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Model: maywell/Synatra-Zephyr-7B-v0.01
Source: Original Platform

README.md

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+---
+language:
+- ko
+
+library_name: transformers
+pipeline_tag: text-generation
+license: cc-by-nc-4.0
+---
+
+# **This is VERY Ealry Model of Development!**
+
+이 모델은 Synatra-Zephyr-7B의 극초기 버전입니다.
+
+# **Synatra-Zephyr-7B-v0.01🐧**  
+![Synatra-Zephyr-7B-v0.01](./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? Contact me on Telegram **AlzarTakkarsen**
+
+# **License**
+
+This model is strictly [*non-commercial*](https://creativecommons.org/licenses/by-nc/4.0/) (**cc-by-nc-4.0**) use only.
+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-nc-4.0** license in any parent repository, and the non-commercial use statute remains, regardless of other models' licences. 
+The licence can be changed after new model released. If you are to use this model for commercial purpose, Contact me.
+
+# **Model Details**
+**Base Model**  
+[mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta)    
+
+**Trained On**  
+A100 80G * 4
+
+
+# **Model Benchmark**
+
+## 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-Zephyr-7B-v0.01")
+tokenizer = AutoTokenizer.from_pretrained("maywell/Synatra-Zephyr-7B-v0.01")
+
+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])
+```