Create README.md

README.md

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+---
+language:
+- ko
+library_name: transformers
+pipeline_tag: text-generation
+license: cc-by-nc-sa-4.0
+---
+**The license is `cc-by-nc-sa-4.0`.**  
+  
+# **🐻‍❄️SOLARC-MOE-10.7Bx6🐻‍❄️**  
+![img](https://drive.google.com/uc?export=view&id=1_Qa2TfLMw3WeJ23dHkrP1Xln_RNt1jqG)  
+
+
+## Model Details
+
+**Model Developers** Seungyoo Lee(DopeorNope)
+
+I am in charge of Large Language Models (LLMs) at Markr AI team in South Korea.
+
+**Input** Models input text only.
+
+**Output** Models generate text only.
+
+**Model Architecture**  
+SOLARC-MOE-10.7Bx6 is an auto-regressive language model based on the SOLAR architecture.
+
+---
+
+## **Base Model**  
+
+[kyujinpy/Sakura-SOLAR-Instruct](https://huggingface.co/kyujinpy/Sakura-SOLAR-Instruct)   
+
+[Weyaxi/SauerkrautLM-UNA-SOLAR-Instruct](https://huggingface.co/Weyaxi/SauerkrautLM-UNA-SOLAR-Instruct)   
+
+[VAGOsolutions/SauerkrautLM-SOLAR-Instruct](https://huggingface.co/VAGOsolutions/SauerkrautLM-SOLAR-Instruct)   
+
+[fblgit/UNA-SOLAR-10.7B-Instruct-v1.0](https://huggingface.co/fblgit/UNA-SOLAR-10.7B-Instruct-v1.0)   
+
+[jeonsworld/CarbonVillain-en-10.7B-v1](https://huggingface.co/jeonsworld/CarbonVillain-en-10.7B-v1)
+
+
+## **Implemented Method**
+
+I have built a model using the Mixture of Experts (MOE) approach, utilizing each of these models as the base.
+
+I wanted to test if it was possible with a non-power of 2, like with 6
+
+---
+  
+# Implementation Code
+
+
+## Load model
+```python
+
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+repo = "DopeorNope/SOLARC-MOE-10.7Bx6"
+OpenOrca = AutoModelForCausalLM.from_pretrained(
+        repo,
+        return_dict=True,
+        torch_dtype=torch.float16,
+        device_map='auto'
+)
+OpenOrca_tokenizer = AutoTokenizer.from_pretrained(repo)
+```
+
+
+---