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id)
instruction → tugas,input → konteks,output → jawaban.1from transformers import AutoModelForCausalLM, AutoTokenizer
2
3model_id = "Syamsuddin/qwen3-4B-Indonesia-AdminAI"
4
5tokenizer = AutoTokenizer.from_pretrained(model_id)
6model = AutoModelForCausalLM.from_pretrained(model_id)
7
8prompt = "Apa kode sumber pendanaan untuk 'Pendapatan Asli Daerah'?"
9inputs = tokenizer(prompt, return_tensors="pt")
10outputs = model.generate(**inputs, max_new_tokens=200)
11
12print(tokenizer.decode(outputs[0], skip_special_tokens=True))1@misc{syamsuddin2025qwenid,
2 author = {Syamsuddin},
3 title = {Qwen3-4B-Thinking Indonesia Admin AI},
4 year = {2025},
5 howpublished = {HuggingFace Models},
6 url = {https://huggingface.co/Syamsuddin/qwen3-4B-Indonesia-AdminAI},
7 note = {Fine-tuned Qwen3-4B untuk konteks pemerintahan Indonesia (APBD, SAKIP, Wilayah)}
8}