📊 Model Details

🎯 Specialization

This model is fine-tuned on real-world industrial maintenance scenarios for:

• Equipment Types: Rolling Mills, Blast Furnace Blowers, Compressors, Conveyor Motors
• Failure Analysis: Bearing wear, thermal overload, electrical faults, pressure system failures
• Maintenance Procedures: Step-by-step repair instructions with safety protocols
• Technical Specifications: Torque values, part numbers, measurement standards

🚀 Quick Start

Installation

bash
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pip install transformers torch peft

Usage (Cross-Platform)

python
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from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

# Load base model
base_model = AutoModelForCausalLM.from_pretrained(
    "microsoft/Phi-3.5-mini-instruct",
    torch_dtype="auto",
    device_map="auto"
)

# Load fine-tuned adapter
model = PeftModel.from_pretrained(
    base_model,
    "Santhoshkumarp/phi35-maintenance-wizard-lora"
)

tokenizer = AutoTokenizer.from_pretrained("microsoft/Phi-3.5-mini-instruct")

# Generate maintenance guidance
prompt = """<|system|>
You are an expert AI maintenance engineer specializing in steel plant equipment.
<|end|>
<|user|>
Rolling Mill RM1 shows excessive vibration (12.5 mm/s) and high temperature (95°C). 
What could be the root cause and recommended actions?
<|end|>
<|assistant|>
"""

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=500, temperature=0.7)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(response)

Apple Silicon (MLX - Faster)

bash
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pip install mlx-lm

python
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from mlx_lm import load, generate

model, tokenizer = load(
    "microsoft/Phi-3.5-mini-instruct",
    adapter_path="Santhoshkumarp/phi35-maintenance-wizard-lora"
)

prompt = """<|system|>
You are an expert AI maintenance engineer specializing in steel plant equipment.
<|end|>
<|user|>
Rolling Mill RM1 shows excessive vibration (12.5 mm/s) and high temperature (95°C). 
What could be the root cause and recommended actions?
<|end|>
<|assistant|>
"""

response = generate(model, tokenizer, prompt=prompt, max_tokens=500, verbose=False)
print(response)

📈 Training Details

Dataset

• Size: 2,027 maintenance Q&A pairs
• Sources:
  • Historical incident reports
  • Standard Operating Procedures (SOPs)
  • Equipment maintenance manuals
  • Sensor data analysis scenarios

Training Configuration

yaml
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Model: microsoft/Phi-3.5-mini-instruct
Method: LoRA fine-tuning
Rank: 8
Alpha: 16
Dropout: 0.05
Learning Rate: 2e-4
Batch Size: 4
Gradient Accumulation: 4
Epochs: 3
Optimizer: AdamW
Scheduler: Cosine with warmup
Warmup Steps: 100
Max Sequence Length: 2048

Hardware

• Training: Apple M3 Max (MLX framework)
• Training Time: ~45 minutes
• Memory: ~12GB RAM

💡 Example Outputs

Query: "Blast furnace blower shows high current (65A) and unusual noise. Diagnose the issue."

Base Phi-3.5:

"High current in a blower could indicate several issues. Check the motor, bearings, and ensure proper maintenance."

Fine-tuned Model:

"Root Cause: Motor overload due to bearing degradation or misalignment.

Immediate Actions:

1. STOP blower operation immediately (safety-critical equipment)
2. Lock out / Tag out (LOTO) - ensure zero energy state
3. Monitor for smoke or excessive heating

Diagnostic Steps:

1. Check bearing condition using vibration analysis
2. Measure motor winding resistance (megger test)
3. Inspect coupling alignment
4. Verify voltage balance across all phases

Parts Required: Bearing assembly (Part: BF-BRG-001), Motor contactor if damaged

Repair Procedure: Follow SOP-BF-003 for bearing replacement. Torque coupling bolts to 85 Nm."

⚠️ Limitations

• Domain-Specific: Optimized for steel plant equipment, may not generalize to other industries
• Safety-Critical: Always verify recommendations with qualified maintenance personnel
• English Only: Trained on English-language maintenance documentation
• Sensor Data: Best performance with specific numerical values (vibration, temperature, current, pressure)

🔧 System Architecture

This model is used in the Industrial Agent AI system:

markdown
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User Query
    ↓
Equipment Sensor Data + Historical Context
    ↓
RAG Retrieval (Qdrant Vector DB)
    ↓
Fine-tuned Phi-3.5 Mini (this model)
    ↓
Multi-Agent Analysis
    ↓
Actionable Maintenance Plan + Citations

📄 Citation

bibtex
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@misc{phi35-maintenance-wizard-2024,
  title  = {Phi-3.5 Mini Industrial Maintenance Wizard LoRA Adapter},
  author = {Santhosh Kumar P},
  year   = {2024},
  url    = {https://huggingface.co/Santhoshkumarp/phi35-maintenance-wizard-lora}
}

📜 License

MIT License - Adapter weights only. Base model license: MIT

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Built for the Industrial AI Hackathon • GitHub Repository