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README.md

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+---
+license: other
+language:
+- en
+pipeline_tag: text-generation
+inference: false
+tags:
+- transformers
+- gguf
+- imatrix
+- Sailor2-8B-Chat
+---
+Quantizations of https://huggingface.co/sail/Sailor2-8B-Chat
+
+### Inference Clients/UIs
+* [llama.cpp](https://github.com/ggerganov/llama.cpp)
+* [KoboldCPP](https://github.com/LostRuins/koboldcpp)
+* [ollama](https://github.com/ollama/ollama)
+* [jan](https://github.com/janhq/jan)
+* [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
+* [GPT4All](https://github.com/nomic-ai/gpt4all)
+---
+
+# From original readme
+
+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.
+The Sailor2 model comes in three sizes, 1B, 8B, and 20B, which are **expanded from the Qwen2.5 base models** of 0.5B, 7B, and 14B, respectively. 
+
+## Model Summary
+- **Model Collections:** [Base Model & Chat Model](https://huggingface.co/collections/sail/sailor2-language-models-674d7c9e6b4dbbd9a869906b)
+- **Project Website:** [sea-sailor.github.io/blog/sailor2/](https://sea-sailor.github.io/blog/sailor2/)
+- **Codebase:** [github.com/sail-sg/sailor2](https://github.com/sail-sg/sailor2)
+- **Technical Report:** Coming Soon
+
+
+## Training details
+
+During development, we employ a range of advanced technologies to ensure top-tier performance and efficiency:
+
+1. model expansion
+2. optimized data mixing strategies 
+3. multi-stage pre-training protocols 
+4. advanced multilingual post-training
+
+Please refer to [Sailor2 Blog](https://sea-sailor.github.io/blog/sailor2/) for more 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
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+device = "cuda"
+
+model = AutoModelForCausalLM.from_pretrained(
+    'sail/Sailor2-20B-Chat',
+    torch_dtype=torch.bfloat16,
+    device_map="auto"
+)
+
+tokenizer = AutoTokenizer.from_pretrained('sail/Sailor2-8B-Chat')
+system_prompt= \
+'You are an AI assistant named Sailor2, created by Sea AI Lab. \
+As an AI assistant, you can answer questions in English, Chinese, and Southeast Asian languages \
+such as Burmese, Cebuano, Ilocano, Indonesian, Javanese, Khmer, Lao, Malay, Sundanese, Tagalog, Thai, Vietnamese, and Waray. \
+Your responses should be friendly, unbiased, informative, detailed, and faithful.'
+
+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": "user", "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)
+```