Irfanuruchi/qwen2.5-1.5b-buildeng-openvino-int8

BuildEng 1.5B OpenVINO (INT8)

Intel optimized version of BuildEng 1.5B.

This INT8 version keeps more precision than INT4 while still staying lightweight and practical for OpenVINO inference on Intel hardware.

What can it help with?

• Building defects and pathology
• Reinforced concrete and masonry
• Foundation and settlement issues
• Structural inspection guidance
• Construction best practices
• Civil and building engineering Q&A

Important

BuildEng is an AI assistant. It should not replace professional engineering judgment, real site inspections, licensed structural assessments, or local building codes.

Use it as support, not as final authority.

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Base Model

• Qwen2.5-1.5B-Instruct

Fine-tuned by

• Irfan Uruçi

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Why this version?

• Higher precision than INT4
• Still small and efficient
• Good choice when quality matters more than smallest size

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Installation

pip install "optimum-intel[openvino]" openvino transformers

Usage

from optimum.intel.openvino import OVModelForCausalLM
from transformers import AutoTokenizer

model_id = "Irfanuruchi/qwen2.5-1.5b-buildeng-openvino-int8"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = OVModelForCausalLM.from_pretrained(model_id, device="CPU", compile=True)

messages = [
    {"role": "system", "content": "You are BuildEng. Give concise, inspection-first building engineering guidance."},
    {"role": "user", "content": "A homeowner reports diagonal cracks near a window corner. What should be inspected first?"}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors="pt")

output = model.generate(**inputs, max_new_tokens=160, do_sample=False)
new_tokens = output[0][inputs["input_ids"].shape[-1]:]

print(tokenizer.decode(new_tokens, skip_special_tokens=True))

Devices

• CPU
• GPU
• NPU, where supported by OpenVINO and the system

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Other Formats

Format	Repository
Hugging Face	Irfanuruchi/qwen2.5-1.5b-buildeng
GGUF	Irfanuruchi/qwen2.5-1.5b-buildeng-GGUF-Q4_K_M
MLX 4-bit	Irfanuruchi/qwen2.5-1.5b-buildeng-mlx-4bit
MLX 8-bit	Irfanuruchi/qwen2.5-1.5b-buildeng-mlx-8bit
OpenVINO INT4	Irfanuruchi/qwen2.5-1.5b-buildeng-openvino-int4

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Built by Irfan Uruçi
Computer Engineer