RichardErkhov/DopeorNope_-_SOLARC-MOE-10.7Bx4-gguf

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SOLARC-MOE-10.7Bx4 - GGUF

• —Model creator: https://huggingface.co/DopeorNope/
• —Original model: https://huggingface.co/DopeorNope/SOLARC-MOE-10.7Bx4/

Original model description: --- language:

• —ko libraryname: transformers pipelinetag: 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

Weyaxi/SauerkrautLM-UNA-SOLAR-Instruct

VAGOsolutions/SauerkrautLM-SOLAR-Instruct

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

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)