---
base_model: unsloth/ERNIE-4.5-VL-28B-A3B-PT
library_name: peft
license: apache-2.0
pipeline_tag: image-text-to-text
tags:
- lora
- ernie
- gauge-reading
- industrial
- vision-language
- unsloth
- fine-tuned
datasets:
- custom
language:
- en
---

# MeterMind: Industrial Gauge Reading with ERNIE-4.5-VL

Fine-tuned LoRA adapter for reading analog industrial gauges from images.

## Model Description

MeterMind is a fine-tuned version of ERNIE-4.5-VL-28B-A3B that accurately reads analog gauge values from photographs. It was trained on synthetic industrial gauge images and achieves near-human accuracy.

- **Base Model:** [unsloth/ERNIE-4.5-VL-28B-A3B-PT](https://huggingface.co/unsloth/ERNIE-4.5-VL-28B-A3B-PT)
- **Fine-tuning Method:** LoRA (Low-Rank Adaptation)
- **Training Framework:** Unsloth + TRL

## Performance

| Metric | Baseline (Zero-Shot) | MeterMind (Fine-Tuned) | Improvement |
|--------|---------------------|------------------------|-------------|
| MAE | 2.82 | 0.60 | **79% better** |
| RMSE | 4.35 | 1.10 | **75% better** |
| Within ±1 | 53% | 87% | **+34 points** |
| Exact Match | 27% | 60% | **+33 points** |

## Supported Gauge Types

- Standard pressure gauges (0-100 PSI)
- Glycerin-filled pressure gauges (0-100 PSI)  
- Bimetal thermometers (0-220°F)

## Training Details

### Hyperparameters

- **LoRA rank (r):** 8
- **LoRA alpha:** 16
- **Learning rate:** 2e-4
- **Training steps:** 285 (1 epoch)
- **Batch size:** 2 (effective)
- **Warmup steps:** 10
- **Scheduler:** Linear
- **Optimizer:** AdamW 8-bit

### Target Modules

```
q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj, fc1, fc2
```

## Usage

```python
from transformers import AutoProcessor, AutoModelForVision2Seq
from peft import PeftModel

# Load base model
base_model = AutoModelForVision2Seq.from_pretrained("unsloth/ERNIE-4.5-VL-28B-A3B-PT")
processor = AutoProcessor.from_pretrained("unsloth/ERNIE-4.5-VL-28B-A3B-PT")

# Load LoRA adapter
model = PeftModel.from_pretrained(base_model, "luliuzee/metermind-ernie-gauge-lora")

# Inference
prompt = "What is the reading on this gauge? Reply with only the numeric value."
# ... process image and generate
```

## Intended Use

- Industrial inspection automation
- Digital rounds and operator logging
- Equipment monitoring systems
- Facility management applications

## Limitations

- Trained on synthetic data; real-world performance may vary
- Best suited for circular analog gauges with clear markings
- Requires clear, well-lit images for optimal accuracy

## Citation

```bibtex
@misc{metermind2024,
  title={MeterMind: Industrial Gauge Reading with Fine-Tuned ERNIE-4.5-VL},
  author={luliuzee},
  year={2024},
  publisher={HuggingFace}
}
```

## Links

- **Code Repository:** GitHub (MeterMind)
- **Hackathon:** [ERNIE AI Developer Challenge](https://baiduernieai.devpost.com)

## Framework Versions

- PEFT: 0.18.0
- Transformers: 4.x
- Unsloth: Latest