base_model: Goekdeniz-Guelmez/Josiefied-Qwen2.5-7B-Instruct-abliterated-v2
language:

• en
  license: apache-2.0
  license_link: https://huggingface.co/Qwen/Qwen2.5-7B-Instruct/blob/main/LICENSE
  pipeline_tag: text-generation
  tags:
• chat
• mlx
  model-index:
• name: Josiefied-Qwen2.5-7B-Instruct-abliterated-v2
  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: 78.41
      name: strict accuracy
      source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Isaak-Carter/Josiefied-Qwen2.5-7B-Instruct-abliterated-v2
      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: 33.29
      name: normalized accuracy
      source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Isaak-Carter/Josiefied-Qwen2.5-7B-Instruct-abliterated-v2
      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: 0.0
      name: exact match
      source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Isaak-Carter/Josiefied-Qwen2.5-7B-Instruct-abliterated-v2
      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: 6.49
      name: acc_norm
      source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Isaak-Carter/Josiefied-Qwen2.5-7B-Instruct-abliterated-v2
      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: 13.96
      name: acc_norm
      source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Isaak-Carter/Josiefied-Qwen2.5-7B-Instruct-abliterated-v2
      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: 34.76
      name: accuracy
      source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Isaak-Carter/Josiefied-Qwen2.5-7B-Instruct-abliterated-v2
      name: Open LLM Leaderboard

mlx-community/Josiefied-Qwen2.5-7B-Instruct-abliterated-v2-2-bit

The Model mlx-community/Josiefied-Qwen2.5-7B-Instruct-abliterated-v2-2-bit was converted to MLX format from Goekdeniz-Guelmez/Josiefied-Qwen2.5-7B-Instruct-abliterated-v2 using mlx-lm version 0.18.2.

Use with mlx

pip install mlx-lm

from mlx_lm import load, generate

model, tokenizer = load("mlx-community/Josiefied-Qwen2.5-7B-Instruct-abliterated-v2-2-bit")

prompt="hello"

if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
    messages = [{"role": "user", "content": prompt}]
    prompt = tokenizer.apply_chat_template(
        messages, tokenize=False, add_generation_prompt=True
    )

response = generate(model, tokenizer, prompt=prompt, verbose=True)