kalisai
/

Nusantara-2.7b-Indo-Chat

@@ -1,201 +1,109 @@
 ---
 library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
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-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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 ---
 library_name: transformers
+widget:
+- text: Siapakah Thomas Alva Edison?
+  example_title: Tokoh
+- text: Berikan saya resep memasak nasi goreng yang lezat.
+  example_title: Resep Memasak
+- 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
 ---
+![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/642b04e4ecec03b44649e318/6CCm81lqJ-i7aB38MtrAY.jpeg)
 ### 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 0.5B, 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 [Zulfikar Aji Kusworo](https://huggingface.co/gmonsoon).
+- **Finetuned by:** [Kalis AI](https://huggingface.co/kalisai) / [Zulfikar Aji Kusworo](https://huggingface.co/gmonsoon)
+- **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](https://huggingface.co/Qwen/Qwen1.5-4B/blob/main/LICENSE)
+- **Finetuned from model:** [Qwen1.5-4B](https://huggingface.co/Qwen/Qwen1.5-4B/tree/main)
+### Attentions!
 <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+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(
+    "gmonsoon/Nusantaro-2.7B-Base",
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained("gmonsoon/Nusantaro-2.7B-Base")
+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}
+}
+```