library_name: transformers
widget:
- text: Siapakah Tan Malaka?
example_title: Tokoh
- text: Berikan saya resep memasak nasi goreng yang lezat.
example_title: Resep
- text: Bagaimana solusi untuk mengobati jerawat di wajah?
example_title: Solusi
pipeline_tag: text-generation
tags:
- conversational
- convAI
license: apache-2.0
language:
- id
- en
datasets:
- argilla/OpenHermes2.5-dpo-binarized-alpha
- wikimedia/wikipedia
- FreedomIntelligence/evol-instruct-indonesian
Model Description
Nusantara is a series of Open Weight Language Model of Bahasa Indonesia (Indonesia language). Nusantara is based from Qwen1.5 Language Model, finetuned by domain specific of datasets. As Chat-implemented language model, Nusantara is capable to do Question-Answering and respond to instructions given in Bahasa Indonesia. Due to limited resources, only 0.8B, 1.8B, 2.7B, 4B and 7B models are available. If you're interested in funding this project for further development, specific usage, or larger parameters, please contact us.
- Finetuned by: Kalis AI
- Funded by: Self-funded
- Model type: transformer-based decoder-only language model
- Language(s): Bahasa Indonesia (id), English (en)
- License: Nusantara is licensed under Apache-2.0, but any usage of this model should comply with Qwen License
- Finetuned from model: Qwen1.5-4B
Attentions!
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Because this model is also trained with uncensored datasets, there is the possibility of negative impacts arising from using this model. All kinds of impacts that arise as a result of using this model are entirely the responsibility of the user. The model maker is not responsible for any risks incurred.
How to Get Started with the Model
Here provides a code snippet with apply_chat_template to show you how to load the tokenizer and model and how to generate contents.
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"kalisai/Nusantara-7B-Indo-Chat",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("kalisai/Nusantara-7B-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]
Citation
If you use the Nusantara language model in your research or project, please cite it as:
@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}
}