AUTOMOTIVE
Domain-adapted variant of Qwen2.5-3B-Instruct, fine-tuned on automotive instruction-following data using QLoRA (4-bit quantized LoRA fine-tuning). Created as an experimental learning and research project focused on modern parameter-efficient fine-tuning workflows in 2026-style LLM engineering.
Specialized toward automotive-related question answering and technical explanations. Trained on a curated subset of 4,000 samples from the BAAI automotive industry instruction dataset.
Fine-tuned on the English subset of BAAI/IndustryInstruction_Automobiles. A custom subset of approximately 4,000 automotive instruction samples was selected for training.
The dataset consists primarily of automotive technical Q&A, diagnostic explanations, vehicle systems knowledge, and maintenance and repair related instructions.
# Load model from Hugging Face Hub from transformers import AutoTokenizer, AutoModelForCausalLMmodel_name = "Nasim435/Qwen-3B-Automotive-4000"
tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained( model_name, device_map="auto" )
prompt = "Explain symptoms of a failing alternator." messages = [{"role": "user", "content": prompt}]
text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True )
inputs = tokenizer(text, return_tensors="pt").to(model.device) outputs = model.generate( **inputs, max_new_tokens=150, temperature=0.7, top_p=0.9, do_sample=True )
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
- Experimental fine-tuned model — not intended for production safety systems
- May hallucinate or generate inaccurate automotive advice
- Not suitable for safety-critical or professional mechanical decision-making
- Trained on a relatively small subset (~4k samples); generalization may be limited
Apache 2.0 · English · 2026
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