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hawkeye-warehouse-safety-lora
Jsohal174/hawkeye-warehouse-safety-lora
hawkeye-warehouse-safety-lora is a image-text-to-text model from Jsohal174. Use it for the image-text-to-text task on the model card, and read the license before you ship it in a product. It is set up for peft. The card lists the license as cc-by-4.0.
LoRA adapter for warehouse safety hazard classification, trained on synthetic warehouse data on top of unsloth/Qwen3.5-2B.
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Repo size
113 MB
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.safetensors92.5 MB · 82%
At a glance
- Task
- Image-Text-to-Text
- Library
- peft
- License
- cc-by-4.0
- Access
- Public
- Created
- Apr 12, 2026
- Updated
- Sep 18, 2026
- SHA
- ac813996
Try a prompt
Base models
Model card
From the Hugging Face model README
Warehouse Safety Hazard Detection, LoRA Adapter
LoRA adapter for warehouse safety hazard classification, trained on synthetic warehouse data on top of unsloth/Qwen3.5-2B.
If you just want to run the model, use the Q4 GGUF release instead. This repo is the raw adapter for people who want to merge it themselves or continue training.
Research project at the University of Guelph supervised by Dr. John Akinyemi.
Adapter configuration
| Setting | Value |
|---|---|
| Base model | unsloth/Qwen3.5-2B |
| Rank (r) | 16 |
| Alpha | 32 |
| Dropout | 0.05 |
| Bias | none |
| Task type | CAUSAL_LM |
| Targets | attention and MLP projections, vision and language towers |
| Adapter size | ~88 MB |
Usage
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoProcessor
base = AutoModelForCausalLM.from_pretrained("unsloth/Qwen3.5-2B", device_map="auto")
model = PeftModel.from_pretrained(base, "Jsohal174/hawkeye-warehouse-safety-lora")
processor = AutoProcessor.from_pretrained("unsloth/Qwen3.5-2B")
Categories
spill, forklift_violation, improper_stacking, obstacle, safe
Results
Held-out test set, n=186 images. Measured after merging and quantizing to Q4_K_M, running on a Mac Mini M4.
| Configuration | Accuracy | Latency |
|---|---|---|
| Trained, Q4 no-think (deployed) | 91.9% | ~3 s |
| Trained, Q4 think | 92.5% | ~4.2 s |
| Base, zero-shot | 65.3% | ~3.1 s |
Training delta: +26.6 percentage points over the same base, same prompt, same hardware.
Training
2,521 conversations from 727 images, 3.5 hours on a free Kaggle T4.
Related
- Q4 GGUF, Ollama-ready: https://huggingface.co/Jsohal174/hawkeye-warehouse-safety-gguf
- Full precision merged: https://huggingface.co/Jsohal174/hawkeye-warehouse-safety-merged
- Dataset, 913 labeled images: https://huggingface.co/datasets/Jsohal174/warehouse-safety-hazard-dataset
- Code and benchmarks: https://github.com/Jsohal174/safety-hazard-detection
Citation
@misc{sohal2026synthetic,
author = {Sohal, Jaskirat Singh},
title = {Synthetic Specialization: Training and Compressing Foundation Models for Domain-Specific Tasks},
year = {2026},
howpublished = {University of Guelph},
note = {Supervisor: Dr. John Akinyemi},
url = {https://github.com/Jsohal174/safety-hazard-detection}
}
- Task
- Image-Text-to-Text
- Library
- peft
- License
- cc-by-4.0
- Created
- Apr 12, 2026
- Updated
- Sep 18, 2026