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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
- cpp
language:
- en
- fr
pipeline_tag: text-generation
---
# Ailiance — Devstral-Small-2-24B-BF16 cpp (bf16) LoRA
LoRA adapter fine-tuned on `mistralai/Devstral-Small-2-24B-Instruct-2512` for **cpp** tasks.
> **Variant**: trained on the BF16 base for higher numerical fidelity.
> 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-cpp-bf16-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 | 4096 |
| 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.
## 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 (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_cpp_bf16_2026,
author = {Ailiance},
title = {Ailiance — Devstral-Small-2-24B-BF16 cpp (bf16) LoRA},
year = {2026},
publisher = {Hugging Face},
url = {https://huggingface.co/Ailiance-fr/devstral-cpp-bf16-lora}
}
```
## Related
See the full [Ailiance-fr LoRA collection](https://huggingface.co/Ailiance-fr).
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