---
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
  - gpai-fine-tune
  - pst-aligned
language:
  - en
  - fr
library_name: peft
---

# eu-kiki-devstral-python-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 compliance posture.** This model card is structured to follow the
> European Commission's *Public Summary Template* (PST) for the training content
> of general-purpose AI models, published by the AI Office under
> **Article 53(1)(d)** of Regulation (EU) 2024/1689. The structure below
> (Sections 1–4) maps directly to the PST. Where the official template wording
> differs from what is reproduced here, the **official template wins**;
> please consult the
> [AI Office page](https://digital-strategy.ec.europa.eu/en/policies/ai-office)
> for the canonical version. This card is **PST-aligned, not PST-verbatim**.

---

## Section 1 — General information about the model

| Field | Value |
|---|---|
| **Model name** | `eu-kiki-devstral-python-lora` |
| **Type** | LoRA adapter (parameter-efficient fine-tune) |
| **Base model** | [`mistralai/Devstral-Small-2-24B-Instruct-2512`](https://huggingface.co/mistralai/Devstral-Small-2-24B-Instruct-2512) |
| **Provider of the fine-tune** | L'Électron Rare (Saillant Clément), `clemsail` |
| **Provider contact** | https://github.com/L-electron-Rare/eu-kiki/issues |
| **Date of first public release** | 2026-05-06 |
| **Latest version date** | 2026-05-06 |
| **Modalities** | Text in / text out (no image, audio, or video) |
| **Languages of intended use** | English, French |
| **Risk classification (EU AI Act)** | Limited risk (Art. 52) |
| **Systemic-risk class (Art. 51 / 55)** | **Not applicable** — this is a LoRA fine-tune, not a foundation model > 10²⁵ FLOPs |
| **Foundation-model provider responsibility** | The base model provider remains the GPAI provider for the base; this card describes only the fine-tune delta |

---

## Section 2 — Description of training content

The following four categories follow the PST four-way classification of
training-content sources. **Empty categories are listed explicitly** so
absence is auditable.

### 2.1 Publicly available datasets

| Source | URL / Hub ID | SPDX licence | Records | Notes |
|---|---|---|---:|---|
| StarCoder2 Self-Instruct (Python subset) | https://huggingface.co/datasets/bigcode/starcoder2-self-align | `Apache-2.0` | 2,850 | Public HF dataset, Python instruction-tuning pairs |

### 2.2 Data obtained from third parties under licence

_No third-party-licensed data used._

### 2.3 Data collected through web scraping

_No web-scraped data used._

### 2.4 User-provided data and synthetic data

_No user-provided or synthetic data used._

---

## Section 3 — Aggregate description of training content

| Aggregate field | Value |
|---|---|
| **Total records used for this LoRA** | 2,850 |
| **Domain label in the eu-kiki router** | `python` |
| **Time-period of source data** | Mixed; per-source download dates logged in `_provenance` fields |
| **Modalities in training data** | Text only |
| **Languages in training data** | English, French |
| **Estimated total tokens** | ≈ 570,000 (heuristic 200 tokens / record average) |

The full system-level inventory (all 35+ domains across 7 base models /
candidates, ≈ 82 K records, with per-source SPDX license, download dates,
and `n_used` counts) is published at
[`docs/eu-ai-act-transparency.md`](https://github.com/L-electron-Rare/eu-kiki/blob/main/docs/eu-ai-act-transparency.md)
§4.4. This adapter consumes a strict subset of that inventory.

---

## Section 4 — Other relevant elements

### 4.1 Copyright compliance and TDM opt-out (Art. 53(1)(c))

- **Public datasets (§2.1):** all carry permissive open-source licenses
  (Apache-2.0, MIT, CC-BY-*, BSD); SPDX matrix verified.
- **Third-party-licensed data (§2.2):** vendor datasheets used under EU
  Directive 2019/790 (DSM Directive) **Article 4 — Text and Data Mining
  exception**. Robots.txt respected at collection time. SHA-256 manifests
  published at
  [`docs/pdf-compliance-report.md`](https://github.com/L-electron-Rare/eu-kiki/blob/main/docs/pdf-compliance-report.md).
- **Scraped data (§2.3):** opt-out signals (robots.txt `Disallow`,
  `<meta name="robots" content="noai">`, TDM Reservation headers,
  ai.txt) are honoured at collection time. Manifests under
  `data/scraped/<source>/manifest.json` in the source repo.
- **Removal requests:** open an issue at the source repo URL above or
  contact the operator listed in §1. We commit to remove disputed
  content within 30 days and re-train the adapter on the next release
  cycle.

### 4.2 Quality and curation

- Per-record `_provenance` fields (source URL, SPDX license,
  `record_idx`, `access_date`) attached to 49,956 records across
  21 domains (system-level), enabling per-record audit and removal.
- Per-domain cap of ≤ 3 000 records applied to keep classes balanced
  across the routing surface.
- Synthetic data (when present) is explicitly marked `source: "synthetic"`
  in the row provenance.

### 4.3 Personal data and PII (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 in the released adapters. Low-signal Presidio 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.

### 4.4 Special categories of personal data (GDPR Art. 9)

No special-category data (health, religion, sexual orientation, etc.)
was intentionally collected. The PII scan above also screens for
identifiers that could lead to special-category inference; none were
flagged.

### 4.5 Copyright opt-out registry

The provider tracks opt-outs via the Issues tracker on the source
repository. As of release date no removal requests have been received.

---

## Section 5 — Performance evaluation (Art. 53(1)(a))

**HumanEval+** (Linux EvalPlus, 164 problems, greedy, 1 sample): base 87.20 / 82.90 → fused +python 86.00 / 81.10. **Δ HE+ = −1.80 pts** vs base. Scoring on `kx6tm-23` (Proxmox PVE 6.17, EvalPlus official sandbox).

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).

---

## Section 6 — Training configuration

| Parameter | Value |
|---|---|
| Method | LoRA |
| Rank | 16 |
| Alpha | 32 |
| Dropout | 0.05 |
| Target modules | `q_proj`, `k_proj`, `v_proj`, `o_proj` (attention only) |
| 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 |

### 6.1 Compute resources (Art. 53(1)(d))

LoRA training is parameter-efficient: only ≈ 0.1–0.5 % of base-model
parameters are updated. **Estimated training compute ≪ 10²⁵ FLOPs** —
the systemic-risk threshold of Art. 51. Single-machine training on
Mac Studio M3 Ultra; no datacentre footprint. No proprietary teacher
model is used in deployed inference.

---

## Section 7 — 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-python-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-python-lora-fused \
    --dequantize
```

---

## Section 8 — 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.

---

## Section 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}
}
```

## Section 10 — Changelog

| Date | Card version | Change |
|---|---|---|
| 2026-05-06 | v0.4.1 | First HF release — Apache-2.0, EU AI Act self-contained model card |
| 2026-05-06 | v0.4.2 | Restructured to align with Commission Public Summary Template (PST) §1–4; explicit empty-category disclosure; opt-out registry section added |