chore: rebrand card to Ailiance
Browse files
README.md
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- peft
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- mlx
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- ailiance
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- eu-ai-act
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- art-52
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- art-53
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# devstral-cpp-lora
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LoRA adapter for **mistralai/Devstral-Small-2-24B-Instruct-2512**, part of the [ailiance](https://github.com/
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> **EU AI Act compliance.** This card follows the **European Commission's
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> *Template for the Public Summary of Training Content* for general-purpose
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| Field | Value |
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|---|---|
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| **Provider name and contact details** |
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| **Authorised representative name and contact details** | Not applicable — provider is established within the European Union (France). |
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## 1.2. Model identification
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| **Approximate size in alternative units** | ≈ 0.6 M tokens (2 850 rows × ≈ 200 tokens/row). |
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| **Latest date of data acquisition / collection for model training** | 10/2025 (last commit on scraped repos). The model is **not** continuously trained on new data after this date. |
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| **Linguistic characteristics of the overall training data** | English (technical instruction language). No other natural languages. |
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| **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/
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---
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- **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.
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- **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.
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- **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/
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- **Public copyright policy (Art. 53(1)(c)):** [`docs/eu-ai-act-transparency.md`](https://github.com/
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## 3.2. Removal of illegal content
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**HumanEval** (custom Studio scorer; EvalPlus extra-tests not run — Linux-only sandbox): base 87.20 → +cpp 85.98 = **−1.22 pts**. For rigorous HumanEval+ ��, sample re-scoring on Linux is required (samples preserved at `eval/results/2026-05-04/devstral-cpp-fused-humanevalplus/`).
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Full bench results, methodology, env.json, and rerun.sh per measurement:
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[`eval/results/SUMMARY.md`](https://github.com/
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[`MODEL_CARD.md`](https://github.com/
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---
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# Appendix D — Citation
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```bibtex
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@misc{
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title = {
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author = {Saillant, Clément},
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year = {2026},
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url = {https://github.com/
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note = {Live demo: https://www.ailiance.fr}
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}
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```
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- peft
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- mlx
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- ailiance
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- ailiance
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- eu-ai-act
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- art-52
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- art-53
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# devstral-cpp-lora
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LoRA adapter for **mistralai/Devstral-Small-2-24B-Instruct-2512**, part of the [ailiance](https://github.com/ailiance/ailiance) project. Live demo: https://www.ailiance.fr.
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> **EU AI Act compliance.** This card follows the **European Commission's
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> *Template for the Public Summary of Training Content* for general-purpose
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| Field | Value |
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|---|---|
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| **Provider name and contact details** | Ailiance (Saillant Clément) — `clemsail` on Hugging Face — Issues: https://github.com/ailiance/ailiance/issues |
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| **Authorised representative name and contact details** | Not applicable — provider is established within the European Union (France). |
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## 1.2. Model identification
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| **Approximate size in alternative units** | ≈ 0.6 M tokens (2 850 rows × ≈ 200 tokens/row). |
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| **Latest date of data acquisition / collection for model training** | 10/2025 (last commit on scraped repos). The model is **not** continuously trained on new data after this date. |
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| **Linguistic characteristics of the overall training data** | English (technical instruction language). No other natural languages. |
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| **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. |
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---
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- **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.
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- **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.
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- **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).
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- **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.
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## 3.2. Removal of illegal content
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**HumanEval** (custom Studio scorer; EvalPlus extra-tests not run — Linux-only sandbox): base 87.20 → +cpp 85.98 = **−1.22 pts**. For rigorous HumanEval+ ��, sample re-scoring on Linux is required (samples preserved at `eval/results/2026-05-04/devstral-cpp-fused-humanevalplus/`).
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Full bench results, methodology, env.json, and rerun.sh per measurement:
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[`eval/results/SUMMARY.md`](https://github.com/ailiance/ailiance/blob/main/eval/results/SUMMARY.md) ·
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[`MODEL_CARD.md`](https://github.com/ailiance/ailiance/blob/main/MODEL_CARD.md).
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---
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# Appendix D — Citation
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```bibtex
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@misc{ailiance-2026,
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title = {ailiance: EU-sovereign multi-model LLM serving with HF-traceable LoRA adapters},
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author = {Saillant, Clément},
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year = {2026},
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url = {https://github.com/ailiance/ailiance},
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note = {Live demo: https://www.ailiance.fr}
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}
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```
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