README.md

---
language:
- en
- fr
- es
- ru
- zh
- ja
- fa
- code
license: mit
library_name: transformers
base_model:
- fluently-lm/FluentlyLM-Prinum
tags:
- abliterated
- uncensored
- fluently-lm
- fluently
- prinum
- instruct
- trained
- math
- roleplay
- reasoning
- axolotl
- unsloth
- argilla
- qwen2
datasets:
- fluently-sets/ultraset
- fluently-sets/ultrathink
- fluently-sets/reasoning-1-1k
- fluently-sets/MATH-500-Overall
inference: true
pipeline_tag: text-generation
model-index:
- name: FluentlyLM-Prinum
  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: 80.9
      name: strict accuracy
    source:
      url: >-
        https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=fluently-lm/FluentlyLM-Prinum
      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: 59.48
      name: normalized accuracy
    source:
      url: >-
        https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=fluently-lm/FluentlyLM-Prinum
      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: 54
      name: exact match
    source:
      url: >-
        https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=fluently-lm/FluentlyLM-Prinum
      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: 18.23
      name: acc_norm
    source:
      url: >-
        https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=fluently-lm/FluentlyLM-Prinum
      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: 17.26
      name: acc_norm
    source:
      url: >-
        https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=fluently-lm/FluentlyLM-Prinum
      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: 53.42
      name: accuracy
    source:
      url: >-
        https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=fluently-lm/FluentlyLM-Prinum
      name: Open LLM Leaderboard
---

# huihui-ai/FluentlyLM-Prinum-abliterated


This is an uncensored version of [fluently-lm/FluentlyLM-Prinum](https://huggingface.co./fluently-lm/FluentlyLM-Prinum) created with abliteration (see [remove-refusals-with-transformers](https://github.com/Sumandora/remove-refusals-with-transformers) to know more about it).  
This is a crude, proof-of-concept implementation to remove refusals from an LLM model without using TransformerLens.    


## Use with ollama

You can use [huihui_ai/fluentlylm-prinum-abliterated](https://ollama.com/huihui_ai/fluentlylm-prinum-abliterated) directly
```
ollama run huihui_ai/fluentlylm-prinum-abliterated
```

### Donation

If you like it, please click 'like' and follow us for more updates.  
You can follow [x.com/support_huihui](https://x.com/support_huihui) to get the latest model information from huihui.ai.

##### Your donation helps us continue our further development and improvement, a cup of coffee can do it.
- bitcoin:
```
  bc1qqnkhuchxw0zqjh2ku3lu4hq45hc6gy84uk70ge
```