jinv2/ai-job-navigator-model

AI Job Navigator Model

This is a fine-tuned distilgpt2 model using LoRA, trained on a small dataset to provide career advice for the AI industry in 2025.

Model Details

• Base Model: distilgpt2
• Fine-tuning Method: LoRA
• Training Data: 16 samples (148,668 characters) of AI industry career advice
• Training Steps: 10 steps, 5 epochs
• Loss: ~9.67 to 11.61

Usage

You can load and use the model as follows:

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base_model = AutoModelForCausalLM.from_pretrained("distilgpt2")
tokenizer = AutoTokenizer.from_pretrained("jinv2/ai-job-navigator-model")
model = PeftModel.from_pretrained(base_model, "jinv2/ai-job-navigator-model")

prompt = "根据最新的AI行业趋势，提供2025年的职业建议："
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200, do_sample=True, temperature=0.7, top_k=50, top_p=0.9)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))