README.md

Quantization made by Richard Erkhov.

[Github](https://github.com/RichardErkhov)

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LogoS-7Bx2-MoE-13B-v0.2 - bnb 8bits
- Model creator: https://huggingface.co/RubielLabarta/
- Original model: https://huggingface.co/RubielLabarta/LogoS-7Bx2-MoE-13B-v0.2/




Original model description:
---
language:
- en
- es
license: apache-2.0
tags:
- moe
- merge
base_model:
- yunconglong/Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B
- TomGrc/FusionNet_7Bx2_MoE_14B
model-index:
- name: LogoS-7Bx2-MoE-13B-v0.1
  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: 74.49
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=RubielLabarta/LogoS-7Bx2-MoE-13B-v0.1
      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: 89.07
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=RubielLabarta/LogoS-7Bx2-MoE-13B-v0.1
      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: 64.74
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=RubielLabarta/LogoS-7Bx2-MoE-13B-v0.1
      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: 74.57
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=RubielLabarta/LogoS-7Bx2-MoE-13B-v0.1
      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: 88.32
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=RubielLabarta/LogoS-7Bx2-MoE-13B-v0.1
      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: 71.65
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=RubielLabarta/LogoS-7Bx2-MoE-13B-v0.1
      name: Open LLM Leaderboard
---

# LogoS-7Bx2-MoE-13B-v0.1

Model built by @RubielLabarta using SLERP merge method. The model is release for research purposes only, commercial use is not allowed.

The LogoS is a model to experiment with the MoE method, which could significantly increase the performance of the original model. The model has 12.9B parameters.
# [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_RubielLabarta__LogoS-7Bx2-MoE-13B-v0.1)

|             Metric              |Value|
|---------------------------------|----:|
|Avg.                             |77.14|
|AI2 Reasoning Challenge (25-Shot)|74.49|
|HellaSwag (10-Shot)              |89.07|
|MMLU (5-Shot)                    |64.74|
|TruthfulQA (0-shot)              |74.57|
|Winogrande (5-shot)              |88.32|
|GSM8k (5-shot)                   |71.65|