| --- |
| library_name: transformers |
| tags: |
| - smollm2 |
| - automotive |
| - question-answering |
| - instruction-tuning |
| - domain-adaptation |
| - workshop-assistant |
| --- |
| |
| # Model Card for SmolLM2-135M-Technician-QA |
|
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| 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. |
|
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| It is optimized for lightweight deployment in workshop assistants, service center copilots, and edge devices. |
|
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| --- |
|
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| ## Model Details |
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| ### Model Description |
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| SmolLM2-135M-Technician-QA is a compact instruction-following language model fine-tuned on a curated dataset of technician question-answer pairs covering: |
|
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| - Customer vehicle issues |
| - Technical diagnostics |
| - Work order lifecycle |
| - Periodic service procedures |
| - Spare part replacement decisions |
| - On-site breakdown support |
|
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| The model is designed for real-world automotive service environments where fast and efficient inference is required. |
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|
| - **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 |
|
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| ### Model Sources |
|
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| - **Repository:** https://huggingface.co/<your-username>/SmolLM2-135M-Technician-QA |
| - **Base Model:** https://huggingface.co/HuggingFaceTB/SmolLM2-135M |
|
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| --- |
|
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| ## Uses |
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| ### Direct Use |
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| This model can be used for: |
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| - Automotive technician assistants |
| - Workshop chatbot systems |
| - Service advisor support |
| - Troubleshooting guidance |
| - Training simulators for technicians |
|
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| ### Downstream Use |
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| The model can be integrated into: |
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| - RAG systems with service manuals |
| - Mobile workshop applications |
| - Edge diagnostic tools |
| - Voice-based service assistants |
|
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| ### Out-of-Scope Use |
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| This model should NOT be used for: |
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| - Safety-critical vehicle control |
| - Legal or compliance decisions |
| - Autonomous driving systems |
| - Financial or medical advice |
|
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| --- |
|
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| ## Bias, Risks, and Limitations |
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| - 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 |
|
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| ### Recommendations |
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| - Always verify outputs with OEM service manuals |
| - Use as an assistive tool, not a final authority |
| - Combine with RAG for production deployment |
|
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| --- |
|
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| ## How to Get Started with the Model |
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|
| ```python |
| from transformers import AutoTokenizer, AutoModelForCausalLM |
| |
| model_id = "<your-username>/SmolLM2-135M-Technician-QA" |
| |
| tokenizer = AutoTokenizer.from_pretrained(model_id) |
| model = AutoModelForCausalLM.from_pretrained(model_id) |
| |
| prompt = "Customer says the car battery drains overnight. What should you check?" |
| |
| inputs = tokenizer(prompt, return_tensors="pt") |
| outputs = model.generate(**inputs, max_new_tokens=120) |
| |
| print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |
| ``` |
| --- |
| ## Evaluation |
| ## Testing Data, Factors & Metrics |
| ## Testing Data |
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| Held-out automotive technician QA samples from the same domain. |
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| ## Factors |
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| Customer complaint handling |
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| Diagnostic reasoning |
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| Spare part replacement logic |
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| Service workflow understanding |
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| ## Metrics |
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| Perplexity |
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| Instruction-following accuracy |
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| Manual domain evaluation |
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| ## Results |
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| Strong performance on workshop troubleshooting queries |
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| Accurate step-by-step diagnostic suggestions |
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| Fast inference on CPU |
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| ## Summary |
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| The fine-tuned model shows clear domain adaptation compared to the base SmolLM2 model, especially for automotive service workflows. |
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