Qwen2.5-1.5B Building Engineering Precheck LoRA (V5)
Repository: Irfanuruchi/qwen2.5-1.5b-buildeng-precheck-lora-v5
Base model: Qwen2.5-1.5B-Instruct
Fine-tuning method: LoRA (Unsloth)
Overview
This repository provides a LoRA adapter for Qwen2.5-1.5B-Instruct fine-tuned for building engineering reasoning tasks.
The goal of this project is to create a lightweight domain-specific assistant capable of performing engineering pre-check calculations and sanity validation.
The model focuses on early-stage engineering estimation, supporting tasks such as:
• HVAC duct sizing
• Hydronic pipe sizing
• Fan and pump power estimation
• Heat transfer calculations
• Structural pre-check calculations
• Engineering unit sanity checks
• Detection of missing or unrealistic design inputs
This model acts as an engineering assistant for early concept validation, not as a replacement for professional engineering verification.
Training Details
Base Model: Qwen2.5-1.5B-Instruct
Framework: Unsloth
Fine-tuning Method: LoRA
Training Configuration
| Parameter | Value |
|---|---|
| LoRA rank | 32 |
| LoRA alpha | 32 |
| Quantization | 4-bit |
| Sequence length | 1536 |
| Optimizer | AdamW 8-bit |
| Learning rate | 1.5e-4 |
| Scheduler | Cosine |
| Training steps | 3000 |
| Train dataset | ~57k samples |
| Validation dataset | ~3k samples |
The dataset consists of synthetic engineering problems generated using physics-based equations covering HVAC, hydronics, heat transfer, and structural estimation.
Example Capabilities
HVAC duct sizing
Input
Airflow = 3500 m³/h
Velocity = 6 m/s
Output
Select round duct ≈ Ø480 mm
Steps
Q = 0.972 m³/s
A = 0.162 m²
d ≈ 480 mm
Pump power estimation
Input
Flow = 4 m³/h
Head = 20 m
Efficiency = 0.7
Output
Pump power ≈ 0.31 kW
Steps
P = ρ g Q H / η
Structural stress calculation
Input
Moment = 20 kNm
Section modulus = 250 cm³
Output
Bending stress ≈ 80 MPa
Engineering sanity check
Input
Size round duct
Airflow = 0.3 m³/h
Velocity = 5 m/s
Output
Airflow extremely small for HVAC systems.
Likely unit mismatch — confirm m³/h vs m³/s.
Engineering Tasks Covered
HVAC
• duct sizing
• fan power estimation
• cooling coil loads
Hydronics
• pipe velocity sizing
• pump power estimation
• water flow calculations
Heat Transfer
• wall heat loss estimation
Structural Pre-checks
• beam bending stress
• beam deflection estimation
• tributary load calculation
• slab load estimation
Engineering Safety Checks
• unrealistic velocities
• unrealistic ΔT values
• unit mismatches
• missing critical inputs
Limitations
This model is experimental and intended for educational or exploratory use.
Known limitations include:
• calculations may require verification
• reasoning depth is limited by model size
• structural calculations are simplified
• model should not be used for final engineering design
All results must be validated using professional engineering standards (ASHRAE, Eurocode, etc.).
Usage
Load the base model and apply the LoRA adapter.
Example using Unsloth:
from unsloth import FastLanguageModel
model, tokenizer = FastLanguageModel.from_pretrained(
model_name="Qwen/Qwen2.5-1.5B-Instruct",
load_in_4bit=True
)
model.load_adapter("path_to_adapter")
FastLanguageModel.for_inference(model)
Training Dataset
The model was trained on the following dataset:
https://huggingface.co/datasets/Irfanuruchi/building-engineering-synthetic-dataset-v5
Intended Use
This model is designed for:
• educational engineering demonstrations
• quick engineering pre-check calculations
• concept-stage HVAC sizing
• structural estimation checks
It should never be used for safety-critical engineering decisions without verification.
Future Work
Planned improvements include:
• expanding dataset beyond 100k engineering problems
• deeper structural engineering reasoning
• multi-step engineering calculation chains
• integration of engineering standards (ASHRAE, Eurocode)
• improved unit consistency detection
Version History
V1 – Initial experiments
V2 – Dataset structure improvements
V3 – HVAC reasoning tasks
V4 – Engineering pre-check assistant (5k dataset)60k), HVAC + structural reasoning + engineering sanity checks
V5 – Expanded dataset (
Author
Irfan Uruchi
This project explores lightweight domain-specialized LLMs for engineering reasoning and pre-design validation (also used as testing for my 32B parameter LLM launching by June).