README.md

---
language:
- en
license: llama2
tags:
- moe
model-index:
- name: orthorus-125b-moe
  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: 67.66
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ibivibiv/orthorus-125b-moe
      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: 85.52
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ibivibiv/orthorus-125b-moe
      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: 68.94
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ibivibiv/orthorus-125b-moe
      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: 56.27
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ibivibiv/orthorus-125b-moe
      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: 82.32
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ibivibiv/orthorus-125b-moe
      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: 56.79
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ibivibiv/orthorus-125b-moe
      name: Open LLM Leaderboard
---

![img](./orthorus.png)

This is a test run for a future 70b parameter models moe model.  I took WizardLM/WizardLM-70B-V1.0 and migtissera/Synthia-70B as two base models and created the discriminator prompts to push technical, logic, and math type questions to the Wizard side and then all creative or conversation questions to the Synthia side.  Now that this is working for me I am going to move to fine tuning models for more specific tasks.  This model takes about 240GB of VRAM for full resolution inference.  As far as I know, it is the first 125B parameter moe model publicly available.  I plan on making more and sharing of course. 

Hopefully I can add more info on this model, it loads perfectly for me and responds nicely.  It might take me a bit since I want to make "Cerberus" with the fine tuned models and get it released.  But enjoy this one, llama2 model.
# [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_ibivibiv__orthorus-125b-moe)

|             Metric              |Value|
|---------------------------------|----:|
|Avg.                             |69.58|
|AI2 Reasoning Challenge (25-Shot)|67.66|
|HellaSwag (10-Shot)              |85.52|
|MMLU (5-Shot)                    |68.94|
|TruthfulQA (0-shot)              |56.27|
|Winogrande (5-shot)              |82.32|
|GSM8k (5-shot)                   |56.79|