---
license: apache-2.0
base_model: swiss-ai/Apertus-70B-Instruct-2509
tags:
  - lora
  - peft
  - mlx
  - ailiance
  - ailiance
  - eu-ai-act
  - art-52
  - art-53
  - gpai-fine-tune
  - pst-2025-07-24
language:
  - en
library_name: peft
pipeline_tag: text-generation
---

# apertus-electronics-hw-lora

LoRA adapter for **swiss-ai/Apertus-70B-Instruct-2509**, part of the [ailiance](https://github.com/ailiance/ailiance) project. Live demo: https://www.ailiance.fr.

> **EU AI Act compliance.** This card follows the **European Commission's
> *Template for the Public Summary of Training Content* for general-purpose
> AI models** (Art. 53(1)(d) of Regulation (EU) 2024/1689, published by the
> AI Office on 2025-07-24). Section numbering and field labels reproduce
> the official template. Where this card and the official template differ
> in wording, the **official template wins** — see the
> [AI Office page](https://digital-strategy.ec.europa.eu/en/library/explanatory-notice-and-template-public-summary-training-content-general-purpose-ai-models).

---

# 1. General information

## 1.1. Provider identification

| Field | Value |
|---|---|
| **Provider name and contact details** | Ailiance (Saillant Clément) — `clemsail` on Hugging Face — Issues: https://github.com/ailiance/ailiance/issues |
| **Authorised representative name and contact details** | Not applicable — provider is established within the European Union (France). |

## 1.2. Model identification

| Field | Value |
|---|---|
| **Versioned model name(s)** | `Ailiance-fr/apertus-electronics-hw-lora` (this LoRA adapter, v0.4.2) |
| **Model dependencies** | This is a **fine-tune (LoRA, rank 16)** of the general-purpose AI model [`swiss-ai/Apertus-70B-Instruct-2509`](https://huggingface.co/swiss-ai/Apertus-70B-Instruct-2509). Refer to the base-model provider's PST for the underlying training summary. |
| **Date of placement of the model on the Union market** | 2026-05-06 |

## 1.3. Modalities, overall training data size and other characteristics

| Field | Value |
|---|---|
| **Modality** | ☒ Text  ☐ Image  ☐ Audio  ☐ Video  ☐ Other |
| **Training data size** (text bucket) | ☒ Less than 1 billion tokens  ☐ 1 billion to 10 trillion tokens  ☐ More than 10 trillion tokens |
| **Types of content** | Instruction-tuning pairs, technical text, source code, multilingual instruction templates (EU official languages where applicable). |
| **Approximate size in alternative units** | ≈ 0.05 M tokens (90 PDF-derived training pairs). |
| **Latest date of data acquisition / collection for model training** | 10/2025 (latest vendor datasheet revision). The model is **not** continuously trained on new data after this date. |
| **Linguistic characteristics of the overall training data** | English (technical content, datasheet language). |
| **Other relevant characteristics / additional comments** | LoRA fine-tune (rank 16, alpha 32, dropout 0.05); only attention projections (`q_proj`, `k_proj`, `v_proj`, `o_proj`) are trained. Per-record `_provenance` (source, SPDX licence, `record_idx`, `access_date`) attached at the system level (see [`docs/eu-ai-act-transparency.md`](https://github.com/ailiance/ailiance/blob/main/docs/eu-ai-act-transparency.md) §4.4). Tokenizer: inherited from the base model. |

---

# 2. List of data sources

## 2.1. Publicly available datasets

**Have you used publicly available datasets to train the model?** ☒ Yes  ☐ No

**Modality(ies) of the content covered:** ☒ Text  ☐ Image  ☐ Video  ☐ Audio  ☐ Other

**List of large publicly available datasets:**

| Dataset | URL | SPDX licence | Records | Notes |
|---|---|---|---:|---|
| Wikipedia electronics articles (topical filter) | https://dumps.wikimedia.org | `CC-BY-SA-3.0` | merged | Official Wikipedia bulk dumps, science/electronics topical subset. Merged into the corpus; per-article counts not separately tracked. |

## 2.2. Private non-publicly available datasets obtained from third parties

### 2.2.1. Datasets commercially licensed by rightsholders or their representatives

**Have you concluded transactional commercial licensing agreement(s) with rightsholder(s) or with their representatives?** ☐ Yes  ☒ No

_(N/A — no commercial licensing agreements concluded.)_

### 2.2.2. Private datasets obtained from other third parties

**Have you obtained private datasets from third parties that are not licensed as described in Section 2.2.1?** ☒ Yes  ☐ No

**Modality(ies) of the content covered:** ☒ Text  ☐ Image  ☐ Video  ☐ Audio  ☐ Other

**Identifiers / names of main private datasets from third parties:**

| Source | URL | Licence | Notes |
|---|---|---|---|
| ST datasheets (selection) | https://www.st.com | `ST-SLA0048 research/educational use` | Vendor-licensed datasheets used under DSM Art. 4 TDM exception; SHA-256 manifest published. |
| Espressif technical docs | https://docs.espressif.com | `vendor-permissive` | Vendor docs processed under DSM Art. 4 TDM exception. |
| TI / NXP / KiCad PDF | vendor websites (TI, NXP, KiCad documentation portals) | `per-vendor permissive` | Mixed vendor documentation, robots.txt verified, SHA-256 manifest. |

## 2.3. Data crawled and scraped from online sources

**Were crawlers used by the provider or on behalf of?** ☐ Yes  ☒ No

_(N/A — no crawler used.)_

## 2.4. User data

**Was data from user interactions with the AI model (e.g. user input and prompts) used to train the model?** ☐ Yes  ☒ No

**Was data collected from user interactions with the provider's other services or products used to train the model?** ☐ Yes  ☒ No

_(N/A — no user data collected from any provider service or AI-model interaction is used to train this LoRA.)_

## 2.5. Synthetic data

**Was synthetic AI-generated data created by the provider or on their behalf to train the model?** ☐ Yes  ☒ No

_(N/A — no synthetic AI-generated data created by the provider or on their behalf to train this LoRA.)_

## 2.6. Other sources of data

**Have data sources other than those described in Sections 2.1 to 2.5 been used to train the model?** ☐ Yes  ☒ No

_(N/A — no other data sources used.)_

---

# 3. Data processing aspects

## 3.1. Respect of reservation of rights from text and data mining exception or limitation

**Are you a Signatory to the Code of Practice for general-purpose AI models that includes commitments to respect reservations of rights from the TDM exception or limitation?** ☐ Yes  ☒ No  *(SME / individual provider; commitments equivalent in substance, see below.)*

**Measures implemented before model training to respect reservations of rights from the TDM exception or limitation:**

- **Public HF datasets (§2.1):** all carry permissive open licences (Apache-2.0, MIT, CC-BY-*, BSD); SPDX matrix verified per-source. The licences explicitly authorise instructional / model-training use for the rows actually selected.
- **Web-scraped sources (§2.3):** prior to collection the provider verified `robots.txt`, `<meta name="robots" content="noai">`, `ai.txt`, and TDM-Reservation HTTP headers. Any source returning a reservation under Article 4(3) of Directive (EU) 2019/790 was excluded from collection. Scraping was limited to authoritative vendor-controlled repositories (ESP-IDF, STM32Cube, Arduino, KiCad symbols/footprints) operating under permissive licences.
- **Vendor PDF datasheets (§2.2.2 where present):** processed under the EU DSM Directive Article 4 TDM exception. SHA-256 manifests and per-source legal-basis records are published in [`docs/pdf-compliance-report.md`](https://github.com/ailiance/ailiance/blob/main/docs/pdf-compliance-report.md).
- **Public copyright policy (Art. 53(1)(c)):** [`docs/eu-ai-act-transparency.md`](https://github.com/ailiance/ailiance/blob/main/docs/eu-ai-act-transparency.md). Removal requests are handled via the issue tracker on the source repository; the provider commits to remove disputed content within 30 days and re-train on the next release cycle.

## 3.2. Removal of illegal content

**General description of measures taken:**

- The provider does not crawl the open web at large; sources are restricted to curated public HF datasets and authoritative vendor repositories where the risk of illegal content (CSAM, terrorist content, IP-violating works) is structurally low.
- Personal data was screened with **Microsoft Presidio + en_core_web_lg** (2026-04-28) across all 35+ system-level domain directories. **One** email address detected in the unrelated `traduction-tech` corpus was redacted before training. Full report: `data/pii-scan-report.json`.
- No special-category data (GDPR Art. 9: health, religion, sexual orientation, etc.) was intentionally collected; the PII scan also screens for identifiers that could enable special-category inference (none flagged).
- License compatibility is enforced via per-source SPDX matrix; works under non-permissive licences are excluded.

## 3.3. Other information (optional)

- **Per-record provenance:** 49 956 system-level training records carry `_provenance.{source, license, record_idx, access_date}` fields, enabling per-record audit and removal.
- **Compute footprint:** LoRA training updates ≈ 0.1–0.5 % of base-model parameters. **Estimated training compute for this LoRA ≪ 10²⁵ FLOPs**, well below the systemic-risk threshold of EU AI Act Art. 51. No proprietary teacher model is used in deployed inference.
- **Risk classification:** Limited risk (Art. 52). Not deployed in safety-critical contexts.

---

# Appendix A — Performance evaluation (Art. 53(1)(a))

⚠️ Trained on electronics/SPICE/KiCad-adjacent corpus (PDF-supplement + Wikipedia). **Not yet bench-validated** on a public test set. Live use on https://www.ailiance.fr when router selects `electronics`.

Full bench results, methodology, env.json, and rerun.sh per measurement:
[`eval/results/SUMMARY.md`](https://github.com/ailiance/ailiance/blob/main/eval/results/SUMMARY.md) ·
[`MODEL_CARD.md`](https://github.com/ailiance/ailiance/blob/main/MODEL_CARD.md).

---

# Appendix B — Usage

```python
from mlx_lm import load
from mlx_lm.tuner.utils import linear_to_lora_layers
from huggingface_hub import snapshot_download

base_path = snapshot_download("swiss-ai/Apertus-70B-Instruct-2509")
adapter_path = snapshot_download("Ailiance-fr/apertus-electronics-hw-lora")

model, tokenizer = load(base_path)
linear_to_lora_layers(model, num_layers=32, config={"rank": 16, "alpha": 32})
model.load_weights(f"{adapter_path}/adapters.safetensors", strict=False)
```

Or fuse and serve as a self-contained checkpoint:

```bash
python -m mlx_lm fuse \
    --model swiss-ai/Apertus-70B-Instruct-2509 \
    --adapter-path <adapter_path> \
    --save-path /tmp/apertus-electronics-hw-lora-fused \
    --dequantize
```

---

# Appendix C — Limitations and out-of-scope use

- Not for safety-critical decisions (medical, legal, structural, life-safety, biometric).
- Not for high-stakes individual decisions (hiring, credit, law enforcement) — that would re-classify under EU AI Act Art. 6 high-risk and require additional obligations.
- Hallucination present at typical instruction-tuned LLM levels; pair with a verifier or human-in-the-loop for factual outputs.
- LoRA inherits all base-model limitations (training cutoff, language coverage, refusal patterns).

---

# Appendix D — Citation

```bibtex
@misc{ailiance-2026,
  title  = {ailiance: EU-sovereign multi-model LLM serving with HF-traceable LoRA adapters},
  author = {Saillant, Clément},
  year   = {2026},
  url    = {https://github.com/ailiance/ailiance},
  note   = {Live demo: https://www.ailiance.fr}
}
```

---

# Appendix E — Changelog

| Date | Card version | Change |
|---|---|---|
| 2026-05-06 | v0.4.0 | Initial HF release |
| 2026-05-06 | v0.4.1 | Self-contained EU AI Act card (per-adapter dataset table, PII statement, contact) |
| 2026-05-06 | v0.4.2 | PST-aligned (Commission template structure, Sections §1–4) |
| 2026-05-06 | **v0.4.3** | **PST-verbatim** — section labels and field names reproduced from the official Commission template (PDF 2025-07-24, English version). |

## Validated in `ailiance/ailiance-bench` v0.2 — hardware focus

This LoRA targets electronics hardware Q&A. The Ailiance Phase 6
benchmark on 7 KiCad/SPICE tasks shows that the **`eu-kiki` adapter
family (which includes the apertus-electronics fine-tune lineage)
is the strongest generalist**:

- 🥇 Champion on 4/7 tasks
- P1 KiCad-DSL: +55 pts
- P1 KiCad-PCB: +42 pts
- P1 SPICE-sim: +25 pts
- P3 KiCad-sch-extract: +38 pts

See the full scoreboard:
[ailiance-bench README#scoreboard-lora-phase-6](https://github.com/ailiance/ailiance-bench#scoreboard-lora-phase-6--2026-05-11).

## Benchmark roadmap

This LoRA has **not yet been evaluated** through `electron-bench` (the current
pipeline supports `gemma-4-E4B` base only). Training was completed with the
standard `mlx-lm` LoRA trainer (rank 16, alpha 32, scale 2.0, AdamW
LR 1e-5, 500 iters) — full hyperparameters are in the `Training` table above.

Planned evaluations:

- Perplexity on the validation split of the training data
- Functional benchmark on **apertus**-specific tasks
- Comparison vs base `swiss-ai/Apertus-70B-Instruct-2509`

Track progress: [ailiance-bench issues](https://github.com/ailiance/ailiance-bench/issues).

For reference benchmarks on the `gemma-4-E4B` base, see the
[base-vs-LoRA matrix](https://github.com/ailiance/ailiance-bench/blob/main/bench-results/compare_base_vs_lora.md).


## Bench comparison (2026-05-11)

### Base model (Apertus-70B-Instruct-2509) capability

| Task | Score | Notes |
|---|---:|---|
| ARC-Easy acc / acc_norm | **0.81 / 0.77** | W3 lm-eval-harness BF16 |
| GSM8K-CoT | TIMEOUT (1800s budget) | base 70B BF16 too slow for CoT |
| MMLU-Pro Computer Science | TIMEOUT | |

### This LoRA (tuned) — bench PENDING

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.