Model Card for SmolLM2-135M-Technician-QA
This model is a domain-adapted version of HuggingFaceTB/SmolLM2-135M, fine-tuned to answer questions related to automotive service, technician workflows, diagnostics, and spare part replacement scenarios.
It is optimized for lightweight deployment in workshop assistants, service center copilots, and edge devices.
Model Details
Model Description
SmolLM2-135M-Technician-QA is a compact instruction-following language model fine-tuned on a curated dataset of technician question-answer pairs covering:
- Customer vehicle issues
- Technical diagnostics
- Work order lifecycle
- Periodic service procedures
- Spare part replacement decisions
- On-site breakdown support
The model is designed for real-world automotive service environments where fast and efficient inference is required.
- Developed by: Shailesh H
- Funded by: Self / Research & Development
- Shared by: Shailesh H
- Model type: Causal Language Model (Instruction-tuned)
- Language(s) (NLP): English
- License: Apache-2.0
- Finetuned from model: HuggingFaceTB/SmolLM2-135M
Model Sources
Uses
Direct Use
This model can be used for:
- Automotive technician assistants
- Workshop chatbot systems
- Service advisor support
- Troubleshooting guidance
- Training simulators for technicians
Downstream Use
The model can be integrated into:
- RAG systems with service manuals
- Mobile workshop applications
- Edge diagnostic tools
- Voice-based service assistants
Out-of-Scope Use
This model should NOT be used for:
- Safety-critical vehicle control
- Legal or compliance decisions
- Autonomous driving systems
- Financial or medical advice
Bias, Risks, and Limitations
- Trained on synthetic domain data → may not cover all vehicle models
- Limited general world knowledge due to small model size
- May generate plausible but incorrect repair steps
- English-only responses
Recommendations
- Always verify outputs with OEM service manuals
- Use as an assistive tool, not a final authority
- Combine with RAG for production deployment
How to Get Started with the Model
1from transformers import AutoTokenizer, AutoModelForCausalLM
2
3model_id = "<your-username>/SmolLM2-135M-Technician-QA"
4
5tokenizer = AutoTokenizer.from_pretrained(model_id)
6model = AutoModelForCausalLM.from_pretrained(model_id)
7
8prompt = "Customer says the car battery drains overnight. What should you check?"
9
10inputs = tokenizer(prompt, return_tensors="pt")
11outputs = model.generate(**inputs, max_new_tokens=120)
12
13print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Evaluation
Testing Data, Factors & Metrics
Testing Data
Held-out automotive technician QA samples from the same domain.
Factors
Customer complaint handling
Diagnostic reasoning
Spare part replacement logic
Service workflow understanding
Metrics
Perplexity
Instruction-following accuracy
Manual domain evaluation
Results
Strong performance on workshop troubleshooting queries
Accurate step-by-step diagnostic suggestions
Fast inference on CPU
Summary
The fine-tuned model shows clear domain adaptation compared to the base SmolLM2 model, especially for automotive service workflows.
