README.md

---
language:
- en
license: mit
library_name: transformers
tags:
- LCARS
- Star-Trek
- 128k-Context
- mistral
- chemistry
- biology
- finance
- legal
- art
- code
- medical
- text-generation-inference
pipeline_tag: text2text-generation
model-index:
- name: LCARS_AI_StarTrek_Computer
  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: 35.83
      name: strict accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=LeroyDyer/LCARS_AI_StarTrek_Computer
      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: 21.78
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=LeroyDyer/LCARS_AI_StarTrek_Computer
      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: 4.08
      name: exact match
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=LeroyDyer/LCARS_AI_StarTrek_Computer
      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: 2.35
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=LeroyDyer/LCARS_AI_StarTrek_Computer
      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: 7.44
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=LeroyDyer/LCARS_AI_StarTrek_Computer
      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: 16.2
      name: accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=LeroyDyer/LCARS_AI_StarTrek_Computer
      name: Open LLM Leaderboard
---
If anybody has star trek data please send as this starship computer database archive needs it!

then i can correctly theme this model to be inside its role as a starship computer : 
so as well as any space dara ffrom nasa ; i have collected some mufon files which i am still framing the correct prompts for ; for recall as well as interogation : 
I shall also be adding a lot of biblical data and historical data ; from sacred texts; so any generated discussions as phylosophers discussing ancient history and how to solve the problems of the past which they encountered ; in thier lifes: using historical and factual data; as well as playig thier roles after generating a biography and character role to the models to play: they should also be amazed by each others acheivements depending on thier periods:
we need multiple role and characters for these discussions: as well as as much historical facts and historys as possible to enhance this models abitlity to dicern ancient aliens truth or false : (so we need astrological, astronomical, as well as sizmological and ecological data for the periods of histroy we know : as well as the unfounded suupositions from youtube subtitles !) another useful source of themed data!


This model is a Collection of merged models via various merge methods :  Reclaiming Previous models which will be orphened by thier parent models :
THis model is the model of models so it may not Remember some task or Infact remember them all as well as highly perform !
There were some very bad NSFW Merges from role play to erotica as well as various characters and roles downloaded into the model: 
So those models were merged into other models which had been specifically trained for maths or medical data and the coding operations or even translation:

the models were heavliy dpo trained ; and various newer methodologies installed : the deep mind series is a special series which contains self correction recal, visio spacial ... step by step thinking:

SO the multi merge often fizes these errors between models as well as training gaps :Hopefully they all took and merged well !
Performing even unknown and unprogrammed tasks:
# [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/LeroyDyer__LCARS_AI_StarTrek_Computer-details)

|      Metric       |Value|
|-------------------|----:|
|Avg.               |14.61|
|IFEval (0-Shot)    |35.83|
|BBH (3-Shot)       |21.78|
|MATH Lvl 5 (4-Shot)| 4.08|
|GPQA (0-shot)      | 2.35|
|MuSR (0-shot)      | 7.44|
|MMLU-PRO (5-shot)  |16.20|