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clemsail
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Model Details
Model Provider
clemsail
Model Tree
Qwen/Qwen3-8B
Input Modalities
Output Modalities
Supported Functionality
| Parameter | Value |
|---|---|
| Base Model | Qwen/Qwen3-8B |
| Method | QLoRA (4-bit NF4) |
| LoRA Rank | 16 |
| Epochs | 3 |
| Dataset | 2267 examples |
| Domain | power |
python
from peft import PeftModelfrom transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-8B", device_map="auto")model = PeftModel.from_pretrained(model, "clemsail/ailiance-power-sft")tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-8B")Apache 2.0
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 | Power electronics |
| 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.
from peft import PeftModelfrom transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-8B", device_map="auto")model = PeftModel.from_pretrained(model, "clemsail/ailiance-power-sft")tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-8B")