Update README.md
Browse files
README.md
CHANGED
@@ -1,201 +1,109 @@
|
|
1 |
---
|
2 |
library_name: transformers
|
3 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
4 |
---
|
5 |
|
6 |
-
# Model Card for Model ID
|
7 |
|
8 |
-
|
9 |
|
10 |
|
11 |
|
12 |
-
## Model Details
|
13 |
-
|
14 |
### Model Description
|
15 |
|
16 |
-
|
17 |
-
|
18 |
-
|
19 |
-
|
20 |
-
- **Developed by:** [More Information Needed]
|
21 |
-
- **Funded by [optional]:** [More Information Needed]
|
22 |
-
- **Shared by [optional]:** [More Information Needed]
|
23 |
-
- **Model type:** [More Information Needed]
|
24 |
-
- **Language(s) (NLP):** [More Information Needed]
|
25 |
-
- **License:** [More Information Needed]
|
26 |
-
- **Finetuned from model [optional]:** [More Information Needed]
|
27 |
-
|
28 |
-
### Model Sources [optional]
|
29 |
-
|
30 |
-
<!-- Provide the basic links for the model. -->
|
31 |
-
|
32 |
-
- **Repository:** [More Information Needed]
|
33 |
-
- **Paper [optional]:** [More Information Needed]
|
34 |
-
- **Demo [optional]:** [More Information Needed]
|
35 |
-
|
36 |
-
## Uses
|
37 |
-
|
38 |
-
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
|
39 |
-
|
40 |
-
### Direct Use
|
41 |
-
|
42 |
-
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
|
43 |
-
|
44 |
-
[More Information Needed]
|
45 |
|
46 |
-
### Downstream Use [optional]
|
47 |
|
48 |
-
|
|
|
|
|
|
|
|
|
|
|
49 |
|
50 |
-
|
51 |
-
|
52 |
-
### Out-of-Scope Use
|
53 |
-
|
54 |
-
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
|
55 |
-
|
56 |
-
[More Information Needed]
|
57 |
-
|
58 |
-
## Bias, Risks, and Limitations
|
59 |
-
|
60 |
-
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
|
61 |
-
|
62 |
-
[More Information Needed]
|
63 |
-
|
64 |
-
### Recommendations
|
65 |
|
66 |
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
|
67 |
|
68 |
-
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model.
|
69 |
-
|
70 |
-
## How to Get Started with the Model
|
71 |
-
|
72 |
-
Use the code below to get started with the model.
|
73 |
-
|
74 |
-
[More Information Needed]
|
75 |
-
|
76 |
-
## Training Details
|
77 |
-
|
78 |
-
### Training Data
|
79 |
-
|
80 |
-
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
|
81 |
-
|
82 |
-
[More Information Needed]
|
83 |
-
|
84 |
-
### Training Procedure
|
85 |
-
|
86 |
-
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
|
87 |
-
|
88 |
-
#### Preprocessing [optional]
|
89 |
-
|
90 |
-
[More Information Needed]
|
91 |
-
|
92 |
-
|
93 |
-
#### Training Hyperparameters
|
94 |
-
|
95 |
-
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
|
96 |
-
|
97 |
-
#### Speeds, Sizes, Times [optional]
|
98 |
-
|
99 |
-
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
|
100 |
-
|
101 |
-
[More Information Needed]
|
102 |
-
|
103 |
-
## Evaluation
|
104 |
-
|
105 |
-
<!-- This section describes the evaluation protocols and provides the results. -->
|
106 |
-
|
107 |
-
### Testing Data, Factors & Metrics
|
108 |
-
|
109 |
-
#### Testing Data
|
110 |
-
|
111 |
-
<!-- This should link to a Dataset Card if possible. -->
|
112 |
-
|
113 |
-
[More Information Needed]
|
114 |
|
115 |
-
#### Factors
|
116 |
-
|
117 |
-
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
|
118 |
-
|
119 |
-
[More Information Needed]
|
120 |
-
|
121 |
-
#### Metrics
|
122 |
-
|
123 |
-
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
|
124 |
-
|
125 |
-
[More Information Needed]
|
126 |
-
|
127 |
-
### Results
|
128 |
-
|
129 |
-
[More Information Needed]
|
130 |
-
|
131 |
-
#### Summary
|
132 |
-
|
133 |
-
|
134 |
-
|
135 |
-
## Model Examination [optional]
|
136 |
-
|
137 |
-
<!-- Relevant interpretability work for the model goes here -->
|
138 |
-
|
139 |
-
[More Information Needed]
|
140 |
-
|
141 |
-
## Environmental Impact
|
142 |
-
|
143 |
-
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
|
144 |
-
|
145 |
-
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
|
146 |
-
|
147 |
-
- **Hardware Type:** [More Information Needed]
|
148 |
-
- **Hours used:** [More Information Needed]
|
149 |
-
- **Cloud Provider:** [More Information Needed]
|
150 |
-
- **Compute Region:** [More Information Needed]
|
151 |
-
- **Carbon Emitted:** [More Information Needed]
|
152 |
-
|
153 |
-
## Technical Specifications [optional]
|
154 |
-
|
155 |
-
### Model Architecture and Objective
|
156 |
-
|
157 |
-
[More Information Needed]
|
158 |
-
|
159 |
-
### Compute Infrastructure
|
160 |
-
|
161 |
-
[More Information Needed]
|
162 |
-
|
163 |
-
#### Hardware
|
164 |
-
|
165 |
-
[More Information Needed]
|
166 |
-
|
167 |
-
#### Software
|
168 |
-
|
169 |
-
[More Information Needed]
|
170 |
-
|
171 |
-
## Citation [optional]
|
172 |
-
|
173 |
-
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
|
174 |
-
|
175 |
-
**BibTeX:**
|
176 |
-
|
177 |
-
[More Information Needed]
|
178 |
-
|
179 |
-
**APA:**
|
180 |
-
|
181 |
-
[More Information Needed]
|
182 |
-
|
183 |
-
## Glossary [optional]
|
184 |
-
|
185 |
-
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
|
186 |
-
|
187 |
-
[More Information Needed]
|
188 |
-
|
189 |
-
## More Information [optional]
|
190 |
-
|
191 |
-
[More Information Needed]
|
192 |
-
|
193 |
-
## Model Card Authors [optional]
|
194 |
-
|
195 |
-
[More Information Needed]
|
196 |
-
|
197 |
-
## Model Card Contact
|
198 |
-
|
199 |
-
[More Information Needed]
|
200 |
|
|
|
201 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
---
|
2 |
library_name: transformers
|
3 |
+
widget:
|
4 |
+
- text: Siapakah Tan Malaka?
|
5 |
+
example_title: Tokoh
|
6 |
+
- text: Berikan saya resep memasak nasi goreng yang lezat.
|
7 |
+
example_title: Resep
|
8 |
+
- text: Bagaimana solusi untuk mengobati jerawat di wajah?
|
9 |
+
example_title: Solusi
|
10 |
+
pipeline_tag: text-generation
|
11 |
+
tags:
|
12 |
+
- conversational
|
13 |
+
- convAI
|
14 |
+
license: apache-2.0
|
15 |
+
language:
|
16 |
+
- id
|
17 |
+
- en
|
18 |
+
datasets:
|
19 |
+
- argilla/OpenHermes2.5-dpo-binarized-alpha
|
20 |
+
- wikimedia/wikipedia
|
21 |
+
- FreedomIntelligence/evol-instruct-indonesian
|
22 |
---
|
23 |
|
|
|
24 |
|
25 |
+
![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/642b04e4ecec03b44649e318/6CCm81lqJ-i7aB38MtrAY.jpeg)
|
26 |
|
27 |
|
28 |
|
|
|
|
|
29 |
### Model Description
|
30 |
|
31 |
+
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.
|
32 |
+
As Chat-implemented language model, Nusantara is capable to do Question-Answering and respond to instructions given in Bahasa Indonesia.
|
33 |
+
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.
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
34 |
|
|
|
35 |
|
36 |
+
- **Finetuned by:** [Kalis AI](https://huggingface.co/kalisai)
|
37 |
+
- **Funded by:** Self-funded
|
38 |
+
- **Model type:** transformer-based decoder-only language model
|
39 |
+
- **Language(s):** Bahasa Indonesia (id), English (en)
|
40 |
+
- **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)
|
41 |
+
- **Finetuned from model:** [Qwen1.5-4B](https://huggingface.co/Qwen/Qwen1.5-4B/tree/main)
|
42 |
|
43 |
+
### Attentions!
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
44 |
|
45 |
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
|
46 |
|
47 |
+
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model.
|
48 |
+
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.
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
49 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
50 |
|
51 |
+
## How to Get Started with the Model
|
52 |
|
53 |
+
Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents.
|
54 |
+
|
55 |
+
```python
|
56 |
+
from transformers import AutoModelForCausalLM, AutoTokenizer
|
57 |
+
device = "cuda" # the device to load the model onto
|
58 |
+
|
59 |
+
model = AutoModelForCausalLM.from_pretrained(
|
60 |
+
"kalisai/Nusantara-7B-Indo-Chat",
|
61 |
+
torch_dtype="auto",
|
62 |
+
device_map="auto"
|
63 |
+
)
|
64 |
+
tokenizer = AutoTokenizer.from_pretrained("kalisai/Nusantara-7B-Indo-Chat")
|
65 |
+
|
66 |
+
prompt = "Berikan saya resep memasak nasi goreng yang lezat."
|
67 |
+
messages = [
|
68 |
+
{"role": "system", "content": "Kamu adalah Nusantara, asisten AI yang pintar."},
|
69 |
+
{"role": "user", "content": prompt}
|
70 |
+
]
|
71 |
+
text = tokenizer.apply_chat_template(
|
72 |
+
messages,
|
73 |
+
tokenize=False,
|
74 |
+
add_generation_prompt=True
|
75 |
+
)
|
76 |
+
model_inputs = tokenizer([text], return_tensors="pt").to(device)
|
77 |
+
|
78 |
+
generated_ids = model.generate(
|
79 |
+
model_inputs.input_ids,
|
80 |
+
max_new_tokens=512
|
81 |
+
)
|
82 |
+
generated_ids = [
|
83 |
+
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
|
84 |
+
]
|
85 |
+
|
86 |
+
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
|
87 |
+
```
|
88 |
+
|
89 |
+
|
90 |
+
## Citation
|
91 |
+
|
92 |
+
If you use the Nusantara language model in your research or project, please cite it as:
|
93 |
+
```
|
94 |
+
@article{Nusantara,
|
95 |
+
title={Nusantara: An Open Weight Language Model of Bahasa Indonesia},
|
96 |
+
author={Zulfikar Aji Kusworo},
|
97 |
+
publisher={Hugging Face}
|
98 |
+
journal={Hugging Face Repository},
|
99 |
+
year={2024}
|
100 |
+
}
|
101 |
+
```
|
102 |
+
```
|
103 |
+
@article{qwen,
|
104 |
+
title={Qwen Technical Report},
|
105 |
+
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},
|
106 |
+
journal={arXiv preprint arXiv:2309.16609},
|
107 |
+
year={2023}
|
108 |
+
}
|
109 |
+
```
|