PEGAAICC/emsLLM-4B

emsLLM: Industrial Equipment Fault Diagnosis & Maintenance Assistant

Model Summary

emsLLM is a Large Language Model (LLM) fine-tuned specifically for the Electronic Manufacturing Services (EMS) and industrial maintenance sectors. Built upon the Qwen3-4B architecture, this model has been trained on equipment operation manuals, troubleshooting guides, and standardized maintenance documents. It is designed to assist engineers with equipment fault diagnosis and SOP (Standard Operating Procedure) generation.

This is a Merged Version, meaning the fine-tuned LoRA weights have been merged into the base model. It can be loaded directly for inference without requiring additional adapters, ensuring easy deployment and integration.

Evaluation & Performance

We evaluated emsLLM against state-of-the-art general-purpose models (Llama-3.3-70B and Qwen3-32B) using an "LLM-as-a-Judge" approach. The evaluation focused on industrial fault diagnosis accuracy and inference speed.

1. Diagnosis Accuracy

Despite their significantly smaller parameter count, our fine-tuned models (trained on public technical documents) outperformed larger base models in retrieving and generating correct maintenance solutions.

Model	Public Test Set Accuracy	Private Test Set Accuracy
emsLLM-8B (Ours)	95.0%	100.0%
emsLLM-4B (Ours)	95.0%	96.0%
Qwen3-32B (Base)	90.0%	92.0%
Llama-3.3-70B (Base)	87.5%	92.0%

Key Finding: The emsLLM-8B achieves 95% accuracy on public technical queries and 100% on private domain tasks, surpassing the Llama-3.3-70B base model while using significantly fewer resources.

2. Inference Speed

For industrial applications requiring real-time response, our models offer ultra-low latency.

Model	Time to First Token (TTFT)	Time Per Output Token (TPOT)
emsLLM-4B	30.46 ms	3.20 ms
emsLLM-8B	58.92 ms	6.40 ms
Qwen3-32B	253.49 ms	20.91 ms
Llama-3.3-70B	428.14 ms	36.02 ms

Key Finding: The emsLLM-8B provides an approximate 6x speedup in generation speed (TPOT) compared to Llama-3.3-70B, making it highly suitable for edge deployment and rapid interaction.

Key Features

• Fault Diagnosis Assistant: Provides analysis of potential causes and maintenance suggestions for common production line equipment (e.g., robotic arms, dispensing machines).
• SOP Content Generation: Understands industrial documentation logic and assists in drafting standardized SOPs, including operational procedures and responsibility assignments.
• Domain Optimization: Enhanced understanding of maintenance terminology and logic compared to generic models, offering responses more aligned with engineering field requirements.
• Ready-to-Deploy: Weights are fully merged, supporting direct loading with standard inference frameworks (e.g., Hugging Face Transformers, vLLM).

Quick Start

Since the weights are already merged, you can load the model just like any standard Transformers model:

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# Replace with your actual Hugging Face model ID
model_id = "Your-Username/emsLLM" 

# 1. Load Tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_id)

# 2. Load Model
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# 3. Example: Fault Diagnosis
messages = [
    {"role": "system", "content": "You are a professional industrial equipment maintenance assistant."},
    {"role": "user", "content": "The dispensing machine on the production line has unstable output. What are the possible causes and solutions?"}
]

text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)

model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

generated_ids = model.generate(
    model_inputs.input_ids,
    max_new_tokens=512,
    temperature=0.1  # Low temperature is recommended for stability in industrial tasks
)

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Contact info

• Email: fred_tung@pegatroncorp.com