---
language:
- en
license: cc-by-nc-sa-4.0
library_name: transformers
tags:
- moe
- merge
- MoE
pipeline_tag: text-generation
model-index:
- name: SOLARC-MOE-10.7Bx6
  results:
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: AI2 Reasoning Challenge (25-Shot)
      type: ai2_arc
      config: ARC-Challenge
      split: test
      args:
        num_few_shot: 25
    metrics:
    - type: acc_norm
      value: 70.9
      name: normalized accuracy
    source:
      url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=DopeorNope/SOLARC-MOE-10.7Bx6
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: HellaSwag (10-Shot)
      type: hellaswag
      split: validation
      args:
        num_few_shot: 10
    metrics:
    - type: acc_norm
      value: 88.4
      name: normalized accuracy
    source:
      url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=DopeorNope/SOLARC-MOE-10.7Bx6
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MMLU (5-Shot)
      type: cais/mmlu
      config: all
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 66.36
      name: accuracy
    source:
      url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=DopeorNope/SOLARC-MOE-10.7Bx6
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: TruthfulQA (0-shot)
      type: truthful_qa
      config: multiple_choice
      split: validation
      args:
        num_few_shot: 0
    metrics:
    - type: mc2
      value: 71.85
    source:
      url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=DopeorNope/SOLARC-MOE-10.7Bx6
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: Winogrande (5-shot)
      type: winogrande
      config: winogrande_xl
      split: validation
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 83.66
      name: accuracy
    source:
      url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=DopeorNope/SOLARC-MOE-10.7Bx6
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: GSM8k (5-shot)
      type: gsm8k
      config: main
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 64.9
      name: accuracy
    source:
      url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=DopeorNope/SOLARC-MOE-10.7Bx6
      name: Open LLM Leaderboard
---
**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 to compile 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.float32,
        device_map='auto'
)
OpenOrca_tokenizer = AutoTokenizer.from_pretrained(repo)
```


---
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co./datasets/open-llm-leaderboard/details_DopeorNope__SOLARC-MOE-10.7Bx6)

|             Metric              |Value|
|---------------------------------|----:|
|Avg.                             |74.35|
|AI2 Reasoning Challenge (25-Shot)|70.90|
|HellaSwag (10-Shot)              |88.40|
|MMLU (5-Shot)                    |66.36|
|TruthfulQA (0-shot)              |71.85|
|Winogrande (5-shot)              |83.66|
|GSM8k (5-shot)                   |64.90|