from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-8B", device_map="auto")
model = PeftModel.from_pretrained(model, "clemsail/ailiance-spice-sft")
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-8B")

License

Apache 2.0

🇪🇺 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	Ailiance (clemsail / electron-rare)
Role under AI Act	GPAI provider for this adapter
Base model	`Qwen/Qwen3-8B` — see upstream provenance
Adapter type	LoRA / PEFT — adapter weights only; base unchanged
Training data origin	Ailiance 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.