---
language:
- en
license: apache-2.0
tags:
- phi-3
- qlora
- predictive-maintenance
- industrial-ai
- steel-manufacturing
library_name: transformers
pipeline_tag: text-generation
base_model: microsoft/Phi-3-mini-4k-instruct
model-index:
- name: alloy-agent-phi3-maintenance
  results:
  - task:
      type: text-generation
    metrics:
    - name: Evaluation Loss
      type: loss
      value: 0.02508
---

# Alloy-Agent Phi-3 Maintenance

Fine-tuned Phi-3-mini-4k-instruct for predictive maintenance tasks in industrial environments. The model was trained on equipment sensor data, failure diagnostics, and maintenance procedures from steel manufacturing contexts.

## Model Overview

This is a QLoRA fine-tuned version of microsoft/Phi-3-mini-4k-instruct (3.8B parameters) specialized for equipment maintenance analysis. Training focused on interpreting sensor readings, diagnosing failure modes, and generating maintenance recommendations.

The model handles tasks like remaining useful life (RUL) estimation, root cause analysis, risk classification, and maintenance procedure generation based on equipment telemetry data.

## Training Data

Dataset: 1,973 maintenance records combining NASA CMAPSS turbofan data, UCI AI4I 2020 predictive maintenance dataset, and synthetic domain scenarios.

Split: 1,776 training / 197 validation

Input format:
```
Equipment: [type] | ID: [id]
Operating Hours: [hours]
Sensor Readings: Temperature, Vibration, Pressure, etc.
```

Output format:
```
DIAGNOSIS: [failure analysis]
ROOT CAUSE: [technical cause]
RISK LEVEL: [LOW/MEDIUM/HIGH/CRITICAL]
RUL: [hours] ± [confidence]
RECOMMENDATIONS: [maintenance actions]
```

## Training Configuration

Hardware: Google Colab T4 GPU (15GB VRAM)  
Duration: 4.5 hours, 666 steps over 3 epochs  
Final eval loss: **0.02508**

Method: QLoRA (4-bit quantization + LoRA adapters)
- LoRA rank: 16, alpha: 16, dropout: 0.05
- Target modules: all attention and MLP projection layers
- Trainable params: 29.9M / 3.8B (0.78%)

Hyperparameters:
```
learning_rate: 2e-4
batch_size: 8 (2 per device, 4 gradient accumulation steps)
optimizer: adamw_8bit
weight_decay: 0.01
warmup_steps: 50
max_grad_norm: 1.0
lr_scheduler: linear
fp16: True
gradient_checkpointing: True
```

Training utilized Unsloth for 2x speedup during fine-tuning.

## Usage

Basic inference:

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "abdul-nazeer/alloy-agent-phi3-maintenance",
    device_map="auto",
    torch_dtype="auto",
    trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained("abdul-nazeer/alloy-agent-phi3-maintenance")

prompt = """<|system|>You are an industrial maintenance AI assistant specialized in steel plant equipment analysis.<|end|>
<|user|>Equipment: Air Compressor Unit
Temperature: 95°C (baseline: 75°C)
Vibration: 1.2 mm/s (baseline: 0.5 mm/s)
Pressure: 7.8 bar (baseline: 8.5 bar)
Operating Hours: 2,150 hours

Analyze and provide maintenance recommendations.<|end|>
<|assistant|>"""

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(
    **inputs,
    max_new_tokens=400,
    temperature=0.7,
    do_sample=True,
    top_p=0.9
)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response.split("<|assistant|>")[1])
```

For faster inference with 4-bit quantization:

```python
from unsloth import FastLanguageModel

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name="abdul-nazeer/alloy-agent-phi3-maintenance",
    max_seq_length=4096,
    dtype=None,
    load_in_4bit=True,
)
FastLanguageModel.for_inference(model)
```

## Performance

Training converged smoothly with no overfitting:

```
Step   0: train_loss ~2.50
Step 100: train_loss 0.52, eval_loss 0.0687
Step 200: train_loss 0.18
Step 400: train_loss 0.08
Step 666: train_loss 0.025, eval_loss 0.02508
```

Loss reduction: 2.5 → 0.025 (100x improvement)

Inference: ~0.7s per response on T4 GPU with 4-bit quantization

## Limitations

- Trained primarily on steel plant equipment data - performance on other industrial domains may vary
- Outputs should be validated by maintenance engineers for critical systems
- Model provides estimates, not guarantees - RUL predictions have inherent uncertainty
- English language only
- Requires structured sensor data inputs for best results

## Use Cases

The model is designed for decision support in industrial maintenance:
- Early failure detection from sensor anomalies
- RUL estimation for maintenance scheduling
- Root cause analysis during equipment diagnostics
- Generating maintenance work orders and procedures

Not intended for:
- Autonomous control of equipment
- Safety-critical decisions without human review
- Financial/legal advice
- Medical equipment diagnostics

## Technical Details

Architecture: Phi-3-mini-4k-instruct base
- Total parameters: 3.82B
- Trainable (LoRA): 29.88M (0.78%)
- Quantization: 4-bit NF4
- Context window: 4096 tokens (2048 used in training)
- Attention heads: 32
- Hidden size: 3072
- Layers: 32

Requirements:
- Minimum: 4GB GPU VRAM (with 4-bit quantization)
- Recommended: 8GB+ GPU VRAM for production
- Dependencies: transformers, torch, accelerate, bitsandbytes

## Citation

```bibtex
@misc{alloy-agent-phi3-2026,
  title={Alloy-Agent Phi-3: Fine-Tuned Model for Industrial Predictive Maintenance},
  author={Abdul Nazeer},
  year={2026},
  publisher={HuggingFace},
  url={https://huggingface.co/abdul-nazeer/alloy-agent-phi3-maintenance}
}
```

Base model:
```bibtex
@article{phi3,
  title={Phi-3 Technical Report},
  author={Microsoft},
  year={2024},
  url={https://arxiv.org/abs/2404.14219}
}
```

## License

Apache 2.0 (inherited from Phi-3 base model)

## Acknowledgments

Built on microsoft/Phi-3-mini-4k-instruct. Training optimized with Unsloth. Datasets sourced from NASA CMAPSS and UCI AI4I 2020.