README.md

---
license: cc-by-sa-4.0
base_model: mistralai/Devstral-Small-2-24B-Instruct-2512
library_name: peft
tags:
- mlx
- lora
- peft
- ailiance
- devstral
- kicad-pcb
language:
- en
- fr
pipeline_tag: text-generation
---

# Ailiance — Devstral-Small-2-24B-BF16 kicad-pcb (fullseq) LoRA

LoRA adapter fine-tuned on `mistralai/Devstral-Small-2-24B-Instruct-2512` for **kicad-pcb** tasks.

> **Variant**: trained with full-sequence loss for stronger schema adherence.

> Maintained by **Ailiance** — French AI org publishing EU AI Act aligned LoRA adapters and datasets.

> ## ATTRIBUTION AUDIT COMPLETED
>
> The training dataset
> [`Ailiance-fr/mascarade-kicad-dataset`](https://huggingface.co/datasets/Ailiance-fr/mascarade-kicad-dataset)
> went through a full Stack Exchange attribution audit (2026-05-11):
> 61 samples (~2.3%) carry per-sample URL+author+post_id attribution;
> 169 samples flagged `not_found_on_se` (likely synthetic);
> 2 413 samples (~91%) are LLM-synthetic.
> Audit report: `docs/audit_mascarade_se_attribution.md` in `electron-bench`.

## 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-kicad-pcb-fullseq-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   | 16384  |
| Iterations       | 1000           |
| Optimizer        | Adam, LR 1e-5          |
| Hardware         | Apple M3 Ultra 512 GB  |

## Training data lineage

| Role            | Dataset                                                                                          | License        |
|-----------------|--------------------------------------------------------------------------------------------------|----------------|
| Primary corpus  | [`Ailiance-fr/mascarade-kicad-dataset`](https://huggingface.co/datasets/Ailiance-fr/mascarade-kicad-dataset)                          | cc-by-sa-4.0 |

For per-sample provenance and attribution status, consult the dataset card.

## Benchmark roadmap

This LoRA has **not yet been evaluated** through `electron-bench` (the current
pipeline supports `gemma-4-E4B` base only). Training was completed with the
standard `mlx-lm` LoRA trainer (rank 16, alpha 32, scale 2.0, AdamW
LR 1e-5, 500 iters) — full hyperparameters are in the `Training` table above.

Planned evaluations:

- Perplexity on the validation split of the training data
- Functional benchmark on **devstral**-specific tasks
- Comparison vs base `mistralai/Devstral-Small-2-24B-Instruct-2512`

Track progress: [ailiance-bench issues](https://github.com/ailiance/ailiance-bench/issues).

For reference benchmarks on the `gemma-4-E4B` base, see the
[base-vs-LoRA matrix](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 ([`Ailiance-fr/mascarade-kicad-dataset`](https://huggingface.co/datasets/Ailiance-fr/mascarade-kicad-dataset))         | cc-by-sa-4.0      |
| **LoRA adapter (this repo)**      | **cc-by-sa-4.0**|

_Most restrictive license in the chain (CC-BY-SA-4.0 share-alike) propagates to derivatives._

## 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: **cc-by-sa-4.0** — see License chain table above for derivation rationale.

## Citation

```bibtex
@misc{ailiance_devstral_kicad_pcb_fullseq_2026,
  author    = {Ailiance},
  title     = {Ailiance — Devstral-Small-2-24B-BF16 kicad-pcb (fullseq) LoRA},
  year      = {2026},
  publisher = {Hugging Face},
  url       = {https://huggingface.co/Ailiance-fr/devstral-kicad-pcb-fullseq-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>