README.md

---
license: other
pipeline_tag: text-generation
license_name: microsoft-research-license
model-index:
- name: Orca-2-13b-Alpaca-Uncensored
  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: 61.09
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=athirdpath/Orca-2-13b-Alpaca-Uncensored
      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: 79.27
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=athirdpath/Orca-2-13b-Alpaca-Uncensored
      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: 60.13
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=athirdpath/Orca-2-13b-Alpaca-Uncensored
      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: 53.59
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=athirdpath/Orca-2-13b-Alpaca-Uncensored
      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: 77.43
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=athirdpath/Orca-2-13b-Alpaca-Uncensored
      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: 38.29
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=athirdpath/Orca-2-13b-Alpaca-Uncensored
      name: Open LLM Leaderboard
---
This model is a fine-tuned version of microsoft/Orca-2-13b on a subset of the Vezora/Mini_Orca_Uncencored_Alpaca dataset, adjusted to demonstrate the relationship between instruction and input, with some particularly spicy prompts added to reduce the risk of rejections.

Only the q_proj and k_proj modules were targeted and a low rank (8) was used, in hopes of containing the adjustments to the prompt format and alignment. This is promising on paper, with the training's per-step loss averaging <0.9 for the last third of the run.

Reasoning stayed solid (for a 13b model) and I consider this a success. Performance is slighty worse than OG Orca-2 in Ooba's chat mode, comparable in Alpaca chat-instruct mode to the OG in ChatLM chat-instruct mode.

May still reject some shocking prompts, but can easily be overcome with author's note or character card.
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_athirdpath__Orca-2-13b-Alpaca-Uncensored)

|             Metric              |Value|
|---------------------------------|----:|
|Avg.                             |61.63|
|AI2 Reasoning Challenge (25-Shot)|61.09|
|HellaSwag (10-Shot)              |79.27|
|MMLU (5-Shot)                    |60.13|
|TruthfulQA (0-shot)              |53.59|
|Winogrande (5-shot)              |77.43|
|GSM8k (5-shot)                   |38.29|