Create README.md

README.md

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+---
+license: cc-by-nc-sa-4.0
+datasets:
+- squarelike/sharegpt_deepl_ko_translation
+language:
+- ko
+pipeline_tag: translation
+tags:
+- translate
+---
+# **Seagull-13b-translation 📇**
+![Seagull-typewriter](./Seagull-typewriter.png)
+**Seagull-13b-translation** is yet another translator model, but carefully considered the following issues with 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). 또한 가끔 문장 반복 현상이 생길 수 있으니, 후처리 단계에서 테스트하는 것을 추천합니다.
+
+> 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](https://allganize.career.greetinghr.com/o/65146).
+For a coffee chat or if you have any questions, please do not hesitate to contact me as well! - [email protected]
+
+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](https://huggingface.co/beomi/llama-2-koen-13b)
+#### **Datasets**
+- [sharegpt_deepl_ko_translation](https://huggingface.co/datasets/squarelike/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.
+
+```python
+<|im_start|>system
+주어진 문장을 한국어로 번역하세요.<|im_end|>
+<|im_start|>user
+{instruction}<|im_end|>
+<|im_start|>assistant
+
+```
+```python
+<|im_start|>system
+주어진 문장을 영어로 번역하세요.<|im_end|>
+<|im_start|>user
+{instruction}<|im_end|>
+<|im_start|>assistant
+
+```
+
+#### Example code
+Since, chat_template already contains insturction format above.
+You can use the code below.
+```python
+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": "user", "content": "바나나는 원래 하얀색이야?"},
+]
+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])
+```