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kalisai_-_Nusantara-2.7b-Indo-Chat-gguf

Name: kalisai_-_Nusantara-2.7b-Indo-Chat-gguf
Availability: InStock

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Nusantara-2.7b-Indo-Chat - GGUF

Model creator: https://huggingface.co/kalisai/
Original model: https://huggingface.co/kalisai/Nusantara-2.7b-Indo-Chat/

Name	Quant method	Size
Nusantara-2.7b-Indo-Chat.Q2_K.gguf	Q2_K	1.09GB
Nusantara-2.7b-Indo-Chat.IQ3_XS.gguf	IQ3_XS	1.19GB
Nusantara-2.7b-Indo-Chat.IQ3_S.gguf	IQ3_S	1.23GB
Nusantara-2.7b-Indo-Chat.Q3_K_S.gguf	Q3_K_S	1.23GB
Nusantara-2.7b-Indo-Chat.IQ3_M.gguf	IQ3_M	1.28GB
Nusantara-2.7b-Indo-Chat.Q3_K.gguf	Q3_K	1.33GB
Nusantara-2.7b-Indo-Chat.Q3_K_M.gguf	Q3_K_M	1.33GB
Nusantara-2.7b-Indo-Chat.Q3_K_L.gguf	Q3_K_L	1.39GB
Nusantara-2.7b-Indo-Chat.IQ4_XS.gguf	IQ4_XS	1.42GB
Nusantara-2.7b-Indo-Chat.Q4_0.gguf	Q4_0	1.47GB
Nusantara-2.7b-Indo-Chat.IQ4_NL.gguf	IQ4_NL	1.48GB
Nusantara-2.7b-Indo-Chat.Q4_K_S.gguf	Q4_K_S	1.53GB
Nusantara-2.7b-Indo-Chat.Q4_K.gguf	Q4_K	1.62GB
Nusantara-2.7b-Indo-Chat.Q4_K_M.gguf	Q4_K_M	1.62GB
Nusantara-2.7b-Indo-Chat.Q4_1.gguf	Q4_1	1.6GB
Nusantara-2.7b-Indo-Chat.Q5_0.gguf	Q5_0	1.74GB
Nusantara-2.7b-Indo-Chat.Q5_K_S.gguf	Q5_K_S	1.77GB
Nusantara-2.7b-Indo-Chat.Q5_K.gguf	Q5_K	1.84GB
Nusantara-2.7b-Indo-Chat.Q5_K_M.gguf	Q5_K_M	1.84GB
Nusantara-2.7b-Indo-Chat.Q5_1.gguf	Q5_1	1.88GB
Nusantara-2.7b-Indo-Chat.Q6_K.gguf	Q6_K	2.13GB
Nusantara-2.7b-Indo-Chat.Q8_0.gguf	Q8_0	2.63GB

Original model description:

library_name: transformers widget:

text: Siapa karakter utama dalam novel Harry Potter? 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!

Due to certain circumstances, models with <4B parameters tend to hallucinate easily. 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.

python

from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto

model = AutoModelForCausalLM.from_pretrained(
    "kalisai/Nusantara-2.7B-Indo-Chat",
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("kalisai/Nusantara-2.7B-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:

@article{Nusantara,
  title={Nusantara: An Open Weight Language Model of Bahasa Indonesia},
  author={Zulfikar Aji Kusworo},
  publisher={Hugging Face}
  journal={Hugging Face Repository},
  year={2024}
}

@article{qwen,
  title={Qwen Technical Report},
  author={Jinze Bai and Shuai Bai and Yunfei Chu and Zeyu Cui and Kai Dang and Xiaodong Deng and Yang Fan and Wenbin Ge and Yu Han and Fei Huang and Binyuan Hui and Luo Ji and Mei Li and Junyang Lin and Runji Lin and Dayiheng Liu and Gao Liu and Chengqiang Lu and Keming Lu and Jianxin Ma and Rui Men and Xingzhang Ren and Xuancheng Ren and Chuanqi Tan and Sinan Tan and Jianhong Tu and Peng Wang and Shijie Wang and Wei Wang and Shengguang Wu and Benfeng Xu and Jin Xu and An Yang and Hao Yang and Jian Yang and Shusheng Yang and Yang Yao and Bowen Yu and Hongyi Yuan and Zheng Yuan and Jianwei Zhang and Xingxuan Zhang and Yichang Zhang and Zhenru Zhang and Chang Zhou and Jingren Zhou and Xiaohuan Zhou and Tianhang Zhu},
  journal={arXiv preprint arXiv:2309.16609},
  year={2023}
}

RichardErkhov/kalisai_-_Nusantara-2.7b-Indo-Chat-gguf