea/precommiteu-models

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf AlexandruGirlea/precommiteu-models
# Run inference directly in the terminal:
llama cli -hf AlexandruGirlea/precommiteu-models

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 AlexandruGirlea/precommiteu-models
# Run inference directly in the terminal:
./llama-cli -hf AlexandruGirlea/precommiteu-models

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 AlexandruGirlea/precommiteu-models
# Run inference directly in the terminal:
./build/bin/llama-cli -hf AlexandruGirlea/precommiteu-models

Use Docker

docker model run hf.co/AlexandruGirlea/precommiteu-models

LM Studio
Jan

vLLM

How to use AlexandruGirlea/precommiteu-models with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/AlexandruGirlea/precommiteu-models

Ollama
How to use AlexandruGirlea/precommiteu-models with Ollama:
```
ollama run hf.co/AlexandruGirlea/precommiteu-models
```

Unsloth Studio

How to use AlexandruGirlea/precommiteu-models 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 AlexandruGirlea/precommiteu-models 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 AlexandruGirlea/precommiteu-models to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for AlexandruGirlea/precommiteu-models to start chatting

How to use AlexandruGirlea/precommiteu-models with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf AlexandruGirlea/precommiteu-models

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": "AlexandruGirlea/precommiteu-models"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use AlexandruGirlea/precommiteu-models with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf AlexandruGirlea/precommiteu-models

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 "AlexandruGirlea/precommiteu-models" \
  --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 AlexandruGirlea/precommiteu-models with Docker Model Runner:
```
docker model run hf.co/AlexandruGirlea/precommiteu-models
```

Lemonade

How to use AlexandruGirlea/precommiteu-models with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull AlexandruGirlea/precommiteu-models

Run and chat with the model

lemonade run user.precommiteu-models-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use AlexandruGirlea/precommiteu-models with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf AlexandruGirlea/precommiteu-models

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 AlexandruGirlea/precommiteu-models

Run Hermes

hermes

Atomic Chat

precommitEU model bundle

Weights for precommitEU, a local-first EU regulatory compliance scanner for source code.

The scanner itself is a separate pure-Python package on PyPI (pip install precommiteu); this repository holds only the model files it runs. One shared base model serves every role, orchestrator, detector and validator, with a small LoRA adapter per regulation swapped in for detection. Eight EU regulations are covered: GDPR, the EU AI Act, NIS2, DORA, the Cyber Resilience Act, the Digital Services Act, the Digital Markets Act and the Data Act.

precommitEU scanning a Python project for GDPR violations

A real scan, unedited: about 80 seconds on an M-series MacBook, played back at 5×. Replay it step by step at precommit.eu/try, including the cross-file lookup that turns a harmless-looking log line into a confirmed GDPR Art. 32 finding.

No finding without proof. The adapter here is only stage one: it proposes candidates. A validator then has to locate the quoted evidence verbatim in the file, or the candidate is dropped. Confirmed findings cite the line. Candidates that fail validation are reported as advisories and never fail a build.

The Zen of EU Code

Put purpose before collection.
Collect less than you could, and keep it for less time.
Let people know what the system knows.
Make consent a choice, not a trap.
Give people working controls over their data.
Protect what you keep, from design to update.
Children deserve stronger defaults.
Explain automated decisions before they become consequences.
Build systems that fail safely, recover clearly, and report harm responsibly.
Make switching, portability, and interoperability real.

Files

base.gguf: Qwen2.5-Coder-7B-Instruct, Q4_K_M, 4.36 GiB. Shared by every regulation and by all three roles.

`--regulations` value	Regulation	Adapter
`gdpr`	General Data Protection Regulation	`gdpr/detector-adapter.gguf`
`eu_ai_act`	EU AI Act	`eu_ai_act/detector-adapter.gguf`
`eu_data_act`	EU Data Act	`eu_data_act/detector-adapter.gguf`
`dora`	Digital Operational Resilience Act	`dora/detector-adapter.gguf`
`dsa`	Digital Services Act	`dsa/detector-adapter.gguf`
`cra_dma_nis2`	Cyber Resilience Act / DMA / NIS2	`cra_dma_nis2/detector-adapter.gguf`

Every adapter is 77 MiB. SHA256SUMS covers all seven files:

shasum -a 256 -c SHA256SUMS

Which adapters do I need?

Adapter	Regulation	Applies since	Use it when your code…
`gdpr`	General Data Protection Regulation, Regulation (EU) 2016/679	25 May 2018	touches personal data: user records, auth, profiles, logging, exports, analytics
`eu_ai_act`	Artificial Intelligence Act, Regulation (EU) 2024/1689	2 Aug 2026 (phased)	builds, serves or calls AI models; automated decisions, biometrics
`eu_data_act`	Data Act, Regulation (EU) 2023/2854	12 Sep 2025	is a connected/IoT product, or does data sharing, access requests, cloud switching
`dora`	Digital Operational Resilience Act, Regulation (EU) 2022/2554	17 Jan 2025	belongs to a financial entity: payments, trading, ICT risk, incident reporting
`dsa`	Digital Services Act, Regulation (EU) 2022/2065	17 Feb 2024	runs a platform with user content: moderation, recommenders, ads

cra_dma_nis2 is one adapter covering three related acts:

Regulation	Applies since	Use it when your code…
CRA, Cyber Resilience Act, Regulation (EU) 2024/2847	11 Dec 2027 (reporting duties 11 Sep 2026)	ships a product with digital elements: vulnerability handling, secure updates, SBOM
DMA, Digital Markets Act, Regulation (EU) 2022/1925	2 May 2023	belongs to a designated gatekeeper: interoperability, self-preferencing, data reuse
NIS2, Network and Information Systems Directive 2 (EU-wide cybersecurity), Directive (EU) 2022/2555	via national law, due 17 Oct 2024	runs an essential or important entity in one of 18 critical sectors: risk management, incident reporting

Everything above except NIS2 is a Regulation: it applies directly and identically in every member state from its date, with no national step. NIS2 is a Directive, so it binds each member state to legislate rather than applying on its own: what you comply with is your country's implementing act. Transposition was due 17 October 2024; most states have legislated, a few are still behind, so the detail varies by country.

The AI Act applies in stages: prohibitions and AI-literacy duties since 2 February 2025, general-purpose AI model obligations since 2 August 2025, most remaining provisions from 2 August 2026, and Article 6(1) high-risk duties from 2 August 2027.

gdpr is the sensible default for almost any product repo; the other five are opt-in when your sector or feature set matches.

Use each adapter only where its regulation applies. Every adapter is trained and evaluated on code its own regulation governs. Pointed at a codebase outside that scope it produces unreliable output, flagging code that is not a violation under that regulation. Loading all six is not more thorough: only noisier.

Download

pip install -U "huggingface_hub[cli]"

# everything, ~4.9 GB
hf download AlexandruGirlea/precommiteu-models --local-dir ~/.precommiteu/models

# or base + one regulation, ~4.5 GB
hf download AlexandruGirlea/precommiteu-models \
    base.gguf gdpr/detector-adapter.gguf \
    --local-dir ~/.precommiteu/models

Then:

export PRECOMMITEU_MODELS_DIR=~/.precommiteu/models
precommiteu scan src/

The directory names match the scanner's --regulations values; it resolves <models-dir>/<regulation>/detector-adapter.gguf. Full instructions in the installation guide.

Runtime

Served by llama.cpp llama-server (build b4400+), which the scanner starts and stops itself. Every response is constrained by a formal grammar (GBNF), so the model can only produce the exact output shape the scanner expects. Runs on CPU, or faster with Metal on Apple Silicon and CUDA on NVIDIA GPUs.

16 GB of RAM is the practical minimum. A scan runs two servers side by side, one on the plain base model and one on the base model with a detector adapter, and each loads its own full copy of the weights. Measured peak is about 6.3 GiB per server, so roughly 12.6 GiB together.

Most of what sits on top of the 4.36 GiB model file is the context window: the model holds everything it has read so far in memory, at roughly 56 KiB per token, per server. Halving the window with --n-ctx 16384 (the default is 32768) brings the pair down to about 10.8 GiB. On an 8 GB machine the default will fall back to swapping to disk, or the scan will be killed for running out of memory.

These are not general-purpose chat models. The adapters are trained for one task, flagging candidate regulatory violations in source files, and they are used through the scanner, not directly.

Training

One LoRA fine-tune per regulation over the shared base weights, trained in-house on a private labelled dataset. Quality is measured against held-out labelled data rather than standard code benchmarks.

Limitations

Output is a compliance signal, not legal advice. Confirmed findings with source: "precommiteu" are backed by verbatim code evidence; findings with source: "retrieval" cite a matched violation pattern instead and carry no quoted line. Unconfirmed candidates are surfaced separately as advisories and should not gate a build. Coverage is limited to the articles in each regulation pack.

Disclaimer

These models are free and fully open source under the Apache License 2.0, and are provided as is, without warranty or condition of any kind, express or implied. Use is entirely at your own risk. There is no service level and no guarantee of accuracy or fitness for any purpose.

They produce a compliance signal, not legal advice: they can flag code that is not a violation and can miss violations that are present. No output establishes, certifies or evidences compliance with any regulation.

The author is not a lawyer and provides no legal, regulatory or compliance advice. These models do not replace legal analysis. To the maximum extent permitted by applicable law, Alexandru Girlea accepts no liability for any damages, losses, costs, regulatory outcome or misrepresentation arising from their use or from reliance on their output. Have findings reviewed by qualified legal counsel before acting on them.

License

Apache 2.0, for everything here: the adapters, and the quantized base.

base.gguf is derived from Qwen2.5-Coder-7B-Instruct (Copyright Alibaba Cloud / Qwen team, Apache-2.0), converted to GGUF and quantized to Q4_K_M with no other changes to the weights. The adapters are derivative works trained by Alexandru Girlea and are released under the same licence. See NOTICE.

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Model size

8B params

Architecture

qwen2

Hardware compatibility

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Model tree for AlexandruGirlea/precommiteu-models

Base model

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AlexandruGirlea
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precommiteu-models

precommitEU model bundle

The Zen of EU Code

Files

Which adapters do I need?

Download

Links

Runtime

Training

Limitations

Disclaimer

License

Model tree for AlexandruGirlea/precommiteu-models