docs: EU AI Act self-contained model card v0.4.1

README.md

@@ -7,6 +7,8 @@ tags:
   - mlx
   - eu-kiki
   - eu-ai-act
+  - art-52
+  - art-53
 language:
   - fr
   - en
@@ -15,13 +17,88 @@ library_name: peft
 
 # eu-kiki-devstral-rust-lora
 
-LoRA adapter for **mistralai/Devstral-Small-2-24B-Instruct-2512**, part of the [eu-kiki](https://github.com/L-electron-Rare/eu-kiki) project — a 100 % EU-sovereign multi-model LLM serving pipeline. EU AI Act Article 52/53 compliant.
+LoRA adapter for **mistralai/Devstral-Small-2-24B-Instruct-2512**, part of the [eu-kiki](https://github.com/L-electron-Rare/eu-kiki) project — a 100 % EU-sovereign multi-model LLM serving pipeline. **EU AI Act Article 52 / 53 compliant** (limited risk, GPAI fine-tune).
 
-## Performance
+## 1. Model identity
 
-**HumanEval (custom Studio scorer):** base 87.20 → +rust 86.59 = −0.61 pts (best of the 3 Devstral adapters).
+| Field | Value |
+|---|---|
+| **Adapter name** | `eu-kiki-devstral-rust-lora` |
+| **Base model** | [`mistralai/Devstral-Small-2-24B-Instruct-2512`](https://huggingface.co/mistralai/Devstral-Small-2-24B-Instruct-2512) |
+| **Adapter method** | LoRA (rank 16, alpha 32, dropout 0.05) |
+| **Target modules** | `q_proj`, `k_proj`, `v_proj`, `o_proj` (attention only) |
+| **Precision** | BF16 |
+| **Domain** | `rust` |
+| **Training records** | 2,850 (curated, deduplicated) |
+| **License** | Apache-2.0 (matches base model) |
+| **Risk class** | **Limited risk** (Art. 52). Not safety-critical. |
+| **System operator** | L'Électron Rare (clemsail), Saillant Clément |
+| **Live demo** | https://ml.saillant.cc |
+| **Source repo** | https://github.com/L-electron-Rare/eu-kiki |
+
+## 2. Performance evaluation (Art. 53(1)(d))
+
+**HumanEval** (custom Studio scorer): base 87.20 → +rust 86.59 = **−0.61 pts**. Best of the 3 Devstral adapters in this release.
+
+Full bench results, methodology, env.json, and rerun.sh per measurement:
+[`eval/results/SUMMARY.md`](https://github.com/L-electron-Rare/eu-kiki/blob/main/eval/results/SUMMARY.md) · [`MODEL_CARD.md`](https://github.com/L-electron-Rare/eu-kiki/blob/main/MODEL_CARD.md).
+
+## 3. Training data (Art. 53(1)(b)+(d))
+
+The following sources were used to fine-tune **this specific adapter**.
+Per-record `_provenance` fields (source, SPDX license, record_idx,
+access_date) are present in the source dataset; see system-level
+transparency record for full audit trail.
+
+| Source | HF / URL | SPDX License | Records used |
+|---|---|---|---:|
+| StarCoder2 Self-Instruct | `bigcode/starcoder2-self-align` | `Apache-2.0` | 2,850 |
+
+**Total records used for this LoRA:** 2,850.
+
+System-level inventory (all 35+ domains, full SPDX, scraping manifests,
+PDF pipeline DSM Art. 4 TDM compliance):
+[`docs/eu-ai-act-transparency.md`](https://github.com/L-electron-Rare/eu-kiki/blob/main/docs/eu-ai-act-transparency.md).
+
+### 3.1 Copyright policy (Art. 53(1)(c))
+
+- All HF-traced datasets carry permissive licenses (Apache-2.0, MIT,
+  CC-BY-*, BSD); copyleft compatibility verified via SPDX matrix.
+- PDF datasheets (when used) processed under EU DSM Directive
+  Article 4 TDM exception: robots.txt respected, SHA-256 manifests,
+  dedicated audit at
+  [`docs/pdf-compliance-report.md`](https://github.com/L-electron-Rare/eu-kiki/blob/main/docs/pdf-compliance-report.md).
+- Opt-out / removal requests: open an issue on the source repo or
+  email the system operator (see §5).
+
+### 3.2 PII statement (Art. 10 + Art. 53(1)(d))
+
+Training data scanned with **Microsoft Presidio + en_core_web_lg**
+(2026-04-28) across all 35+ domain directories. **One** email address
+detected in the unrelated `traduction-tech` corpus was redacted before
+training. No high-signal PII (email, phone, credit card, SSN, IBAN)
+remains. Low-signal detections (PERSON, LOCATION, DATE_TIME) are
+common false positives in technical text and were left in place.
+Full report: `data/pii-scan-report.json` in the source repo.
+
+## 4. Training configuration
+
+| Parameter | Value |
+|---|---|
+| Method | LoRA |
+| Rank | 16 |
+| Alpha | 32 |
+| Dropout | 0.05 |
+| Target modules | `q_proj`, `k_proj`, `v_proj`, `o_proj` |
+| Precision | BF16 |
+| Optimiser | AdamW |
+| Learning rate | 1e-5 |
+| Batch size × grad-accum | 1 × 4–8 |
+| Framework | MLX (`mlx_lm` fork on Apple Silicon) |
+| Hardware | Mac Studio M3 Ultra 512 GB unified memory |
+| Energy footprint | ≪ training a foundation model from scratch (LoRA is parameter-efficient by design) |
 
-## Usage
+## 5. Usage
 
 ```python
 from mlx_lm import load
@@ -36,7 +113,7 @@ linear_to_lora_layers(model, num_layers=32, config={"rank": 16, "alpha": 32})
 model.load_weights(f"{adapter_path}/adapters.safetensors", strict=False)
 ```
 
-Or, simpler, fuse and serve via `mlx_lm fuse`:
+Or fuse and serve as a self-contained checkpoint:
 
 ```bash
 python -m mlx_lm fuse \
@@ -46,38 +123,41 @@ python -m mlx_lm fuse \
     --dequantize
 ```
 
-## Training configuration
-
-| Parameter | Value |
-|---|---|
-| Method | LoRA |
-| Rank | 16 |
-| Alpha | 32 |
-| Dropout | 0.05 |
-| Target modules | q_proj, k_proj, v_proj, o_proj |
-| Precision | BF16 |
-| Optimiser | AdamW |
-| Learning rate | 1e-5 |
-| Framework | MLX (`mlx_lm` fork on Apple Silicon) |
-| Hardware | Mac Studio M3 Ultra 512 GB unified memory |
-
-## Provenance & EU AI Act compliance
+## 6. Limitations & out-of-scope use
 
-Datasets used to train this adapter are HF-traceable. Per-source SPDX licenses, download dates, source row counts, and used row counts are documented in:
+- **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 is a fine-tune of the base model**: it inherits all base-model
+  limitations and biases (training data cutoff, language coverage,
+  refusal patterns).
 
-- [`docs/eu-ai-act-transparency.md`](https://github.com/L-electron-Rare/eu-kiki/blob/main/docs/eu-ai-act-transparency.md) — system-level transparency record (Art. 52/53)
-- [`MODEL_CARD.md`](https://github.com/L-electron-Rare/eu-kiki/blob/main/MODEL_CARD.md) — full evaluation summary across HumanEval+, MT-Bench, GSM8K, KIKI-DSL v3
-- [`eval/results/SUMMARY.md`](https://github.com/L-electron-Rare/eu-kiki/blob/main/eval/results/SUMMARY.md) — per-bench reproducible results
+## 7. Contact (Art. 53(1)(d))
 
-## Risk classification
-
-**Limited risk** (EU AI Act Art. 52). General-purpose AI; not deployed in safety-critical contexts.
+| Subject | Contact |
+|---|---|
+| Operator | clemsail (`L-electron-Rare` on GitHub) |
+| Issues / audit requests | https://github.com/L-electron-Rare/eu-kiki/issues |
+| Base model PII / copyright | See base model card on Hugging Face |
+| Apertus PII / copyright | `llm-privacy-requests@swiss-ai.org`, `llm-copyright-requests@swiss-ai.org` |
 
-## License
+## 8. EU AI Act compliance summary
 
-Apache 2.0, matching the base model.
+| Article | Coverage |
+|---|---|
+| Art. 52 (transparency to users) | Adapter publishes its purpose, base, fine-tune nature, and limitations in this card |
+| Art. 53(1)(a) (technical doc) | This card + system-level [`MODEL_CARD.md`](https://github.com/L-electron-Rare/eu-kiki/blob/main/MODEL_CARD.md) |
+| Art. 53(1)(b) (training data summary) | §3 above + system-level [`transparency.md`](https://github.com/L-electron-Rare/eu-kiki/blob/main/docs/eu-ai-act-transparency.md) §4 |
+| Art. 53(1)(c) (copyright policy) | §3.1 above + DSM Art. 4 TDM compliance for PDF-derived corpora |
+| Art. 53(1)(d) (evaluation summary) | §2 above + per-bench reproducible results in [`eval/results/SUMMARY.md`](https://github.com/L-electron-Rare/eu-kiki/blob/main/eval/results/SUMMARY.md) |
+| Art. 53(2) (open-source exemption) | All weights Apache-2.0, datasets traceable, no proprietary teacher used in deployed inference |
+| Art. 55 (systemic risk) | **Not applicable** — no foundation model > 10²⁵ FLOPs trained here; this is a LoRA fine-tune |
 
-## Citation
+## 9. Citation
 
 ```bibtex
 @misc{eu-kiki-2026,
@@ -88,3 +168,9 @@ Apache 2.0, matching the base model.
   note   = {Live demo: https://ml.saillant.cc}
 }
 ```
+
+## 10. Changelog
+
+| Date | Change |
+|---|---|
+| 2026-05-06 | First HF release — Apache-2.0, EU AI Act self-contained model card v0.4.1 |