| --- |
| language: |
| - en |
| - zh |
| license: apache-2.0 |
| library_name: transformers |
| tags: |
| - industrial |
| - maintenance |
| - fault-diagnosis |
| - sop-generation |
| - qwen |
| base_model: Qwen/Qwen3-4B |
| pipeline_tag: text-generation |
| --- |
| |
| # 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: |
|
|
| ```python |
| 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 |
