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PEGAAICC
Available on FriendliAI
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Model Details
Model Provider
PEGAAICC
Model Tree
Qwen/Qwen3-4B
Input Modalities
Output Modalities
Supported Functionality
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.
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.
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.
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.
Since the weights are already merged, you can load the model just like any standard Transformers model:
python
from transformers import AutoModelForCausalLM, AutoTokenizerimport torch # Replace with your actual Hugging Face model IDmodel_id = "Your-Username/emsLLM" # 1. Load Tokenizertokenizer = AutoTokenizer.from_pretrained(model_id) # 2. Load Modelmodel = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.bfloat16, device_map="auto") # 3. Example: Fault Diagnosismessages = [ {"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)from transformers import AutoModelForCausalLM, AutoTokenizerimport torch # Replace with your actual Hugging Face model IDmodel_id = "Your-Username/emsLLM" # 1. Load Tokenizertokenizer = AutoTokenizer.from_pretrained(model_id) # 2. Load Modelmodel = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.bfloat16, device_map="auto") # 3. Example: Fault Diagnosismessages = [ {"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)