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| <html lang="en"> | |
| <head> | |
| <meta charset="utf-8"> | |
| <meta name="viewport" content="width=device-width, initial-scale=1"> | |
| <title>EuLLM</title> | |
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| <h1>EuLLM — sovereign LLMs for Europe</h1> | |
| <p class="tag">Modelli linguistici aperti, verticali e locali, costruiti in Europa.</p> | |
| <p>EuLLM builds open-weight language models specialised for one domain and one language, small enough to run on your own hardware, and the engine to run them. No data leaves your machine.</p> | |
| <h2>Models</h2> | |
| <table> | |
| <tr><th>Model</th><th>What it does</th><th>Size (Q4_K_M)</th><th>Held-out exam</th></tr> | |
| <tr><td><a href="https://huggingface.co/EuLLM/legal-it-8b" target="_blank">legal-it-8b</a></td><td>Answers questions on Italian law from the statutes retrieved for it, citing them</td><td>5.2 GB</td><td>89.0% (base model: 80.9%)</td></tr> | |
| <tr><td><a href="https://huggingface.co/EuLLM/legal-it-4b" target="_blank">legal-it-4b</a></td><td>The same, for smaller machines</td><td>2.5 GB</td><td>85.1% (base model: 77.6%)</td></tr> | |
| </table> | |
| <ul> | |
| <li><b>How they are used.</b> Both are built for retrieval-augmented, open-book use. The recommended retrieval (BM25, Qwen3-Embedding-0.6B and Qwen3-Reranker-0.6B, about 1.2 GB in all) is documented in the model cards.</li> | |
| <li><b>How they were measured.</b> On 429 held-out questions never seen in training, with paired significance tests and with a check that needs no judge model.</li> | |
| <li><b>Not legal advice:</b> they are tools for consulting the legal texts.</li> | |
| </ul> | |
| <h2>Engine</h2> | |
| <p><a href="https://github.com/eullm/eullm" target="_blank">EuLLM Engine</a> is a single Rust binary for running models like these locally:</p> | |
| <ul> | |
| <li>the same API as Ollama and OpenAI, so existing clients work unchanged;</li> | |
| <li>continuous batching;</li> | |
| <li>no telemetry;</li> | |
| <li>an audit trail designed for the EU AI Act.</li> | |
| </ul> | |
| <h2>Built with European compute</h2> | |
| <p>The models are trained on the Leonardo supercomputer (CINECA, Italy) through the EuroHPC AI Factory allocation EHPC-AIF-2026PG01-1147. We thank the EuroHPC Joint Undertaking for access.</p> | |
| <p><a href="https://www.eullm.eu" target="_blank">eullm.eu</a> · <a href="https://github.com/eullm" target="_blank">GitHub</a></p> | |
| </body> | |
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