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README.md

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 license: cc-by-sa-4.0
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+
+# **Synatra-7B-v0.3-Translation🐧**  
+![Synatra-7B-v0.3-Translation](./Synatra.png)
+
+## Support Me
+시나트라는 개인 프로젝트로, 1인의 자원으로 개발되고 있습니다. 모델이 마음에 드셨다면 약간의 연구비 지원은 어떨까요?
+[<img src="https://cdn.buymeacoffee.com/buttons/default-orange.png" alt="Buy me a Coffee" width="217" height="50">](https://www.buymeacoffee.com/mwell)
+
+Wanna be a sponser? (Please) Contact me on Telegram **AlzarTakkarsen**
+
+# **License**
+
+This model is strictly [*non-commercial*](https://creativecommons.org/licenses/by-sa/4.0/) (**cc-by-sa-4.0**) use, Under **5K MAU**
+The "Model" is completely free (ie. base model, derivates, merges/mixes) to use for non-commercial purposes as long as the the included **cc-by-sa-4.0** license in any parent repository, and the non-commercial use statute remains, regardless of other models' licences. 
+If your service has over **5K MAU** contact me for license approval.
+
+# **Model Details**
+**Base Model**  
+[mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1)  
+
+**Trained On**  
+A100 80GB * 1
+
+**Instruction format**
+
+It follows [ChatML](https://github.com/openai/openai-python/blob/main/chatml.md) format and **Alpaca(No-Input)** format.
+
+```python
+<|im_start|>system
+주어진 문장을 한국어로 번역해라.<|im_end|>
+<|im_start|>user
+{instruction}<|im_end|>
+<|im_start|>assistant
+
+```
+
+## Ko-LLM-Leaderboard
+
+On Benchmarking...
+
+# **Implementation 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("maywell/Synatra-7B-v0.3-Translation")
+tokenizer = AutoTokenizer.from_pretrained("maywell/Synatra-7B-v0.3-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])
+```