metadata
library_name: peft
base_model: Qwen/Qwen3-8B
license: apache-2.0
tags:
- electronics
- embedded-systems
- embedded
- lora
- sft
- ailiance-tuning
language:
- en
- fr
datasets:
- custom
pipeline_tag: text-generation
Ailiance EMBEDDED SFT — LoRA Adapter
Fine-tuned LoRA adapter for embedded domain expertise, based on Qwen/Qwen3-8B.
Part of the Ailiance Models Tuning pipeline for the Ailiance platform.
Training Details
| Parameter | Value |
|---|---|
| Base Model | Qwen/Qwen3-8B |
| Method | QLoRA (4-bit NF4) |
| LoRA Rank | 16 |
| Epochs | 3 |
| Dataset | 8344 examples |
| Domain | embedded |
Usage
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-embedded-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 | Embedded systems programming |
| 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.