README.md

---
license: apache-2.0
base_model: mistralai/Devstral-Small-2-24B-Instruct-2512
tags:
  - lora
  - peft
  - mlx
  - eu-kiki
  - eu-ai-act
  - art-52
  - art-53
language:
  - fr
  - en
library_name: peft
---

# eu-kiki-devstral-cpp-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** (limited risk, GPAI fine-tune).

## 1. Model identity

| Field | Value |
|---|---|
| **Adapter name** | `eu-kiki-devstral-cpp-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** | `cpp` |
| **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, EvalPlus extra-tests not run — Linux-only sandbox): base 87.20 → +cpp 85.98 = **−1.22 pts**. For rigorous HumanEval+ Δ, sample re-scoring on Linux is required.

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 |
|---|---|---|---:|
| CommitPackFT | `bigcode/commitpackft` | `MIT` | 1,500 |
| ESP-IDF examples | `espressif/esp-idf` | `Apache-2.0` | 700 |
| STM32Cube examples | `STMicroelectronics/STM32CubeF4` | `BSD-3-Clause` | 450 |
| Arduino examples | `arduino/Arduino` | `CC0-1.0` | 200 |

**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) |

## 5. 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("mistralai/Devstral-Small-2-24B-Instruct-2512")
adapter_path = snapshot_download("clemsail/eu-kiki-devstral-cpp-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 mistralai/Devstral-Small-2-24B-Instruct-2512 \
    --adapter-path <adapter_path> \
    --save-path /tmp/eu-kiki-devstral-cpp-lora-fused \
    --dequantize
```

## 6. Limitations & 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 is a fine-tune of the base model**: it inherits all base-model
  limitations and biases (training data cutoff, language coverage,
  refusal patterns).

## 7. Contact (Art. 53(1)(d))

| 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` |

## 8. EU AI Act compliance summary

| 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 |

## 9. Citation

```bibtex
@misc{eu-kiki-2026,
  title  = {eu-kiki: EU-sovereign multi-model LLM serving with HF-traceable LoRA adapters},
  author = {Saillant, Clément},
  year   = {2026},
  url    = {https://github.com/L-electron-Rare/eu-kiki},
  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 |