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README.md

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+Quantization made by Richard Erkhov.
+
+[Github](https://github.com/RichardErkhov)
+
+[Discord](https://discord.gg/pvy7H8DZMG)
+
+[Request more models](https://github.com/RichardErkhov/quant_request)
+
+
+Sailor-7B-Chat - GGUF
+- Model creator: https://huggingface.co/sail/
+- Original model: https://huggingface.co/sail/Sailor-7B-Chat/
+
+
+| Name | Quant method | Size |
+| ---- | ---- | ---- |
+| [Sailor-7B-Chat.Q2_K.gguf](https://huggingface.co/RichardErkhov/sail_-_Sailor-7B-Chat-gguf/blob/main/Sailor-7B-Chat.Q2_K.gguf) | Q2_K | 2.89GB |
+| [Sailor-7B-Chat.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/sail_-_Sailor-7B-Chat-gguf/blob/main/Sailor-7B-Chat.IQ3_XS.gguf) | IQ3_XS | 3.18GB |
+| [Sailor-7B-Chat.IQ3_S.gguf](https://huggingface.co/RichardErkhov/sail_-_Sailor-7B-Chat-gguf/blob/main/Sailor-7B-Chat.IQ3_S.gguf) | IQ3_S | 3.32GB |
+| [Sailor-7B-Chat.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/sail_-_Sailor-7B-Chat-gguf/blob/main/Sailor-7B-Chat.Q3_K_S.gguf) | Q3_K_S | 3.32GB |
+| [Sailor-7B-Chat.IQ3_M.gguf](https://huggingface.co/RichardErkhov/sail_-_Sailor-7B-Chat-gguf/blob/main/Sailor-7B-Chat.IQ3_M.gguf) | IQ3_M | 3.48GB |
+| [Sailor-7B-Chat.Q3_K.gguf](https://huggingface.co/RichardErkhov/sail_-_Sailor-7B-Chat-gguf/blob/main/Sailor-7B-Chat.Q3_K.gguf) | Q3_K | 3.65GB |
+| [Sailor-7B-Chat.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/sail_-_Sailor-7B-Chat-gguf/blob/main/Sailor-7B-Chat.Q3_K_M.gguf) | Q3_K_M | 3.65GB |
+| [Sailor-7B-Chat.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/sail_-_Sailor-7B-Chat-gguf/blob/main/Sailor-7B-Chat.Q3_K_L.gguf) | Q3_K_L | 3.93GB |
+| [Sailor-7B-Chat.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/sail_-_Sailor-7B-Chat-gguf/blob/main/Sailor-7B-Chat.IQ4_XS.gguf) | IQ4_XS | 4.02GB |
+| [Sailor-7B-Chat.Q4_0.gguf](https://huggingface.co/RichardErkhov/sail_-_Sailor-7B-Chat-gguf/blob/main/Sailor-7B-Chat.Q4_0.gguf) | Q4_0 | 4.2GB |
+| [Sailor-7B-Chat.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/sail_-_Sailor-7B-Chat-gguf/blob/main/Sailor-7B-Chat.IQ4_NL.gguf) | IQ4_NL | 4.22GB |
+| [Sailor-7B-Chat.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/sail_-_Sailor-7B-Chat-gguf/blob/main/Sailor-7B-Chat.Q4_K_S.gguf) | Q4_K_S | 4.23GB |
+| [Sailor-7B-Chat.Q4_K.gguf](https://huggingface.co/RichardErkhov/sail_-_Sailor-7B-Chat-gguf/blob/main/Sailor-7B-Chat.Q4_K.gguf) | Q4_K | 4.44GB |
+| [Sailor-7B-Chat.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/sail_-_Sailor-7B-Chat-gguf/blob/main/Sailor-7B-Chat.Q4_K_M.gguf) | Q4_K_M | 4.44GB |
+| [Sailor-7B-Chat.Q4_1.gguf](https://huggingface.co/RichardErkhov/sail_-_Sailor-7B-Chat-gguf/blob/main/Sailor-7B-Chat.Q4_1.gguf) | Q4_1 | 4.62GB |
+| [Sailor-7B-Chat.Q5_0.gguf](https://huggingface.co/RichardErkhov/sail_-_Sailor-7B-Chat-gguf/blob/main/Sailor-7B-Chat.Q5_0.gguf) | Q5_0 | 5.03GB |
+| [Sailor-7B-Chat.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/sail_-_Sailor-7B-Chat-gguf/blob/main/Sailor-7B-Chat.Q5_K_S.gguf) | Q5_K_S | 5.03GB |
+| [Sailor-7B-Chat.Q5_K.gguf](https://huggingface.co/RichardErkhov/sail_-_Sailor-7B-Chat-gguf/blob/main/Sailor-7B-Chat.Q5_K.gguf) | Q5_K | 5.15GB |
+| [Sailor-7B-Chat.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/sail_-_Sailor-7B-Chat-gguf/blob/main/Sailor-7B-Chat.Q5_K_M.gguf) | Q5_K_M | 5.15GB |
+| [Sailor-7B-Chat.Q5_1.gguf](https://huggingface.co/RichardErkhov/sail_-_Sailor-7B-Chat-gguf/blob/main/Sailor-7B-Chat.Q5_1.gguf) | Q5_1 | 5.44GB |
+| [Sailor-7B-Chat.Q6_K.gguf](https://huggingface.co/RichardErkhov/sail_-_Sailor-7B-Chat-gguf/blob/main/Sailor-7B-Chat.Q6_K.gguf) | Q6_K | 5.91GB |
+| [Sailor-7B-Chat.Q8_0.gguf](https://huggingface.co/RichardErkhov/sail_-_Sailor-7B-Chat-gguf/blob/main/Sailor-7B-Chat.Q8_0.gguf) | Q8_0 | 7.65GB |
+
+
+
+
+Original model description:
+---
+language:
+- en
+- zh
+- id
+- th
+- vi
+- ms
+- lo
+datasets:
+- CohereForAI/aya_dataset
+- CohereForAI/aya_collection
+- Open-Orca/OpenOrca
+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: sail/Sailor-7B
+---
+
+<div align="center">
+  <img src="banner_sailor.jpg" width="700"/>
+</div>
+
+Sailor is a suite of Open Language Models tailored for South-East Asia (SEA), focusing on languages such as 🇮🇩Indonesian, 🇹🇭Thai, 🇻🇳Vietnamese, 🇲🇾Malay, and 🇱🇦Lao. 
+Developed with careful data curation, Sailor models are designed to understand and generate text across diverse linguistic landscapes of SEA region. 
+Built from [Qwen 1.5](https://huggingface.co/collections/Qwen/qwen15-65c0a2f577b1ecb76d786524) , Sailor encompasses models of varying sizes, spanning from 0.5B to 7B versions for different requirements. 
+We further fine-tune the base model with open-source datasets to get instruction-tuned models, namedly Sailor-Chat. 
+Benchmarking results demonstrate Sailor's proficiency in tasks such as question answering, commonsense reasoning, and other tasks in SEA languages.
+
+> The logo was generated by MidJourney
+
+## Model Summary
+- **Model Collections:** [Base Model & Chat Model](https://huggingface.co/collections/sail/sailor-65e19a749f978976f1959825)
+- **Project Website:** [sailorllm.github.io](https://sailorllm.github.io/)
+- **Codebase:** [github.com/sail-sg/sailor-llm](https://github.com/sail-sg/sailor-llm)
+- **Technical Report:** [arxiv.org/pdf/2404.03608.pdf](https://arxiv.org/pdf/2404.03608.pdf) 
+
+
+## Training details
+Sailor is crafted by continually pre-training from language models like the remarkable Qwen 1.5 models, which already has a great performance on SEA languages. 
+The pre-training corpus heavily leverages the publicly available corpus, including 
+[SlimPajama](https://huggingface.co/datasets/cerebras/SlimPajama-627B), 
+[SkyPile](https://huggingface.co/datasets/Skywork/SkyPile-150B), 
+[CC100](https://huggingface.co/datasets/cc100) and [MADLAD-400](https://huggingface.co/datasets/allenai/MADLAD-400).
+The instruction tuning corpus are all publicly available including 
+[aya_collection](https://huggingface.co/datasets/CohereForAI/aya_collection), 
+[aya_dataset](https://huggingface.co/datasets/CohereForAI/aya_dataset), 
+[OpenOrca](https://huggingface.co/datasets/Open-Orca/OpenOrca).
+
+By employing aggressive data deduplication and careful data cleaning on the collected corpus, we have attained a high-quality dataset spanning various languages. 
+Through systematic experiments to determine the weights of different languages, Sailor models undergo training from 200B to 400B tokens, tailored to different model sizes. 
+The approach boosts their performance on SEA languages while maintaining proficiency in English and Chinese without significant compromise. 
+Finally, we continually pre-train the Qwen1.5-0.5B model with 400 Billion tokens, and other models with 200 Billion tokens to obtain the Sailor models.
+
+## Requirements
+The code of Sailor has been in the latest Hugging face transformers and we advise you to install `transformers>=4.37.0`.
+
+## 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"
+
+model = AutoModelForCausalLM.from_pretrained(
+    'sail/Sailor-7B-Chat',
+    torch_dtype="auto",
+    device_map="auto"
+)
+
+tokenizer = AutoTokenizer.from_pretrained('sail/Sailor-7B-Chat')
+system_prompt= 'You are a helpful assistant'
+
+prompt = "Beri saya pengenalan singkat tentang model bahasa besar."
+# prompt = "Hãy cho tôi một giới thiệu ngắn gọn về mô hình ngôn ngữ lớn."
+# prompt = "ให้ฉันแนะนำสั้น ๆ เกี่ยวกับโมเดลภาษาขนาดใหญ่"
+
+messages = [
+    {"role": "system", "content": system_prompt},
+    {"role": "question", "content": prompt}
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+
+model_inputs = tokenizer([text], return_tensors="pt").to(device)
+input_ids = model_inputs.input_ids.to(device)
+
+generated_ids = model.generate(
+    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]
+print(response)
+```
+
+# License
+
+Sailor is distributed under the terms of the Apache License 2.0. 
+No restrict on the research and the commercial use, but should comply with the [Qwen License](https://huggingface.co/Qwen/Qwen1.5-1.8B/blob/main/LICENSE).
+
+## Citation
+
+If you find sailor useful, please cite our work as follows:
+
+```
+@misc{dou2024sailor,
+      title={Sailor: Open Language Models for South-East Asia}, 
+      author={Longxu Dou and Qian Liu and Guangtao Zeng and Jia Guo and Jiahui Zhou and Wei Lu and Min Lin},
+      year={2024},
+      eprint={2404.03608},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```
+
+# 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.com](mailto:liuqian@sea.com).
+