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Here provides a code snippet with apply_chat_template to show you how to load the tokenizer and model and how to generate contents.
python
from transformers import AutoModelForCausalLM, AutoTokenizerdevice = "cuda" # the device to load the model onto model = AutoModelForCausalLM.from_pretrained( "kalisai/Nusantara-4B-Indo-Chat", torch_dtype="auto", device_map="auto")tokenizer = AutoTokenizer.from_pretrained("kalisai/Nusantara-4B-Indo-Chat") prompt = "Berikan saya resep memasak nasi goreng yang lezat."messages = [ {"role": "system", "content": "Kamu adalah Nusantara, asisten AI yang pintar."}, {"role": "user", "content": prompt}]text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True)model_inputs = tokenizer([text], return_tensors="pt").to(device) generated_ids = model.generate( model_inputs.input_ids, max_new_tokens=512)generated_ids = [ output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)] response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]If you use the Nusantara language model in your research or project, please cite it as:
text
@misc{zulfikar_aji_kusworo_2024, title={Nusantara: A Series of Versatile Open Weight Language Model of Bahasa Indonesia}, author={Zulfikar Aji Kusworo}, publisher={Hugging Face} journal={Hugging Face Repository}, year={2024} url = {https://huggingface.co/kalisai}}from transformers import AutoModelForCausalLM, AutoTokenizerdevice = "cuda" # the device to load the model onto model = AutoModelForCausalLM.from_pretrained( "kalisai/Nusantara-4B-Indo-Chat", torch_dtype="auto", device_map="auto")tokenizer = AutoTokenizer.from_pretrained("kalisai/Nusantara-4B-Indo-Chat") prompt = "Berikan saya resep memasak nasi goreng yang lezat."messages = [ {"role": "system", "content": "Kamu adalah Nusantara, asisten AI yang pintar."}, {"role": "user", "content": prompt}]text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True)model_inputs = tokenizer([text], return_tensors="pt").to(device) generated_ids = model.generate( model_inputs.input_ids, max_new_tokens=512)generated_ids = [ output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)] response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]