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chore: rebrand card to Ailiance

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  1. README.md +12 -12
README.md CHANGED
@@ -6,7 +6,7 @@ tags:
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  - peft
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  - mlx
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  - ailiance
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- - eu-kiki
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  - eu-ai-act
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  - art-52
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  - art-53
@@ -20,7 +20,7 @@ library_name: peft
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  # devstral-python-lora
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- LoRA adapter for **mistralai/Devstral-Small-2-24B-Instruct-2512**, part of the [ailiance](https://github.com/L-electron-Rare/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
@@ -38,7 +38,7 @@ LoRA adapter for **mistralai/Devstral-Small-2-24B-Instruct-2512**, part of the [
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  | Field | Value |
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  |---|---|
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- | **Provider name and contact details** | L'Électron Rare (Saillant Clément) — `clemsail` on Hugging Face — Issues: https://github.com/L-electron-Rare/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
@@ -59,7 +59,7 @@ LoRA adapter for **mistralai/Devstral-Small-2-24B-Instruct-2512**, part of the [
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  | **Approximate size in alternative units** | ≈ 0.6 M tokens (2 850 rows × ≈ 200 tokens/row, single-pass). |
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  | **Latest date of data acquisition / collection for model training** | 11/2024 (StarCoder2 Self-Instruct release). The model is **not** continuously trained on new data after this date. |
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  | **Linguistic characteristics of the overall training data** | English (primary, instruction language); French (system-prompt context). No other natural languages in training rows. |
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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/L-electron-Rare/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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@@ -129,8 +129,8 @@ _(N/A — no other data sources used.)_
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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/L-electron-Rare/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/L-electron-Rare/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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@@ -151,11 +151,11 @@ _(N/A — no other data sources used.)_
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  # Appendix A — Performance evaluation (Art. 53(1)(a))
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- **HumanEval+** (EvalPlus official Linux scorer, 164 problems, greedy, 1 sample): base 87.20 / 82.90 → +python 86.00 / 81.10. **Δ HE+ = −1.80 pts** vs base. Scoring on `kx6tm-23` (Proxmox PVE 6.17). Full reproducer in [`eval/results/2026-05-04/devstral-python-fused-humanevalplus/rerun.sh`](https://github.com/L-electron-Rare/ailiance/blob/main/eval/results/2026-05-04/devstral-python-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/L-electron-Rare/ailiance/blob/main/eval/results/SUMMARY.md) ·
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- [`MODEL_CARD.md`](https://github.com/L-electron-Rare/ailiance/blob/main/MODEL_CARD.md).
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  ---
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@@ -198,11 +198,11 @@ python -m mlx_lm fuse \
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  # Appendix D — Citation
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  ```bibtex
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- @misc{eu-kiki-2026,
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- title = {eu-kiki: 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/L-electron-Rare/ailiance},
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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
8
  - ailiance
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+ - ailiance
10
  - eu-ai-act
11
  - art-52
12
  - art-53
 
20
 
21
  # devstral-python-lora
22
 
23
+ 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.
24
 
25
  > **EU AI Act compliance.** This card follows the **European Commission's
26
  > *Template for the Public Summary of Training Content* for general-purpose
 
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39
  | Field | Value |
40
  |---|---|
41
+ | **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). |
43
 
44
  ## 1.2. Model identification
 
59
  | **Approximate size in alternative units** | ≈ 0.6 M tokens (2 850 rows × ≈ 200 tokens/row, single-pass). |
60
  | **Latest date of data acquisition / collection for model training** | 11/2024 (StarCoder2 Self-Instruct release). The model is **not** continuously trained on new data after this date. |
61
  | **Linguistic characteristics of the overall training data** | English (primary, instruction language); French (system-prompt context). No other natural languages in training rows. |
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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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64
  ---
65
 
 
129
 
130
  - **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.
131
  - **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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135
  ## 3.2. Removal of illegal content
136
 
 
151
 
152
  # Appendix A — Performance evaluation (Art. 53(1)(a))
153
 
154
+ **HumanEval+** (EvalPlus official Linux scorer, 164 problems, greedy, 1 sample): base 87.20 / 82.90 → +python 86.00 / 81.10. **Δ HE+ = −1.80 pts** vs base. Scoring on `kx6tm-23` (Proxmox PVE 6.17). Full reproducer in [`eval/results/2026-05-04/devstral-python-fused-humanevalplus/rerun.sh`](https://github.com/ailiance/ailiance/blob/main/eval/results/2026-05-04/devstral-python-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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160
  ---
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  # Appendix D — Citation
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200
  ```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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  ```