README.md

metadata

license: apache-2.0
base_model: mistralai/Devstral-Small-2-24B-Instruct-2512
tags:
  - lora
  - peft
  - mlx
  - eu-kiki
  - eu-ai-act
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 project — a 100 % EU-sovereign multi-model LLM serving pipeline. EU AI Act Article 52/53 compliant.

Performance

HumanEval (custom Studio scorer, EvalPlus extra-tests not run): base 87.20 → +cpp 85.98 = −1.22 pts.

Usage

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, simpler, fuse and serve via mlx_lm fuse:

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

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

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:

• docs/eu-ai-act-transparency.md — system-level transparency record (Art. 52/53)
• MODEL_CARD.md — full evaluation summary across HumanEval+, MT-Bench, GSM8K, KIKI-DSL v3
• eval/results/SUMMARY.md — per-bench reproducible results

Risk classification

Limited risk (EU AI Act Art. 52). General-purpose AI; not deployed in safety-critical contexts.

License

Apache 2.0, matching the base model.

Citation

@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}
}