Instructions to use sallani/EUAIAct-Qwen2.5-0.5B-Edge with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sallani/EUAIAct-Qwen2.5-0.5B-Edge with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("sallani/EUAIAct-Qwen2.5-0.5B-Edge")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

llama-cpp-python

How to use sallani/EUAIAct-Qwen2.5-0.5B-Edge with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="sallani/EUAIAct-Qwen2.5-0.5B-Edge",
	filename="euaiact-qwen2.5-0.5b-q4_k_m.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Inference
Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use sallani/EUAIAct-Qwen2.5-0.5B-Edge with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf sallani/EUAIAct-Qwen2.5-0.5B-Edge:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf sallani/EUAIAct-Qwen2.5-0.5B-Edge:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf sallani/EUAIAct-Qwen2.5-0.5B-Edge:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf sallani/EUAIAct-Qwen2.5-0.5B-Edge: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 sallani/EUAIAct-Qwen2.5-0.5B-Edge:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf sallani/EUAIAct-Qwen2.5-0.5B-Edge: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 sallani/EUAIAct-Qwen2.5-0.5B-Edge:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf sallani/EUAIAct-Qwen2.5-0.5B-Edge:Q4_K_M

Use Docker

docker model run hf.co/sallani/EUAIAct-Qwen2.5-0.5B-Edge:Q4_K_M

LM Studio
Jan

vLLM

How to use sallani/EUAIAct-Qwen2.5-0.5B-Edge with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sallani/EUAIAct-Qwen2.5-0.5B-Edge"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sallani/EUAIAct-Qwen2.5-0.5B-Edge",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/sallani/EUAIAct-Qwen2.5-0.5B-Edge:Q4_K_M

Ollama
How to use sallani/EUAIAct-Qwen2.5-0.5B-Edge with Ollama:
```
ollama run hf.co/sallani/EUAIAct-Qwen2.5-0.5B-Edge:Q4_K_M
```

Unsloth Studio

How to use sallani/EUAIAct-Qwen2.5-0.5B-Edge 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 sallani/EUAIAct-Qwen2.5-0.5B-Edge 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 sallani/EUAIAct-Qwen2.5-0.5B-Edge to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for sallani/EUAIAct-Qwen2.5-0.5B-Edge to start chatting

How to use sallani/EUAIAct-Qwen2.5-0.5B-Edge with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "sallani/EUAIAct-Qwen2.5-0.5B-Edge"

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "sallani/EUAIAct-Qwen2.5-0.5B-Edge"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use sallani/EUAIAct-Qwen2.5-0.5B-Edge 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 "sallani/EUAIAct-Qwen2.5-0.5B-Edge"

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 sallani/EUAIAct-Qwen2.5-0.5B-Edge

Run Hermes

hermes

MLX LM

How to use sallani/EUAIAct-Qwen2.5-0.5B-Edge with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "sallani/EUAIAct-Qwen2.5-0.5B-Edge"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "sallani/EUAIAct-Qwen2.5-0.5B-Edge"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "sallani/EUAIAct-Qwen2.5-0.5B-Edge",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Docker Model Runner
How to use sallani/EUAIAct-Qwen2.5-0.5B-Edge with Docker Model Runner:
```
docker model run hf.co/sallani/EUAIAct-Qwen2.5-0.5B-Edge:Q4_K_M
```

Lemonade

How to use sallani/EUAIAct-Qwen2.5-0.5B-Edge with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull sallani/EUAIAct-Qwen2.5-0.5B-Edge:Q4_K_M

Run and chat with the model

lemonade run user.EUAIAct-Qwen2.5-0.5B-Edge-Q4_K_M

List all available models

lemonade list

EUAIAct-Qwen2.5-0.5B-Edge

A compact, offline-first Language Model specialized in EU AI Act & GDPR

Runs on mobile browsers · laptops · edge servers — no cloud, no API, no data transfer

Overview

EUAIAct-Qwen2.5-0.5B-Edge is a 494M parameter Small Language Model fine-tuned on the EU AI Act (Regulation EU 2024/1689) and GDPR compliance corpus.

It is designed to run entirely on-device — in a mobile browser via WebGPU, on Apple Silicon via MLX, or on any machine via GGUF. No internet connection required after download.

Advisory use only — not a substitute for qualified legal counsel.

✦ Key Features

Offline-first — works without internet once downloaded
Cross-platform — Windows · macOS · Linux · Mobile · Browser
Mobile-ready — runs in browser via Transformers.js + WebGPU
Multilingual — French 🇫🇷 and English 🇬🇧
Lightweight — 350 MB (GGUF) to 1 GB (MLX full precision)
Open weights — Apache 2.0, fully auditable

Deployment formats

Format	Size	Runtime	Best for
ONNX INT8	~400 MB	Transformers.js	Browser · Mobile · WebGPU — Windows · macOS · Linux
GGUF Q4_K_M	~350 MB	llama.cpp	Windows · macOS · Linux · Edge servers
MLX BF16	~1.0 GB	mlx_lm	Apple Silicon only (M1/M2/M3/M4)

Quickstart

🌐 Browser & Mobile — Transformers.js + WebGPU

import { pipeline } from '@xenova/transformers';

const ai = await pipeline(
  'text-generation',
  'sallani/EUAIAct-Qwen2.5-0.5B-Edge',
  { device: 'webgpu' }
);

const response = await ai(
  "What are the obligations for high-risk AI system providers under Article 16?",
  { max_new_tokens: 400 }
);

console.log(response[0].generated_text);

🍎 Apple Silicon — MLX

from mlx_lm import load, generate

model, tokenizer = load("sallani/EUAIAct-Qwen2.5-0.5B-Edge")

messages = [
    {"role": "system", "content": "Tu es un expert EU AI Act et RGPD."},
    {"role": "user",   "content": "Qu'est-ce qu'un système IA à haut risque selon l'Article 6 ?"}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
print(generate(model, tokenizer, prompt=prompt, max_tokens=512))

🖥️ llama.cpp — Windows · macOS · Linux

llama-cli \
  -m euaiact-qwen2.5-0.5b-q4_k_m.gguf \
  --chat-template qwen \
  -p "Explain the EU AI Act conformity assessment procedure." \
  -n 512

Model details

Property	Value
Base model	`Qwen/Qwen2.5-0.5B-Instruct` (Apache 2.0)
Fine-tuning method	LoRA — r=8, α=16, 8 layers
Training hardware	Apple M4 Max · MLX
Parameters	494M
Training data	300 curated Q&A pairs — EU AI Act · GDPR
Languages	French 🇫🇷 · English 🇬🇧
Context length	1 024 tokens
License	Apache 2.0

Training corpus

Covers the full regulatory stack for AI compliance in the EU:

Regulation	Coverage
🏛️ EU AI Act (2024/1689)	Articles 1–113, Annexes I–XIII, risk classification, provider & deployer obligations, GPAI, conformity assessment
🔒 GDPR	AI training data, DPIAs, data subject rights, lawful basis

Performance

Format	RAM	Speed (CPU)	Speed (Apple Silicon)
MLX BF16	~2 GB	—	~80 tok/s (M4 Max)
ONNX INT8	~1.2 GB	~20 tok/s	~60 tok/s (WebGPU)
GGUF Q4_K_M	~1 GB	~25 tok/s	~90 tok/s

Intended use

Use case
EU AI Act compliance Q&A	✅
GDPR guidance for AI systems	✅
AI risk classification assistance	✅
GRC documentation support	✅
Formal legal advice	❌
Autonomous compliance decisions	❌

Target users: CISOs, DPOs, GRC consultants, legal counsel, compliance officers.

EU AI Act self-assessment

This model itself falls under limited risk (Article 50 — conversational AI system):

Disclosure obligation: users must be informed they are interacting with AI
No autonomous decisions — advisory only
No personal data processed — on-device inference only
Below GPAI threshold (< 10²³ training FLOPs)

Citation

@misc{allani2026euaiactedge,
  author = {Allani, Sabri},
  title  = {EUAIAct-Qwen2.5-0.5B-Edge: A Sovereign Edge SLM for EU AI Act and GDPR Compliance},
  year   = {2026},
  url    = {https://huggingface.co/sallani/EUAIAct-Qwen2.5-0.5B-Edge},
  note   = {LoRA fine-tune of Qwen2.5-0.5B-Instruct on EU AI Act and GDPR corpus. Apache 2.0.}
}

EU AI Act · GDPR · Edge · Mobile · Offline · Open Weights

Apache 2.0 — Free to use, modify, and distribute

Downloads last month: 154

Safetensors

Model size

0.5B params

Tensor type

BF16

MLX

Hardware compatibility

Quantized

Model tree for sallani/EUAIAct-Qwen2.5-0.5B-Edge

Base model

Qwen/Qwen2.5-0.5B

Finetuned

Qwen/Qwen2.5-0.5B-Instruct

Adapter

(596)

this model

Adapters

1 model