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---
language:
- ko
library_name: transformers
pipeline_tag: text-generation
license: cc-by-nc-sa-4.0
tags:
- moe
- merge
---
**The license is `cc-by-nc-sa-4.0`.**
# **🐻❄️SOLARC-MOE-10.7Bx4🐻❄️**
## 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-10.7Bx4 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)
## **Implemented Method**
I have built a model using the Mixture of Experts (MOE) approach, utilizing each of these models as the base.
---
# Implementation Code
## Load model
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
repo = "DopeorNope/SOLARC-MOE-10.7Bx4"
OpenOrca = AutoModelForCausalLM.from_pretrained(
repo,
return_dict=True,
torch_dtype=torch.float16,
device_map='auto'
)
OpenOrca_tokenizer = AutoTokenizer.from_pretrained(repo)
```
--- |