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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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Training Details
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Evaluation
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Summary
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Environmental Impact
Carbon emissions can be estimated using the
Machine Learning Impact calculator presented in
Lacoste et al. (2019).
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Technical Specifications [optional]
Model Architecture and Objective
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Framework versions
🇪🇺 EU AI Act transparency
This adapter is provided as a fine-tuned LoRA under the AI Act framework
(Regulation EU 2024/1689). Compliance metadata:
| Field | Value |
|---|
| Provider | L'Électron Rare (clemsail / electron-rare) |
| Role under AI Act | GPAI provider for this adapter |
| Base model | unsloth/Mistral-Small-3.1-24B-Instruct-2503-unsloth-bnb-4bit — see upstream provenance |
| Adapter type | LoRA / PEFT — adapter weights only; base unchanged |
| Training data origin | L'Électron Rare proprietary technical corpus + curated public docs |
| License | Apache-2.0 (adapter). Upstream base licence applies separately. |
| Intended use | KiCad PCB design assistance |
| Out of scope | Healthcare diagnosis, legal advice, autonomous safety-critical decisions, generation of malicious code |
| Risk classification | Limited risk — Article 50 transparency obligations apply |
| Copyright respect | Training data does not include scraped copyrighted material. Opt-out signals (robots.txt, ai.txt) are honoured for web-sourced data. |
| Full provenance | https://github.com/ailiance/ailiance/tree/main/docs/provenance |
| Contact | postmaster@saillant.cc — biased output reports, copyright concerns, etc. |
⚠️ You are using an AI model. Outputs may be inaccurate, biased or
fabricated. Do not act on them without independent verification, especially
in regulated domains.
