README.md

---
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)
```