Instructions to use delimitter/qwen3-1.7b-synoema-iot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use delimitter/qwen3-1.7b-synoema-iot with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/qwen3-1.7b-unsloth-bnb-4bit") model = PeftModel.from_pretrained(base_model, "delimitter/qwen3-1.7b-synoema-iot") - Notebooks
- Google Colab
- Kaggle
Qwen3-1.7B Synoema IoT β 7/7 Fine-tune
LoRA adapter on Qwen/Qwen3-1.7B trained to generate Synoema IoT automation rules.
Eval Results β IoT 7-Task Suite
Score: 7/7 (100%) β stable across cycles C13-C16.
| Task | Result |
|---|---|
| T1-bearing-protection | pass |
| T2-irrigation-interlock | pass |
| T3-async-sensor-poll | pass |
| T4-bearing-anomaly | pass |
| T5-hvac-setback | pass |
| T6-vitals-alert | pass |
| T7-co2-anomaly | pass |
Model Details
| Property | Value |
|---|---|
| Base model | unsloth/qwen3-1.7b-unsloth-bnb-4bit |
| Adapter | LoRA QLoRA 4-bit |
| LoRA rank | r=16, alpha=32 |
| Epochs | 3 |
| Train examples | 5,455 |
| Training cycle | C16 (stable 7/7 from C13) |
| Hardware | AMD RX 7900 GRE (ROCm, unsloth) |
Usage
Links
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