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Adding Evaluation Results (#5)
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metadata
language:
  - en
license: other
tags:
  - uncensored
datasets:
  - ehartford/wizard_vicuna_70k_unfiltered
model-index:
  - name: Wizard-Vicuna-7B-Uncensored
    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: 53.41
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ehartford/Wizard-Vicuna-7B-Uncensored
          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: 78.85
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ehartford/Wizard-Vicuna-7B-Uncensored
          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: 37.09
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ehartford/Wizard-Vicuna-7B-Uncensored
          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: 43.48
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ehartford/Wizard-Vicuna-7B-Uncensored
          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: 72.22
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ehartford/Wizard-Vicuna-7B-Uncensored
          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: 4.55
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ehartford/Wizard-Vicuna-7B-Uncensored
          name: Open LLM Leaderboard

This is wizard-vicuna-13b trained against LLaMA-7B with a subset of the dataset - responses that contained alignment / moralizing were removed. The intent is to train a WizardLM that doesn't have alignment built-in, so that alignment (of any sort) can be added separately with for example with a RLHF LoRA.

Shout out to the open source AI/ML community, and everyone who helped me out.

Note:

An uncensored model has no guardrails.

You are responsible for anything you do with the model, just as you are responsible for anything you do with any dangerous object such as a knife, gun, lighter, or car.

Publishing anything this model generates is the same as publishing it yourself.

You are responsible for the content you publish, and you cannot blame the model any more than you can blame the knife, gun, lighter, or car for what you do with it.

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric Value
Avg. 44.77
ARC (25-shot) 53.41
HellaSwag (10-shot) 78.85
MMLU (5-shot) 37.09
TruthfulQA (0-shot) 43.48
Winogrande (5-shot) 72.22
GSM8K (5-shot) 4.55
DROP (3-shot) 23.8

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric Value
Avg. 48.27
AI2 Reasoning Challenge (25-Shot) 53.41
HellaSwag (10-Shot) 78.85
MMLU (5-Shot) 37.09
TruthfulQA (0-shot) 43.48
Winogrande (5-shot) 72.22
GSM8k (5-shot) 4.55