language:

• en
  license: other
  tags:
• uncensored
  datasets:
• ehartford/wizard_vicuna_70k_unfiltered
  model-index:
• name: Wizard-Vicuna-30B-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: 62.12
      name: normalized accuracy
      source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ehartford/Wizard-Vicuna-30B-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: 83.45
      name: normalized accuracy
      source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ehartford/Wizard-Vicuna-30B-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: 58.24
      name: accuracy
      source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ehartford/Wizard-Vicuna-30B-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: 50.81
      source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ehartford/Wizard-Vicuna-30B-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: 78.45
      name: accuracy
      source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ehartford/Wizard-Vicuna-30B-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: 14.25
      name: accuracy
      source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ehartford/Wizard-Vicuna-30B-Uncensored
      name: Open LLM Leaderboard

This is wizard-vicuna-13b trained 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.

Discord: https://discord.gg/cognitivecomputations

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

Open LLM Leaderboard Evaluation Results

Detailed results can be found here