初始化项目，由ModelHub XC社区提供模型

Model: maywell/koOpenChat-sft
Source: Original Platform

README.md

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+---
+license: cc-by-sa-4.0
+---
+
+# **koOpenChat-sft🐧**  
+
+## 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**
+
+
+# **Model Details**
+**Base Model**  
+OpenChat3.5
+
+**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.
+
+# **Model Benchmark**
+None
+
+# **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/koOpenChat-sft")
+tokenizer = AutoTokenizer.from_pretrained("maywell/koOpenChat-sft")
+
+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])
+```
+# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
+Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_maywell__koOpenChat-sft)
+
+| Metric                | Value                     |
+|-----------------------|---------------------------|
+| Avg.                  | 51.36   |
+| ARC (25-shot)         | 59.81          |
+| HellaSwag (10-shot)   | 78.73    |
+| MMLU (5-shot)         | 61.32         |
+| TruthfulQA (0-shot)   | 51.24   |
+| Winogrande (5-shot)   | 76.4   |
+| GSM8K (5-shot)        | 24.18        |
+| DROP (3-shot)         | 7.82         |