AUTOMOTIVE
Domain-adapted variant of Qwen2.5-3B-Instruct, fine-tuned on automotive instruction-following data using QLoRA with Unsloth optimization. Built as an experimental research project focused on modern PEFT workflows and efficient LLM engineering.
Specialized toward automotive question answering, diagnostic explanations, vehicle maintenance assistance, and technical guidance. Trained on a curated subset of 10,000 samples from the BAAI automotive industry instruction dataset.
Fine-tuned on the English subset of BAAI/IndustryInstruction_Automobiles. A custom subset of approximately 10,000 automotive instruction samples was selected for training.
Integrated Unsloth optimizations reduced model download size from ~6GB to ~3GB. Delivered stable low-memory 4-bit training, high GPU utilization, and accelerated transformer patching throughout the QLoRA workflow.
EVALUATED ON 100 SAMPLES
# Load model from Hugging Face Hub from transformers import AutoTokenizer, AutoModelForCausalLMmodel_name = "Nasim435/Qwen-3B-Automotive-10K"
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 ~10k samples; generalization may be limited
MIT License · English · 2026
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