---
language:
- en
license: apache-2.0
library_name: transformers
tags:
- not-for-all-audiences
base_model:
- Qwen/Qwen2.5-7B
pipeline_tag: text-generation
model-index:
- name: Qwen2.5-7B-nerd-uncensored-v1.0
  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: 76.95
      name: strict accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=jeffmeloy/Qwen2.5-7B-nerd-uncensored-v1.0
      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: 34.74
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=jeffmeloy/Qwen2.5-7B-nerd-uncensored-v1.0
      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: 0.15
      name: exact match
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=jeffmeloy/Qwen2.5-7B-nerd-uncensored-v1.0
      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: 5.37
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=jeffmeloy/Qwen2.5-7B-nerd-uncensored-v1.0
      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: 16.82
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=jeffmeloy/Qwen2.5-7B-nerd-uncensored-v1.0
      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: 36.15
      name: accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=jeffmeloy/Qwen2.5-7B-nerd-uncensored-v1.0
      name: Open LLM Leaderboard
---

## Model Description

Model created by analyzing and selecting the optimal layers from other Qwen2.5-7B models based on their dimensional utilization efficiency, measured by the Normalized Effective Rank (NER). Computed like:

   - Input: Weight matrix for each model layer
   - Compute singular values σᵢ where σᵢ ≥ 0 # σᵢ represents the importance of each dimension
   - Filter values above numerical threshold (>1e-12) 
   - Sum all singular values: S = Σσᵢ # S acts as normalization factor
   - Create probability distribution: pᵢ = σᵢ/S # converts singular values to probabilities summing to 1
   - Compute Shannon entropy: H = -Σ(pᵢ * log₂(pᵢ)) # measures information content 
   - Calculate maximum possible entropy: H_max = log₂(n)
   - Final NER score = H/H_max # normalizes score to [0,1] range
   - Results in value between 0 and 1 for each model layer

## Creating Composite Model

Code here: https://huggingface.co/jeffmeloy/Qwen2.5-7B-nerd-uncensored-v1.0/blob/main/ner_merge.py

Code functions:
   - Download selected models from Hugging Face Hub 
   - Calculate Normalized Effective Rank (NER) for each layer within each model 
   - Define model and layer name pairs that have highest NER for each layer based on their NER scores 
   - Incrementally build a composite model using layer with highest NER from model pool
   - Save merge reports documenting layer sources 
   - Copy config and tokenizer files from base model
   - Save the composite model with complete weights # model ready to use

Configfile:

base_model: "Qwen/Qwen2.5-7B"

fine_tuned_models: # uncomment the models you want to merge

#- "Qwen/Qwen2.5-7B"

#- "Qwen/Qwen2.5-7B-Instruct"

#- "EVA-UNIT-01/EVA-Qwen2.5-7B-v0.1"

#- "FourOhFour/Vapor_v2_7B"

#- "Goekdeniz-Guelmez/Josiefied-Qwen2.5-7B-Instruct-abliterated-v2"

#- "happzy2633/qwen2.5-7b-ins-v3"

#- "huihui-ai/Qwen2.5-7B-Instruct-abliterated-v2"

#- "HumanLLMs/Humanish-Qwen2.5-7B-Instruct"

#- "Orion-zhen/Qwen2.5-7B-Instruct-Uncensored"

#- "Orion-zhen/Meissa-Qwen2.5-7B-Instruct"

#- "jeffmeloy/Qwen2.5-7B-nerd-uncensored-v0.9"

#- "jeffmeloy/Qwen2.5-7B-nerd-uncensored-v1.0"

#- "jeffmeloy/Qwen2.5-7B-nerd-uncensored-v1.1"

#- "jeffmeloy/Qwen2.5-7B-nerd-uncensored-v1.2"

#- "AmberYifan/Qwen2.5-7B-dpo-2k"

#- "sethuiyer/Qwen2.5-7B-Anvita"

#- "rombodawg/Rombos-LLM-V2.5-Qwen-7b"

#- "Cran-May/T.E-8.1"

#- "beomi/Qwen2.5-7B-Instruct-kowiki-qa"

#- "Orion-zhen/Qwen2.5-7B-Gutenberg-KTO"

#- "fblgit/cybertron-v4-qw7B-MGS"

#- "nguyentd/FinancialAdvice-Qwen2.5-7B"

#- "WhiteRabbitNeo/WhiteRabbitNeo-2.5-Qwen-2.5-Coder-7B"

#- "edgerunner-ai/EdgeRunner-Command-Nested"

#- "katanemo/Arch-Function-7B"

#- "DeepGlint-AI/llava-mlcd-qwen2.5-7b"

#- "mergekit-community/mergekit-slerp-aflqaqy"

#- "mergekit-community/mergekit-ties-inxwsfo"

#- "Qwen/Qwen2.5-Coder-7B-Instruct"

#- "Qwen/Qwen2.5-Math-7B-Instruct" 

#- "Qwen/Qwen2.5-Coder-7B"

#- "Qwen/Qwen2.5-Math-7B"

#- "thomas-yanxin/XinYuan-Qwen2.5-7B-0917"

#- "jbjeong91/Qwen2.5_7B_IST_StoryGen_vanilla"

#- "AmberYifan/Qwen2.5-7B-dpo-2k-hhrlhf"

#- "jbjeong91/Qwen2.5_7B_IST_StoryGen_test2" 

models_dir: "./input_models/"

output_dir: "./merged_model/"

metric_dir: "./metrics/"
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_jeffmeloy__Qwen2.5-7B-nerd-uncensored-v1.0)

|      Metric       |Value|
|-------------------|----:|
|Avg.               |28.36|
|IFEval (0-Shot)    |76.95|
|BBH (3-Shot)       |34.74|
|MATH Lvl 5 (4-Shot)| 0.15|
|GPQA (0-shot)      | 5.37|
|MuSR (0-shot)      |16.82|
|MMLU-PRO (5-shot)  |36.15|