Text Generation
PEFT
Safetensors
English
French
electronics
embedded-systems
iot
lora
sft
kiki-tuning
conversational
Instructions to use electron-rare/kiki-models-tuning-sft-iot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use electron-rare/kiki-models-tuning-sft-iot with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-8B") model = PeftModel.from_pretrained(base_model, "electron-rare/kiki-models-tuning-sft-iot") - Notebooks
- Google Colab
- Kaggle
KIKI IOT SFT — LoRA Adapter
Fine-tuned LoRA adapter for iot domain expertise, based on Qwen/Qwen3-8B.
Part of the KIKI Models Tuning pipeline for the FineFab platform.
Training Details
| Parameter | Value |
|---|---|
| Base Model | Qwen/Qwen3-8B |
| Method | QLoRA (4-bit NF4) |
| LoRA Rank | 16 |
| Epochs | 3 |
| Dataset | 6005 examples |
| Domain | iot |
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/kiki-iot-sft")
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-8B")
License
Apache 2.0
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