Upload README.md

README.md

@@ -1,3 +1,63 @@
 ---
+language:
+- ko
+datasets: 
+- kyujinpy/KOR-gugugu-platypus-set
+library_name: transformers
+pipeline_tag: text-generation
 license: cc-by-nc-sa-4.0
 ---
+
+# **⭐My custom LLM 13B⭐**  
+
+## Model Details   
+**Model Developers**  
+- Kyujin Han (kyujinpy)  
+
+**Model Architecture**  
+- My custom LLM 13B is an auto-regressive language model based on the LLaMA2 transformer architecture.  
+
+**Base Model**   
+- [beomi/llama-2-koen-13b](https://huggingface.co/beomi/llama-2-koen-13b)   
+
+**Training Dataset**   
+- [kyujinpy/ko-gu-platyorca-mergeset](https://huggingface.co/datasets/kyujinpy/ko-gu-platyorca-mergeset).  
+
+---  
+# Model comparisons
+> Ko-LLM leaderboard(11/27; [link](https://huggingface.co/spaces/upstage/open-ko-llm-leaderboard))
+   
+| Model | Average | Ko-ARC | Ko-HellaSwag | Ko-MMLU | Ko-TruthfulQA | Ko-CommonGen V2 |
+| --- | --- | --- | --- | --- | --- | --- |
+| ⭐My custom LLM 13B-v1⭐ | **50.19** | **45.99** | 56.93 | 41.78 | 41.66 | **64.58** | 
+| ⭐My custom LLM 13B-v4⭐ | 49.89 | 45.05 | **57.06** | 41.83 | **42.93** | 62.57 | 
+| **⭐My custom LLM 13B-v6⭐** | NaN | NaN | NaN | NaN | NaN | NaN | 
+
+---  
+# Model comparisons2
+> AI-Harness evaluation; [link](https://github.com/Beomi/ko-lm-evaluation-harness)   
+   
+| Model | Copa | Copa | HellaSwag | HellaSwag | BoolQ | BoolQ | Sentineg | Sentineg |
+| --- | --- | --- | --- | --- | --- | --- | --- | --- |
+|  | 0-shot | 5-shot | 0-shot | 5-shot | 0-shot | 5-shot | 0-shot | 5-shot |
+| ⭐My custom LLM 13B-v1⭐ | 0.7987 | 0.8269 | 0.4994 | 0.5660 | 0.3343 | 0.5060 | 0.6984 | 0.9723 |
+| ⭐My custom LLM 13B-v4⭐** | 0.7988 | 0.8279 | 0.4995 | 0.4953 | 0.3343 | 0.3558 | **0.7825** | 0.9698 |
+| **⭐My custom LLM 13B-v6⭐** | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
+| [beomi/llama-2-koen-13b](https://huggingface.co/beomi/llama-2-koen-13b) | 0.7768 | 0.8128 | 0.4999 | 0.5127 | 0.3988 | 0.7038 | 0.5870 | 0.9748 |
+   
+--- 
+# Implementation Code
+```python
+### KO-Platypus
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+repo = "PracticeLLM/Custom-KoLLM-13B-v6"
+OpenOrca = AutoModelForCausalLM.from_pretrained(
+        repo,
+        return_dict=True,
+        torch_dtype=torch.float16,
+        device_map='auto'
+)
+OpenOrca_tokenizer = AutoTokenizer.from_pretrained(repo)
+```