from mlx_lm import load, generate

model, tokenizer = load(
    "lmstudio-community/gemma-4-E4B-it-MLX-4bit",
    adapter_path="Ailiance-fr/gemma-4-E4B-kicad9plus-lora",
)

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

Benchmark on production tasks

Gemma KiCad 9+ specialist — evaluated through the electron-bench functional pipeline (Phases P1 → P6, base vs LoRA).

Task	Result
KiCad 9+ DSL	trained 500 iters

Full base-vs-LoRA matrix (all phases, all adapters): compare_base_vs_lora.md.

License chain

Component	License
Base model weights (`lmstudio-community/gemma-4-E4B-it-MLX-4bit`)	Gemma Terms of Use
Training data (`Ailiance-fr/kicad9plus-permissive`)	cc-by-sa-4.0
LoRA adapter (this repo)	CC-BY-SA-4.0

Rationale: weights of the base model inherit from the Gemma Terms of Use, but the LoRA adapter is a derivative of CC-BY-SA-4.0 training data and is therefore released under CC-BY-SA-4.0 (share-alike propagates). Downstream users who load this adapter against the Gemma base must comply with both licenses simultaneously.

Training data lineage

Primary corpus: Ailiance-fr/kicad9plus-permissive (cc-by-sa-4.0). See the Ailiance-fr catalog for related cards.

EU AI Act compliance

Article 53(1)(c): training data licenses preserved upstream.
Article 53(1)(d): training data summary — see dataset cards on Ailiance-fr.
GPAI Code of Practice (July 2025): base model Gemma (Google is a signatory).
No web scraping by Ailiance, no licensed data, no PII.

License

LoRA weights: CC-BY-SA-4.0 (training-data share-alike). Base model weights remain under Gemma Terms of Use.

Citation

bibtex

@misc{ailiance_gemma_4_E4B_kicad9plus_lora_2026,
  author    = {Ailiance},
  title     = {Ailiance — Gemma 4 E4B kicad9plus LoRA},
  year      = {2026},
  publisher = {Hugging Face},
  url       = {https://huggingface.co/Ailiance-fr/gemma-4-E4B-kicad9plus-lora}
}

See the full Ailiance-fr LoRA collection.

Ailiance-fr/gemma-4-E4B-kicad9plus-lora

Ailiance — Gemma 4 E4B kicad9plus LoRA

Quick start (MLX)