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license: apache-2.0
base_model: mistralai/Devstral-Small-2-24B-Instruct-2512
library_name: peft
tags:
- mlx
- lora
- peft
- ailiance
- devstral
- docker-devops
language:
- en
- fr
pipeline_tag: text-generation
---
# Ailiance — Devstral-Small-2-24B-Instruct docker-devops LoRA
LoRA adapter fine-tuned on `mistralai/Devstral-Small-2-24B-Instruct-2512` for **docker-devops** tasks.
> Maintained by **Ailiance** — French AI org publishing EU AI Act aligned LoRA adapters and datasets.
## Quick start (MLX)
```python
from mlx_lm import load, generate
model, tokenizer = load(
"mistralai/Devstral-Small-2-24B-Instruct-2512",
adapter_path="Ailiance-fr/devstral-docker-devops-lora",
)
print(generate(model, tokenizer, prompt="..."))
```
## Training
| Hyperparameter | Value |
|------------------|------------------------|
| Base model | `mistralai/Devstral-Small-2-24B-Instruct-2512` |
| Method | LoRA via `mlx-lm` |
| Rank | 16 |
| Scale | 2.0 |
| Alpha | 32 |
| Max seq length | 2048 |
| Iterations | 500 |
| Optimizer | Adam, LR 1e-5 |
| Hardware | Apple M3 Ultra 512 GB |
## Training data lineage
Derived from the internal **eu-kiki / mascarade** curation. All upstream samples
are synthetic, permissively-licensed, or generated from Apache-2.0 base resources.
See the [Ailiance-fr catalog](https://huggingface.co/Ailiance-fr) for related cards.
## Training metrics
Extracted from training log (`batch_eu_kiki_v2.log`):
| Metric | Value |
|---|---:|
| Final train loss | 0.732 |
| Final validation loss | 0.687 |
| Val loss reduction | +1.133 (from 1.820) |
| Iterations completed | 490 |
| Trainable parameters | 0.224% (279.708M / 125025.989M) |
> Validation loss is measured every 200 iterations on a held-out split of the
> training corpus (`val_batches=5`, `mlx-lm` LoRA trainer).
## Benchmark on production tasks
This LoRA has **not yet been evaluated** through the
[`electron-bench`](https://github.com/ailiance/ailiance-bench/blob/main) functional benchmark
pipeline. The current pipeline targets the `gemma-4-E4B` base only; support for
the **devstral** base is on the roadmap
([open issues](https://github.com/ailiance/ailiance-bench/issues)).
For a comparable reference matrix on a related domain (electronics, embedded,
KiCad), see the Gemma champions:
| Adapter | Highlights |
|---|---|
| [`Ailiance-fr/gemma-4-E4B-eukiki-lora`](https://huggingface.co/Ailiance-fr/gemma-4-E4B-eukiki-lora) | +55 P1-DSL, +42 P1-PCB, +25 SPICE, +38 P3 |
| [`Ailiance-fr/gemma-4-E4B-mascarade-lora`](https://huggingface.co/Ailiance-fr/gemma-4-E4B-mascarade-lora) | +48 P3 extraction |
Full base-vs-LoRA matrix: [`compare_base_vs_lora.md`](https://github.com/ailiance/ailiance-bench/blob/main/bench-results/compare_base_vs_lora.md).
## License chain
| Component | License |
|-----------------------------------|-------------------|
| Base model (`mistralai/Devstral-Small-2-24B-Instruct-2512`) | apache-2.0 |
| Training data (internal Ailiance curation (synthetic + permissive sources)) | apache-2.0 |
| **LoRA adapter (this repo)** | **apache-2.0**|
_All upstream components are Apache 2.0 / MIT — LoRA inherits permissive terms._
## EU AI Act compliance
- **Article 53(1)(c)**: training data licenses preserved (per-dataset cards declare upstream licenses).
- **Article 53(1)(d)**: training data summary — see upstream dataset cards on Ailiance-fr.
- **GPAI Code of Practice (July 2025)**: base `mistralai/Devstral-Small-2-24B-Instruct-2512` released under apache-2.0.
- **No web scraping by Ailiance**, **no licensed data**, **no PII**.
- Upstream Stack Exchange content (where applicable) is CC-BY-SA-4.0 and propagates to this adapter.
## License
LoRA weights: **apache-2.0** — see License chain table above for derivation rationale.
## Citation
```bibtex
@misc{ailiance_devstral_docker_devops_2026,
author = {Ailiance},
title = {Ailiance — Devstral-Small-2-24B-Instruct docker-devops LoRA},
year = {2026},
publisher = {Hugging Face},
url = {https://huggingface.co/Ailiance-fr/devstral-docker-devops-lora}
}
```
## Related
See the full [Ailiance-fr LoRA collection](https://huggingface.co/Ailiance-fr).
## Bench comparison (2026-05-11)
### Base model (Devstral-Small-2-24B-MLX-4bit) capability
| Task | Score | Notes |
|---|---:|---|
| GSM8K-CoT flex EM | **0.96** | W3 lm-eval-harness (--limit 100) |
| ARC-Easy acc / acc_norm | **0.80 / 0.75** | |
| MMLU-Pro Computer Science | **0.64** | |
Source: <https://github.com/ailiance/ailiance/tree/main/output/lm-eval-base-2026-05-11>
### This LoRA (tuned) — bench PENDING
Will include kicad-sch / iact-bench validators + W3 lm-eval delta. See spec for
methodology:
<https://github.com/ailiance/ailiance-bench/blob/main/docs/superpowers/specs/2026-05-11-kicad-sch-gap-design.md>
|