tensorblock/Meta-Llama-3.1-8B-Instruct-abliterated-GGUF

license: llama3.1
library_name: transformers
tags:

• abliterated
• uncensored
• TensorBlock
• GGUF
  base_model: mlabonne/Meta-Llama-3.1-8B-Instruct-abliterated
  model-index:
• name: Meta-Llama-3.1-8B-Instruct-abliterated
  results:
  • task:
    type: text-generation
    name: Text Generation
    dataset:
    name: IFEval (0-Shot)
    type: HuggingFaceH4/ifeval
    args:
    num_few_shot: 0
    
    metrics:
    • type: inst_level_strict_acc and prompt_level_strict_acc
      value: 73.29
      name: strict accuracy
      source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=mlabonne/Meta-Llama-3.1-8B-Instruct-abliterated
      name: Open LLM Leaderboard
  • task:
    type: text-generation
    name: Text Generation
    dataset:
    name: BBH (3-Shot)
    type: BBH
    args:
    num_few_shot: 3
    
    metrics:
    • type: acc_norm
      value: 27.13
      name: normalized accuracy
      source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=mlabonne/Meta-Llama-3.1-8B-Instruct-abliterated
      name: Open LLM Leaderboard
  • task:
    type: text-generation
    name: Text Generation
    dataset:
    name: MATH Lvl 5 (4-Shot)
    type: hendrycks/competition_math
    args:
    num_few_shot: 4
    
    metrics:
    • type: exact_match
      value: 6.42
      name: exact match
      source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=mlabonne/Meta-Llama-3.1-8B-Instruct-abliterated
      name: Open LLM Leaderboard
  • task:
    type: text-generation
    name: Text Generation
    dataset:
    name: GPQA (0-shot)
    type: Idavidrein/gpqa
    args:
    num_few_shot: 0
    
    metrics:
    • type: acc_norm
      value: 0.89
      name: acc_norm
      source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=mlabonne/Meta-Llama-3.1-8B-Instruct-abliterated
      name: Open LLM Leaderboard
  • task:
    type: text-generation
    name: Text Generation
    dataset:
    name: MuSR (0-shot)
    type: TAUR-Lab/MuSR
    args:
    num_few_shot: 0
    
    metrics:
    • type: acc_norm
      value: 3.21
      name: acc_norm
      source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=mlabonne/Meta-Llama-3.1-8B-Instruct-abliterated
      name: Open LLM Leaderboard
  • task:
    type: text-generation
    name: Text Generation
    dataset:
    name: MMLU-PRO (5-shot)
    type: TIGER-Lab/MMLU-Pro
    config: main
    split: test
    args:
    num_few_shot: 5
    
    metrics:
    • type: acc
      value: 27.81
      name: accuracy
      source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=mlabonne/Meta-Llama-3.1-8B-Instruct-abliterated
      name: Open LLM Leaderboard

mlabonne/Meta-Llama-3.1-8B-Instruct-abliterated - GGUF

This repo contains GGUF format model files for mlabonne/Meta-Llama-3.1-8B-Instruct-abliterated.

The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4011.

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<|begin_of_text|><|start_header_id|>system<|end_header_id|>

{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>

{prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>

Model file specification

Downloading instruction

Command line

Firstly, install Huggingface Client

pip install -U "huggingface_hub[cli]"

Then, downoad the individual model file the a local directory

huggingface-cli download tensorblock/Meta-Llama-3.1-8B-Instruct-abliterated-GGUF --include "Meta-Llama-3.1-8B-Instruct-abliterated-Q2_K.gguf" --local-dir MY_LOCAL_DIR

If you wanna download multiple model files with a pattern (e.g., *Q4_K*gguf), you can try:

huggingface-cli download tensorblock/Meta-Llama-3.1-8B-Instruct-abliterated-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'