Create README.md

README.md

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+---
+language:
+- en
+- zh
+- id
+- th
+- vi
+- ms
+- lo
+- my
+- jv
+- km
+- su
+- tl
+tags:
+- multilingual
+- sea
+- sailor
+- sft
+- chat
+- instruction
+widget:
+- text: 如何制作烤鱼？
+  example_title: Chinese
+- text: How to bake fish?
+  example_title: English
+- text: Bagaimana cara memanggang ikan?
+  example_title: Malay
+- text: วิธีย่างปลา?
+  example_title: Thai
+- text: Bagaimana membuat bakaran ikan?
+  example_title: Indonesian
+- text: Làm thế nào để nướng cá?
+  example_title: Vietnamese
+license: apache-2.0
+base_model:
+- Qwen/Qwen2.5-7B
+---
+
+<div align="center">
+  <img src="sailor2_banner.jpg" width="700"/>
+</div>
+
+> The logo was generated by MidJourney
+
+Sailor2 is a community-driven initiative that brings cutting-edge multilingual language models to South-East Asia (SEA). 
+Our research highlights a strong demand for models in the **8B and 20B parameter** range for production use, alongside **1B models** for specialized applications, 
+such as speculative decoding and research purposes. 
+These models, released under the **Apache 2.0 license**, provide enhanced accessibility to advanced language technologies across the region.
+
+
+Sailor2 builds upon the foundation of the awesome multilingual model [Qwen 2.5](https://huggingface.co/collections/Qwen/qwen25-66e81a666513e518adb90d9e) and 
+is continuously pre-trained on **500B tokens** to support **15 languages** better with a unified model. 
+These languages include English, Chinese, Burmese, Cebuano, Ilocano, Indonesian, Javanese, Khmer, Lao, Malay, Sundanese, Tagalog, Thai, Vietnamese, and Waray. 
+By addressing the growing demand for diverse, robust, and accessible language models, 
+Sailor2 seeks to serve the underserved in SEA areas with open, inclusive, and accessible multilingual LLMs.
+
+Refer to [Sailor2 Website](https://sailorllm.github.io/) for more training details.
+
+## Model Summary
+- **Model Collections:** [Base Model & Chat Model](https://huggingface.co/collections/sail/sailor2-language-models-674d7c9e6b4dbbd9a869906b)
+- **Project Website:** [sailorllm.github.io](https://sailorllm.github.io/)
+- **Codebase:** [github.com/sail-sg/sailor2](https://github.com/sail-sg/sailor2)
+- **Technical Report:** Coming Soon
+
+
+## Training details
+
+
+## Requirements
+The code of Sailor2 has been in the latest Hugging face transformers and we advise you to install `transformers==4.46.3`.
+
+### Quickstart
+
+Here provides a code snippet 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
+
+model = AutoModelForCausalLM.from_pretrained("sail/Sailor2-8B", device_map="auto")
+tokenizer = AutoTokenizer.from_pretrained("sail/Sailor2-8B")
+
+input_message = "Model bahasa adalah model probabilistik" 
+### The given Indonesian input translates to 'A language model is a probabilistic model of.'
+
+model_inputs = tokenizer([input_message], return_tensors="pt").to(device)
+
+generated_ids = model.generate(
+    model_inputs.input_ids,
+    max_new_tokens=64
+)
+
+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]
+print(response)
+```
+
+# License
+
+Sailor2 is distributed under the terms of the Apache License 2.0. 
+No restrict on the research and the commercial use.
+
+## Citation
+
+If you find Sailor2 useful, please cite our work as follows:
+
+```
+@misc{sailor2report,
+  title={Sailor2: Sailing in South-East Asia with Inclusive Multilingual LLM},
+  author={Sailor2 Team},
+  year={2024}
+}
+```
+
+# Contact Us
+
+If you have any questions, please raise an issue or contact us at [doulx@sea.com](mailto:doulx@sea.com) or [liuqian.sea@gmail.com](mailto:liuqian.sea@gmail.com).