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distilgpt2 model using LoRA, trained on a small dataset to provide career advice for the AI industry in 2025.1from transformers import AutoModelForCausalLM, AutoTokenizer
2from peft import PeftModel
3
4base_model = AutoModelForCausalLM.from_pretrained("distilgpt2")
5tokenizer = AutoTokenizer.from_pretrained("jinv2/ai-job-navigator-model")
6model = PeftModel.from_pretrained(base_model, "jinv2/ai-job-navigator-model")
7
8prompt = "根据最新的AI行业趋势,提供2025年的职业建议:"
9inputs = tokenizer(prompt, return_tensors="pt")
10outputs = model.generate(**inputs, max_length=200, do_sample=True, temperature=0.7, top_k=50, top_p=0.9)
11print(tokenizer.decode(outputs[0], skip_special_tokens=True))