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Model Description
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Uses
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Bias, Risks, and Limitations
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Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
How to Get Started with the Model
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Training Details
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Training Procedure
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Training Hyperparameters
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Evaluation
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Summary
Model Examination [optional]
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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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Compute Infrastructure
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Hardware
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Software
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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 | Qwen/Qwen2.5-7B-Instruct — 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 | SPICE circuit simulation |
| 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.
