README.md

metadata

language:
  - en
license: apache-2.0
tags:
  - pytorch
  - mixtral
  - fine-tuned
  - moe
pipeline_tag: text-generation
model-index:
  - name: Mixtral-8x7B-Holodeck-v1
    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: 66.55
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=KoboldAI/Mixtral-8x7B-Holodeck-v1
          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: 86.78
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=KoboldAI/Mixtral-8x7B-Holodeck-v1
          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: 71.67
            name: accuracy
        source:
          url: >-
            https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=KoboldAI/Mixtral-8x7B-Holodeck-v1
          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: 48.28
        source:
          url: >-
            https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=KoboldAI/Mixtral-8x7B-Holodeck-v1
          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: 81.22
            name: accuracy
        source:
          url: >-
            https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=KoboldAI/Mixtral-8x7B-Holodeck-v1
          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: 56.18
            name: accuracy
        source:
          url: >-
            https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=KoboldAI/Mixtral-8x7B-Holodeck-v1
          name: Open LLM Leaderboard

Mixtral 8x7B - Holodeck

Model Description

Mistral 7B-Holodeck is a finetune created using Mixtral's 8x7B model.

Training data

The training data contains around 3000 ebooks in various genres. Most parts of the dataset have been prepended using the following text: [Genre: <genre1>, <genre2>]

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Limitations and Biases

Based on known problems with NLP technology, potential relevant factors include bias (gender, profession, race and religion).

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	68.45
AI2 Reasoning Challenge (25-Shot)	66.55
HellaSwag (10-Shot)	86.78
MMLU (5-Shot)	71.67
TruthfulQA (0-shot)	48.28
Winogrande (5-shot)	81.22
GSM8k (5-shot)	56.18