README.md

metadata

license: cc-by-nc-sa-4.0
datasets:
  - squarelike/sharegpt_deepl_ko_translation
language:
  - ko
pipeline_tag: translation
tags:
  - translate
  - awq

Seagull-13b-translation-AWQ 📇

This is quantized version of original model: Seagull-13b-translation.

Seagull-13b-translation is yet another translator model, but carefully considered the following issues from existing translation models.

• newline or space not matching the original text
• Using translated dataset with first letter removed for training
• Codes
• Markdown format
• LaTeX format
• etc

이런 이슈들을 충분히 체크하고 학습을 진행하였지만, 모델을 사용할 때는 이런 부분에 대한 결과를 면밀하게 살펴보는 것을 추천합니다(코드가 포함된 텍스트 등).

If you're interested in building large-scale language models to solve a wide variety of problems in a wide variety of domains, you should consider joining Allganize. For a coffee chat or if you have any questions, please do not hesitate to contact me as well! - kuotient.dev@gmail.com

This model was created as a personal experiment, unrelated to the organization I work for.

License

From original model author:

• Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public License, under LLAMA 2 COMMUNITY LICENSE AGREEMENT
• Full License available at: https://huggingface.co/beomi/llama-2-koen-13b/blob/main/LICENSE

Model Details

Developed by

Jisoo Kim(kuotient)

Base Model

beomi/llama-2-koen-13b

Datasets

• sharegpt_deepl_ko_translation
• AIHUB
  • 기술과학 분야 한-영 번역 병렬 말뭉치 데이터
  • 일상생활 및 구어체 한-영 번역 병렬 말뭉치 데이터

Usage

Format

It follows only ChatML format.

<|im_start|>system
주어진 문장을 한국어로 번역하세요.<|im_end|>
<|im_start|>user
{instruction}<|im_end|>
<|im_start|>assistant
# Don't miss newline here

<|im_start|>system
주어진 문장을 영어로 번역하세요.<|im_end|>
<|im_start|>user
{instruction}<|im_end|>
<|im_start|>assistant
# Don't miss newline here

Example

I highly recommend to inference model with vllm. I will write a guide for quick and easy inference if requested.

Since, chat_template already contains insturction format above. You can use the code below.

from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained("kuotient/Seagull-13B-translation")
tokenizer = AutoTokenizer.from_pretrained("kuotient/Seagull-13B-translation")
messages = [
    {"role": "system", "content", "주어진 문장을 한국어로 번역하세요."}
    {"role": "user", "content": "Here are five examples of nutritious foods to serve your kids."},
]
encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt")

model_inputs = encodeds.to(device)
model.to(device)
generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True)
decoded = tokenizer.batch_decode(generated_ids)
print(decoded[0])