Upload README.md

README.md

@@ -1,3 +1,63 @@
 ---
+language:
+- ko
+datasets:
+- kyujinpy/KOpen-platypus
+library_name: transformers
+pipeline_tag: text-generation
 license: cc-by-nc-sa-4.0
 ---
+
+# **Kosy🍵llama**   
+![img](./Koisy_llama.JPG)  
+
+## Model Details
+
+**Model Developers** Kyujin Han (kyujinpy)
+
+**Model Description**  
+[NEFTune](https://github.com/neelsjain/NEFTune) method를 활용하여 훈련한 Ko-platypus2 new version!  
+(Noisy + KO + llama = Kosy🍵llama)
+
+**Repo Link**  
+Github **KoNEFTune**(not public; wait!): [Kosy🍵llama](https://github.com/Marker-Inc-Korea/KoNEFTune)  
+If you visit our github, you can easily apply **Random_noisy_embedding_fine-tuning**!!
+
+**Base Model**   
+[hyunseoki/ko-en-llama2-13b](https://huggingface.co/hyunseoki/ko-en-llama2-13b)   
+
+**Training Dataset**  
+Version of combined dataset: [kyujinpy/KOpen-platypus](https://huggingface.co/datasets/kyujinpy/KOpen-platypus)  
+I use A100 GPU 40GB and COLAB, when trianing.  
+
+# **Model comparisons**
+[KO-LLM leaderboard](https://huggingface.co/spaces/upstage/open-ko-llm-leaderboard)  
+# **NEFT comparisons**
+![img](./comparison.png)  
+| Model | Average | Ko-ARC | Ko-HellaSwag | Ko-MMLU | Ko-TruthfulQA | Ko-CommonGen V2 |
+| --- | --- | --- | --- | --- | --- | --- |
+| [Ko-Platypus2-13B](https://huggingface.co/kyujinpy/KO-Platypus2-13B) | 45.60 | 44.20 | 54.31 | 42.47 | 44.41 | 42.62 |
+| *NEFT(🍵kosy)+MLP-v1 | 43.64 | 43.94 | 53.88 | 42.68 | 43.46 | 34.24 |   
+| *NEFT(🍵kosy)+MLP-v2 | 45.45 | 44.20 | 54.56 | 42.60 | 42.68 | 42.98 |   
+| ***NEFT(🍵kosy)+MLP-v3** | 46.31 | 43.34 | 54.54 | 43.38 | 44.11 | 46.16 |  
+| NEFT(🍵kosy)+Attention | 44.92 |42.92 | 54.48 | 42.99 | 43.00 | 41.20 |
+| NEFT(🍵kosy) | 45.08 | 43.09 | 53.61 | 41.06 | 43.47 | 43.21 |  
+> *Different Hyperparameters such that learning_rate, batch_size, epoch, etc...  
+  
+# Implementation Code
+```python
+### KO-Platypus
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+repo = "kyujinpy/Koisy-Platypus2-13B"
+OpenOrca = AutoModelForCausalLM.from_pretrained(
+        repo,
+        return_dict=True,
+        torch_dtype=torch.float16,
+        device_map='auto'
+)
+OpenOrca_tokenizer = AutoTokenizer.from_pretrained(repo)
+```
+
+---