Create README.md

README.md

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+---
+language:
+- ko
+datasets:
+- DopeorNope/DPO-Ko-Dataset
+- DopeorNope/Orca_Near_Dedup-v2
+library_name: transformers
+pipeline_tag: text-generation
+license: cc-by-nc-sa-4.0
+---
+
+**(주)미디어그룹사람과숲과 (주)마커의 LLM 연구 컨소시엄에서 개발된 모델입니다**  
+**The license is `cc-by-nc-sa-4.0`.**  
+  
+# **🐻‍❄️COKAL-DPO_13b-v2🐻‍❄️**  
+![img](./COKAL-DPO_bear.png)  
+
+## Model Details
+
+**Model Developers** Seungyoo Lee (DopeorNope)
+
+**Input** Models input text only.
+
+**Output** Models generate text only.
+
+**Model Architecture**  
+COKAL-DPO_test-v2 is an auto-regressive 13B language model based on the LLaMA2 transformer architecture.
+
+**Base Model**  [DopeorNope/COKAL_pre_DPO_Test_v1-13b](https://huggingface.co/DopeorNope/COKAL_pre_DPO_Test_v1-13b)   
+
+DopeorNope/COKAL_pre_DPO_Test_v2-13b is the SFT model to train with DPO methodology.
+
+**Training Dataset**  
+- DPO training dataset: [DopeorNope/DPO-Ko-Dataset](private) - private
+
+This dataset was constructed by directly collecting and reorganizing data by DopeorNope, obtaining insights from ["lvwerra/stack-exchange-paired"](https://huggingface.co/datasets/lvwerra/stack-exchange-paired) to create a paired dataset. (It means I do not use stack-exchange-paired; I just got an insight from it.)
+
+- SFT training dataset: [DopeorNope/Orca_Near_Dedup-v2](private) - private
+
+This dataset is based on ["kyujinpy/OpenOrca-KO"](https://huggingface.co/datasets/kyujinpy/OpenOrca-KO) and has been processed using the Near Dedup algorithm to remove items with a Jaccard Similarity threshold of 0.8 or higher. In addition, inconsistent inputs have been cleaned and modified.
+  
+**Training**  
+The difference between "DopeorNope/COKAL-DPO_test-v2" and this model is that this model has different hyperparameters from the one in that setting when it comes to the final version.
+
+I developed the model in an environment with four RTX 3090 GPUs running Ubuntu 18.04. 
+
+It seems that when uploading the model directly to a repository from a Linux server, there may be an issue causing the model to appear to have more parameters. However, this model is based on a 13B architecture.
+
+
+**Reference papers**
+
+- Data Strategy:
+  - [LIMA(Zhou et al., 2023)](https://arxiv.org/abs/2305.11206)
+  - [Near Dedup algorithm(Lee et al., 2022)](https://arxiv.org/abs/2107.06499)
+
+- Model Architecture:
+  - [Llama2(Touvron et al., 2023)](https://arxiv.org/abs/2307.09288)
+
+
+# Implementation Code
+```python
+
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+repo = "HumanF-MarkrAI/COKAL-DPO-13b-v2"
+model = AutoModelForCausalLM.from_pretrained(
+        repo,
+        return_dict=True,
+        torch_dtype=torch.float16,
+        device_map='auto'
+)
+model_tokenizer = AutoTokenizer.from_pretrained(repo)
+```
+
+
+
+
+---