docs: add upstream base model official evaluations

README.md

@@ -276,3 +276,30 @@ For reference benchmarks on the `gemma-4-E4B` base, see the
 Production usage: served via gateway alias `ailiance-apertus-<domain>` on
 <https://www.ailiance.fr> through the Apertus multi-LoRA hot-swap server
 (Studio :9322, 1 base + 10 LoRA dynamic swap, ~40GB VRAM).
+
+## Upstream base model — official evaluations
+
+This LoRA fine-tunes [`swiss-ai/Apertus-70B-Instruct-2509`](https://huggingface.co/swiss-ai/Apertus-70B-Instruct-2509),
+the EU-sovereign open-source LLM released by the Swiss AI Initiative. Below are
+the **official scores** reported in the [Apertus Tech Report](https://arxiv.org/abs/2509.14233)
+on a suite of multilingual reasoning benchmarks.
+
+| Model                       | Avg   | ARC   | HellaSwag | WinoGrande | XNLI  | XCOPA | PIQA  |
+|-----------------------------|------:|------:|----------:|-----------:|------:|------:|------:|
+| **Apertus-70B** (this base) | 67.5  | 70.6  | 64.0      | 73.3       | 45.3  | 69.8  | 81.9  |
+| Apertus-8B                  | 65.8  | 72.7  | 59.8      | 70.6       | 45.2  | 66.5  | 79.8  |
+| Llama3.1-70B                | 67.3  | 74.4  | 56.5      | 79.4       | 44.3  | 66.7  | 82.3  |
+| Qwen2.5-72B                 | 69.8  | 76.2  | 67.5      | 78.0       | 46.9  | 68.2  | 82.0  |
+| OLMo2-32B                   | 67.7  | 76.2  | 66.7      | 78.6       | 42.9  | 60.1  | 82.1  |
+| EuroLLM-9B                  | 62.8  | 67.9  | 57.9      | 68.8       | 41.5  | 61.1  | 79.6  |
+
+Many additional benchmark evaluations (pretraining/post-training phases,
+multilingual in ~100 languages, long-context) are in Section 5 of the
+[Apertus Tech Report](https://arxiv.org/abs/2509.14233).
+
+**Source:** [official Apertus-70B-Instruct-2509 model card](https://huggingface.co/swiss-ai/Apertus-70B-Instruct-2509).
+
+> **Reading these alongside this LoRA:** Apertus-70B is EU AI Act-compliant
+> (`Apertus_EU_Code_of_Practice.pdf`, `Apertus_EU_Public_Summary.pdf` included
+> in upstream weights). This LoRA inherits that compliance plus the
+> general-capability floor shown above, then adds domain specialization.