Instructions to use Irfanuruchi/qwen2.5-32b-buildeng-GGUF-Q4_K_M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Irfanuruchi/qwen2.5-32b-buildeng-GGUF-Q4_K_M with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Irfanuruchi/qwen2.5-32b-buildeng-GGUF-Q4_K_M",
	filename="buildeng-v8-32b-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Irfanuruchi/qwen2.5-32b-buildeng-GGUF-Q4_K_M with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Irfanuruchi/qwen2.5-32b-buildeng-GGUF-Q4_K_M:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Irfanuruchi/qwen2.5-32b-buildeng-GGUF-Q4_K_M:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Irfanuruchi/qwen2.5-32b-buildeng-GGUF-Q4_K_M:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Irfanuruchi/qwen2.5-32b-buildeng-GGUF-Q4_K_M:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf Irfanuruchi/qwen2.5-32b-buildeng-GGUF-Q4_K_M:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Irfanuruchi/qwen2.5-32b-buildeng-GGUF-Q4_K_M:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf Irfanuruchi/qwen2.5-32b-buildeng-GGUF-Q4_K_M:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Irfanuruchi/qwen2.5-32b-buildeng-GGUF-Q4_K_M:Q4_K_M

Use Docker

docker model run hf.co/Irfanuruchi/qwen2.5-32b-buildeng-GGUF-Q4_K_M:Q4_K_M

LM Studio
Jan

vLLM

How to use Irfanuruchi/qwen2.5-32b-buildeng-GGUF-Q4_K_M with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Irfanuruchi/qwen2.5-32b-buildeng-GGUF-Q4_K_M"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Irfanuruchi/qwen2.5-32b-buildeng-GGUF-Q4_K_M",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Irfanuruchi/qwen2.5-32b-buildeng-GGUF-Q4_K_M:Q4_K_M

Ollama
How to use Irfanuruchi/qwen2.5-32b-buildeng-GGUF-Q4_K_M with Ollama:
```
ollama run hf.co/Irfanuruchi/qwen2.5-32b-buildeng-GGUF-Q4_K_M:Q4_K_M
```

Unsloth Studio

How to use Irfanuruchi/qwen2.5-32b-buildeng-GGUF-Q4_K_M with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Irfanuruchi/qwen2.5-32b-buildeng-GGUF-Q4_K_M to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Irfanuruchi/qwen2.5-32b-buildeng-GGUF-Q4_K_M to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Irfanuruchi/qwen2.5-32b-buildeng-GGUF-Q4_K_M to start chatting

How to use Irfanuruchi/qwen2.5-32b-buildeng-GGUF-Q4_K_M with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Irfanuruchi/qwen2.5-32b-buildeng-GGUF-Q4_K_M:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Irfanuruchi/qwen2.5-32b-buildeng-GGUF-Q4_K_M:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Irfanuruchi/qwen2.5-32b-buildeng-GGUF-Q4_K_M with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Irfanuruchi/qwen2.5-32b-buildeng-GGUF-Q4_K_M:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default Irfanuruchi/qwen2.5-32b-buildeng-GGUF-Q4_K_M:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use Irfanuruchi/qwen2.5-32b-buildeng-GGUF-Q4_K_M with Docker Model Runner:
```
docker model run hf.co/Irfanuruchi/qwen2.5-32b-buildeng-GGUF-Q4_K_M:Q4_K_M
```

Lemonade

How to use Irfanuruchi/qwen2.5-32b-buildeng-GGUF-Q4_K_M with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Irfanuruchi/qwen2.5-32b-buildeng-GGUF-Q4_K_M:Q4_K_M

Run and chat with the model

lemonade run user.qwen2.5-32b-buildeng-GGUF-Q4_K_M-Q4_K_M

List all available models

lemonade list

Qwen2.5-32B BuildEng GGUF (Q4_K_M)

Repository: Irfanuruchi/qwen2.5-32b-buildeng-GGUF-Q4_K_M

This repository contains the GGUF Q4_K_M quantized release of BuildEng V8 32B based on Qwen/Qwen2.5-32B-Instruct.

BuildEng is a domain-specialized engineering language model project focused on civil engineering, structural reasoning, construction workflows, field diagnostics, and conservative engineering-assistant behavior.

The model was trained to approach engineering problems cautiously instead of behaving like an overconfident general-purpose assistant. The primary focus is inspection-first reasoning, uncertainty handling, structural risk awareness, and separating symptoms from confirmed diagnosis.

This GGUF release allows the model to run through llama.cpp, Ollama, LM Studio, KoboldCpp, and other GGUF-compatible inference frameworks.

Quantization

Quant type: Q4_K_M

Original source: merged BuildEng V8 32B model

Conversion stack: llama.cpp GGUF conversion and quantization

The Q4_K_M quantization was selected as the primary public GGUF release because it provides a strong balance between reasoning quality, memory usage, and inference performance.

Dedication

This release is dedicated to my father for his birthday.

He is a civil and building engineer, and one of the main reasons I chose engineering myself. A large part of how I understand responsibility, discipline, and technical thinking comes from him.

BuildEng is my own field connected to the engineering world that inspired me growing up.

“My father is the engineer I looked up to long before I understood what engineering really meant.”

Thank you for everything.

Base Model

Qwen/Qwen2.5-32B-Instruct

Dataset

Dataset repository: Irfanuruchi/buildeng

Training dataset: BuildEng V8 Final

Total validated samples: 145,117

The BuildEng V8 dataset was designed primarily around civil and structural engineering workflows with conservative engineering reasoning behavior.

The dataset includes reinforced concrete beams, slabs, columns, footings, retaining walls, structural load paths, settlement reasoning, soil behavior, failure analysis, temporary bracing, temporary shoring, waterproofing failures, renovation unknowns, HVAC airflow reasoning, inspection workflows, uncertainty handling, contradiction handling, repair logic, multi-turn engineering diagnosis, and adversarial engineering prompts.

The dataset was intentionally structured to reduce unsafe certainty and encourage more cautious engineering behavior.

Main Behavior Goals

BuildEng 32B was trained to:

separate symptoms from diagnosis
avoid unsupported structural approval
request missing engineering information
reduce unsafe certainty
follow inspection-first workflows
recognize load-path concerns
identify potentially unsafe field conditions
support investigation and repair workflows
distinguish serviceability concerns from structural-capacity concerns
respond conservatively to cracking, settlement, corrosion, deflection, demolition, moisture, and structural damage scenarios

The model intentionally avoids behaving like an overconfident assistant.

Usage

Example llama.cpp usage:

./llama-cli \
  -m buildeng-v8-32b-Q4_K_M.gguf \
  -c 4096 \
  -ngl 999

Example Ollama Modelfile:

FROM ./buildeng-v8-32b-Q4_K_M.gguf

PARAMETER num_ctx 4096

SYSTEM """
You are BuildEng V8, a conservative civil and structural engineering assistant.
"""

Important Limitation

This model is not a licensed engineer and must not be used as final engineering approval, design certification, or construction sign-off.

It is intended for research, education, engineering-assistant workflows, drafting support, and preliminary engineering reasoning only.

Final decisions involving structural safety, demolition, occupancy, repairs, construction approval, or certification should always be reviewed and approved by a qualified licensed engineer.

Author

Irfan Uruchi

Part of my ongoing work on domain-specialized engineering language models, structural reasoning datasets, and practical civil/building engineering AI systems.

Downloads last month: 162

GGUF

Model size

33B params

Architecture

qwen2

Hardware compatibility

4-bit

Model tree for Irfanuruchi/qwen2.5-32b-buildeng-GGUF-Q4_K_M

Base model

Qwen/Qwen2.5-32B

Finetuned

Qwen/Qwen2.5-32B-Instruct

Quantized

(146)

this model

Irfanuruchi
/

qwen2.5-32b-buildeng-GGUF-Q4_K_M