Text Generation
PEFT
MLX
English
French
lora
electronics
embedded
kicad
spice
ailiance
gemma
kicad9plus
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 Settings
- 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"
- Atomic Chat
docs: add Benchmark / Training metrics section
Browse files
README.md
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print(generate(model, tokenizer, prompt="..."))
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## License chain
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| Component | License |
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print(generate(model, tokenizer, prompt="..."))
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```
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## Benchmark on production tasks
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**Gemma KiCad 9+ specialist** — evaluated through the
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[`electron-bench`](https://github.com/ailiance/ailiance-bench/blob/main) functional pipeline
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(Phases P1 → P6, base vs LoRA).
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| Task | Result |
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| KiCad 9+ DSL | trained 500 iters |
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Full base-vs-LoRA matrix (all phases, all adapters):
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[`compare_base_vs_lora.md`](https://github.com/ailiance/ailiance-bench/blob/main/bench-results/compare_base_vs_lora.md).
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## License chain
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| Component | License |
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