---
license: cc-by-nc-4.0
model-index:
- name: PiVoT-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: 63.91
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=maywell/PiVoT-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: 83.52
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=maywell/PiVoT-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: 60.71
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=maywell/PiVoT-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: 54.64
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=maywell/PiVoT-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: 76.32
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=maywell/PiVoT-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: 39.12
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=maywell/PiVoT-MoE
      name: Open LLM Leaderboard
---
# PiVot-MoE
![img](./PiVoT-MoE.png)

## Model Description

PiVoT-MoE, is an advanced AI model specifically designed for roleplaying purposes. It has been trained using a combination of four 10.7B sized experts, each with their own specialized characteristic, all fine-tuned to bring a unique and diverse roleplaying experience.

The Mixture of Experts (MoE) technique is utilized in this model, allowing the experts to work together synergistically, resulting in a more cohesive and natural conversation flow. The MoE architecture allows for a higher level of flexibility and adaptability, enabling PiVoT-MoE to handle a wide variety of roleplaying scenarios and characters.

Based on the PiVoT-10.7B-Mistral-v0.2-RP model, PiVoT-MoE takes it a step further with the incorporation of the MoE technique. This means that not only does the model have an expansive knowledge base, but it also has the ability to mix and match its expertise to better suit the specific roleplaying scenario.

## Prompt Template - Alpaca (ChatML works)
```
{system}
### Instruction:
{instruction}
### Response:
{response}
```
# [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_maywell__PiVoT-MoE)

|             Metric              |Value|
|---------------------------------|----:|
|Avg.                             |63.04|
|AI2 Reasoning Challenge (25-Shot)|63.91|
|HellaSwag (10-Shot)              |83.52|
|MMLU (5-Shot)                    |60.71|
|TruthfulQA (0-shot)              |54.64|
|Winogrande (5-shot)              |76.32|
|GSM8k (5-shot)                   |39.12|