from mlx_lm import load, generate

model, tokenizer = load(
    "swiss-ai/Apertus-70B-Instruct-2509",
    adapter_path="Ailiance-fr/apertus-math-gsm8k-lora",
)

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

Training

Hyperparameter	Value
Base model	`swiss-ai/Apertus-70B-Instruct-2509`
Method	LoRA via `mlx-lm`
Rank	16
Scale	2.0
Alpha	32
Max seq length	1024
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 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

Component	License
Base model (`swiss-ai/Apertus-70B-Instruct-2509`)	apache-2.0
Training data (internal Ailiance curation (synthetic + permissive sources))	mit
LoRA adapter (this repo)	mit

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: mit — see License chain table above for derivation rationale.

Citation

bibtex

@misc{ailiance_apertus_math_gsm8k_2026,
  author    = {Ailiance},
  title     = {Ailiance — Apertus-70B-Instruct math-gsm8k LoRA},
  year      = {2026},
  publisher = {Hugging Face},
  url       = {https://huggingface.co/Ailiance-fr/apertus-math-gsm8k-lora}
}

See the full Ailiance-fr LoRA collection.

Bench comparison (2026-05-11)

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

Task	Score	Notes
ARC-Easy acc / acc_norm	0.81 / 0.77	W3 lm-eval-harness BF16
GSM8K-CoT	TIMEOUT (1800s budget)	base 70B BF16 too slow for CoT
MMLU-Pro Computer Science	TIMEOUT

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).

Ailiance-fr/apertus-math-gsm8k-lora

Ailiance — Apertus-70B-Instruct math-gsm8k LoRA

Quick start (MLX)