| --- |
| 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. |
| |
