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README.md

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+# SEA-LION-7B-Instruct-C
+
+SEA-LION is a collection of Large Language Models (LLMs) which has been pretrained and instruct-tuned for the Southeast Asia (SEA) region.
+The size of the models range from 3 billion to 7 billion parameters.
+This is the card for the SEA-LION 7B Instruct (Commercial) model.
+
+For more details on the base model, please refer to the [base model's model card](https://huggingface.co/aisingapore/sealion7b).
+
+SEA-LION stands for <i>Southeast Asian Languages In One Network</i>.
+
+
+## Model Details
+
+### Model Description
+
+The SEA-LION model is a significant leap forward in the field of Natural Language Processing,
+specifically trained to understand the SEA regional context.
+
+SEA-LION is built on the robust MPT architecture and has a vocabulary size of 256K.
+
+For tokenization, the model employs our custom SEABPETokenizer, which is specially tailored for SEA languages, ensuring optimal model performance.
+
+The pre-training data for the base SEA-LION model encompasses 980B tokens.
+The model was then further instruction-tuned on <b>commercially-permissive Indonesian data only</b>.
+
+- **Developed by:** Products Pillar, AI Singapore
+- **Funded by:** Singapore NRF
+- **Model type:** Decoder
+- **Languages:** English, Chinese, Indonesian, Malay, Thai, Vietnamese, Filipino, Tamil, Burmese, Khmer, Lao
+- **License:** CC BY-SA 4.0 License
+
+### Benchmark Performance
+
+Coming soon.
+
+## Technical Specifications
+
+### Model Architecture and Objective
+
+SEA-LION is a decoder model using the MPT architecture.
+
+| Parameter       | SEA-LION 7B |
+|-----------------|:-----------:|
+| Layers          | 32          |
+| d_model         | 4096        |
+| head_dim        | 32          |
+| Vocabulary      | 256000      |
+| Sequence Length | 2048        |
+
+
+### Tokenizer Details
+
+We sample 20M lines from the training data to train the tokenizer.<br>
+The framework for training is [SentencePiece](https://github.com/google/sentencepiece).<br>
+The tokenizer type is Byte-Pair Encoding (BPE).
+
+### Example Usage
+
+```python
+# Please use transformers==4.34.1
+
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+tokenizer = AutoTokenizer.from_pretrained("aisingapore/sealion7b-instruct-c", trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained("aisingapore/sealion7b-instruct-c", trust_remote_code=True)
+
+prompt_template = "### USER:\n{human_prompt}\n\n### RESPONSE:\n"
+prompt = """Apa sentimen dari kalimat berikut ini?
+Kalimat: Buku ini sangat membosankan.
+Jawaban: """
+full_prompt = prompt_template.format(human_prompt=prompt)
+
+tokens = tokenizer(full_prompt, return_tensors="pt")
+output = model.generate(tokens["input_ids"], max_new_tokens=20, eos_token_id=tokenizer.eos_token_id)
+print(tokenizer.decode(output[0], skip_special_tokens=True))
+
+```
+
+
+## The Team
+
+Lam Wen Zhi Clarence<br>
+Leong Wei Qi<br>
+Li Yier<br>
+Liu Bing Jie Darius<br>
+Lovenia Holy<br>
+Montalan Jann Railey<br>
+Ng Boon Cheong Raymond<br>
+Ngui Jian Gang<br>
+Nguyen Thanh Ngan<br>
+Ong Tat-Wee David<br>
+Rengarajan Hamsawardhini<br>
+Susanto Yosephine<br>
+Tai Ngee Chia<br>
+Tan Choon Meng<br>
+Teo Jin Howe<br>
+Teo Eng Sipp Leslie<br>
+Teo Wei Yi<br>
+Tjhi William<br>
+Yeo Yeow Tong<br>
+Yong Xianbin<br>
+
+## Acknowledgements
+
+AI Singapore is a national programme supported by the National Research Foundation, Singapore and hosted by the National University of Singapore.
+Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of National Research Foundation, Singapore.
+
+## Contact
+
+For more info, please contact us using this [SEA-LION Inquiry Form](https://forms.gle/sLCUVb95wmGf43hi6)
+
+[Link to SEA-LION's GitHub repository](https://github.com/aisingapore/sealion)
+
+## Disclaimer
+
+This the repository for the non-commercial instruction-tuned model.
+The model has _not_ been aligned for safety.
+Developers and users should perform their own safety fine-tuning and related security measures.
+In no event shall the authors be held liable for any claim, damages, or other liability
+arising from the use of the released weights and codes.