Instructions to use Ailiance-fr/gemma-4-E4B-kicad9plus-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Ailiance-fr/gemma-4-E4B-kicad9plus-lora with PEFT:
Task type is invalid.
- MLX
How to use Ailiance-fr/gemma-4-E4B-kicad9plus-lora with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # if on a CUDA device, also pip install mlx[cuda] # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("Ailiance-fr/gemma-4-E4B-kicad9plus-lora") prompt = "Once upon a time in" text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- LM Studio
- MLX LM
How to use Ailiance-fr/gemma-4-E4B-kicad9plus-lora with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Generate some text mlx_lm.generate --model "Ailiance-fr/gemma-4-E4B-kicad9plus-lora" --prompt "Once upon a time"
Ailiance โ Gemma 4 E4B kicad9plus LoRA
LoRA adapter fine-tuned on lmstudio-community/gemma-4-E4B-it-MLX-4bit for the kicad9plus domain (electronics, embedded, KiCad, SPICE).
Maintained by Ailiance โ French AI org publishing EU AI Act aligned LoRA adapters and datasets.
Quick start (MLX)
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 โ evaluated through the
electron-bench functional pipeline
(Phases P1 โ 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 โ 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
@misc{ailiance_gemma_4_E4B_kicad9plus_lora_2026,
author = {Ailiance},
title = {Ailiance โ Gemma 4 E4B kicad9plus LoRA},
year = {2026},
publisher = {Hugging Face},
url = {https://huggingface.co/Ailiance-fr/gemma-4-E4B-kicad9plus-lora}
}
Related
See the full Ailiance-fr LoRA collection.
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Quantized
Model tree for Ailiance-fr/gemma-4-E4B-kicad9plus-lora
Base model
google/gemma-4-E4B