license: apache-2.0
tags:

• merge
• mergekit
  base_model:
• failspy/Llama-3-8B-Instruct-abliterated
  model-index:
• name: Aura-Llama-Abliterated
  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: 49.23
      name: normalized accuracy
      source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TheSkullery/Aura-Llama-Abliterated
      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: 72.27
      name: normalized accuracy
      source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TheSkullery/Aura-Llama-Abliterated
      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: 55.71
      name: accuracy
      source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TheSkullery/Aura-Llama-Abliterated
      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: 46.63
      source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TheSkullery/Aura-Llama-Abliterated
      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: 69.3
      name: accuracy
      source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TheSkullery/Aura-Llama-Abliterated
      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: 27.6
      name: accuracy
      source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TheSkullery/Aura-Llama-Abliterated
      name: Open LLM Leaderboard

Exllamav2 quant (exl2 / 2.2 bpw) made with ExLlamaV2 v0.0.21

Other EXL2 quants:

Aura-llama-3-Abliterated

Now that the cute anime girl has your attention.

UPDATE: Model is now using the abliterated version of meta llama 3 8b

Aura-llama is using the methodology presented by SOLAR for scaling LLMs called depth up-scaling (DUS), which encompasses architectural modifications with continued pretraining. Using the solar paper as a base, I integrated Llama-3 weights into the upscaled layers, and In the future plan to continue training the model.

Aura-llama is a merge of the following models to create a base model to work from:

• failspy/Llama-3-8B-Instruct-abliterated
• failspy/Llama-3-8B-Instruct-abliterated

Abliterated Merged Evals (Has Not Been Finetuned):

Aura-llama-Abliterated

• Avg: ?
• ARC: ?
• HellaSwag: ?
• MMLU: ?
• T-QA: ?
• Winogrande: ?
• GSM8K: ?

Non Abliterated Merged Evals (Has Not Been Finetuned):

Aura-llama-Original

• Avg: 63.13
• ARC: 58.02
• HellaSwag: 77.82
• MMLU: 65.61
• T-QA: 51.94
• Winogrande: 73.40
• GSM8K: 52.01

🧩 Configuration

dtype: bfloat16
merge_method: passthrough
slices:
- sources:
  - layer_range: [0, 12]
    model: failspy/Llama-3-8B-Instruct-abliterated
- sources:
  - layer_range: [8, 20]
    model: failspy/Llama-3-8B-Instruct-abliterated
- sources:
  - layer_range: [16, 28]
    model: failspy/Llama-3-8B-Instruct-abliterated
- sources:
  - layer_range: [24, 32]
    model: failspy/Llama-3-8B-Instruct-abliterated
        

Open LLM Leaderboard Evaluation Results

Detailed results can be found here