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Ailiance-fr/apertus-security-fenrir-curriculum-lora

sourceHugging Faceapache-2.0updated 5mo agoView on Hugging Face
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Model Card

Ailiance — Apertus-70B-Instruct security-fenrir (curriculum) LoRA

LoRA adapter fine-tuned on swiss-ai/Apertus-70B-Instruct-2509 for security-fenrir tasks.

Variant: trained with multi-phase length curriculum.
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(
    "swiss-ai/Apertus-70B-Instruct-2509",
    adapter_path="Ailiance-fr/apertus-security-fenrir-curriculum-lora",
)

print(generate(model, tokenizer, prompt="..."))

Training

HyperparameterValue
Base modelswiss-ai/Apertus-70B-Instruct-2509
MethodLoRA via mlx-lm
Rank16
Scale2.0
Alpha32
Max seq length4096
Iterations500
OptimizerAdam, LR 1e-5
HardwareApple 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 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 apertus-specific tasks
  • —Comparison vs base swiss-ai/Apertus-70B-Instruct-2509

Track progress: ailiance-bench issues.

For reference benchmarks on the gemma-4-E4B base, see the base-vs-LoRA matrix.

License chain

ComponentLicense
Base model (swiss-ai/Apertus-70B-Instruct-2509)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 swiss-ai/Apertus-70B-Instruct-2509 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_apertus_security_fenrir_curriculum_2026,
  author    = {Ailiance},
  title     = {Ailiance — Apertus-70B-Instruct security-fenrir (curriculum) LoRA},
  year      = {2026},
  publisher = {Hugging Face},
  url       = {https://huggingface.co/Ailiance-fr/apertus-security-fenrir-curriculum-lora}
}

Related

See the full Ailiance-fr LoRA collection.

Bench comparison (2026-05-11)

Base model (Apertus-70B-Instruct-2509) capability

TaskScoreNotes
ARC-Easy acc / acc_norm0.81 / 0.77W3 lm-eval-harness BF16
GSM8K-CoTTIMEOUT (1800s budget)base 70B BF16 too slow for CoT
MMLU-Pro Computer ScienceTIMEOUT

This LoRA (tuned) — bench PENDING

Production usage: served via gateway alias ailiance-apertus-<domain> on <https://www.ailiance.fr> through the Apertus multi-LoRA hot-swap server (Studio :9322, 1 base + 10 LoRA dynamic swap, ~40GB VRAM).