language:
 - en
license: apache-2.0
tags:
 - text-generation-inference
 - transformers
 - leaderboard
 - mistral
 - trl
base_model: LeroyDyer/Mixtral_AI_CyberTron_DeepMind_III
datasets:
 - gretelai/synthetic_text_to_sql
 - HuggingFaceTB/cosmopedia
 - teknium/OpenHermes-2.5
 - Open-Orca/SlimOrca
 - Open-Orca/OpenOrca
 - cognitivecomputations/dolphin-coder
 - databricks/databricks-dolly-15k
 - yahma/alpaca-cleaned
 - uonlp/CulturaX
 - mwitiderrick/SwahiliPlatypus
 - swahili
 - Rogendo/English-Swahili-Sentence-Pairs
 - ise-uiuc/Magicoder-Evol-Instruct-110K
 - meta-math/MetaMathQA
 - abacusai/ARC_DPO_FewShot
 - abacusai/MetaMath_DPO_FewShot
 - abacusai/HellaSwag_DPO_FewShot
 - HaltiaAI/Her-The-Movie-Samantha-and-Theodore-Dataset
 - gretelai/synthetic_text_to_sql
 - HuggingFaceTB/cosmopedia
 - teknium/OpenHermes-2.5
 - cognitivecomputations/dolphin-coder
 - databricks/databricks-dolly-15k
 - yahma/alpaca-cleaned
 - uonlp/CulturaX
 - mwitiderrick/SwahiliPlatypus
 - swahili
 - Rogendo/English-Swahili-Sentence-Pairs
 - ise-uiuc/Magicoder-Evol-Instruct-110K
 - meta-math/MetaMathQA
metrics:
 - accuracy
 - bertscore
 - bleu
 - brier_score
 - cer
 - character
 - charcut_mt
 - chrf
 - code_eval
y-Gene:
 - LeroyDyer/Mixtral_AI_DeepMind
 - LeroyDyer/Mixtral_AI_CyberUltron_DPO
 - LeroyDyer/Mixtral_AI_Chat_2.0
 - LeroyDyer/Mixtral_AI_DeepMedicalMind
 - LeroyDyer/Mixtral_AI_Samantha
x-Gene:
 - LeroyDyer/Mixtral_AI_Chat_2.0
 - LeroyDyer/Mixtral_BioMedical
 - LeroyDyer/Mixtral_AI_Medic
 - LeroyDyer/Mixtral_Cyber_BioMedic
 - LeroyDyer/Mixtral_AI_DeepMedicalMind
Variant:
 - LeroyDyer/MetaMath_LLM
 - LeroyDyer/TruthfulQA_LLM
 - LeroyDyer/HellaSwag_LLM
 - LeroyDyer/Mixtral_AI_DeepMedicalMind
model-index:
 - name: Mixtral_AI_CyberTron_DeepMind_III_UFT
 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: 61.86
 name: normalized accuracy
 source:
 url: >-
 https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=LeroyDyer/Mixtral_AI_CyberTron_DeepMind_III_UFT
 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: 83.15
 name: normalized accuracy
 source:
 url: >-
 https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=LeroyDyer/Mixtral_AI_CyberTron_DeepMind_III_UFT
 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: 61.95
 name: accuracy
 source:
 url: >-
 https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=LeroyDyer/Mixtral_AI_CyberTron_DeepMind_III_UFT
 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: 49.41
 source:
 url: >-
 https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=LeroyDyer/Mixtral_AI_CyberTron_DeepMind_III_UFT
 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: 77.98
 name: accuracy
 source:
 url: >-
 https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=LeroyDyer/Mixtral_AI_CyberTron_DeepMind_III_UFT
 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: 51.86
 name: accuracy
 source:
 url: >-
 https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=LeroyDyer/Mixtral_AI_CyberTron_DeepMind_III_UFT
 name: Open LLM Leaderboard

[👁 Image
https://github.com/spydaz

::: DEEP MIND PROJECT :::

OH MY GOSH , GOOD WOW! ARE WE MAKING BRAINS NOW!!!!! (Contact me to Sponser me PLEASE)

---- I NEED A CLOUD TO DESIGN THIS MIND! --(freeColab takes years! - i need the large data-sets in... which need a few days on a server fine tuning until fully complete ! i NEED A COLABORATOR!! )

• Mistral models are GREAT!!!!!!! - we have supassed ChatGPT : (- without langchain!!!! )
• I now have amethodolgy to add any functionality to the model !
• we are in the future now :
• we do not want to code or buy software!

Lovely model !!! Very knowledgeabe :: (sometimes requires coaxing !! but it has options to choose from so for a single thing there may be multiple response so you can ask in another way ! good for oneshot prompts and it actually uses the history in the chat !!! )

but we have TASKS!

we can now ask the model to perform these tasks and get the right output without special programming !

take a model !!! This model CONVERGES on ANYTHING! ( i also previously trained it will the clip training for captioning also but never used it ! but i pluged it in and it was spot on!(so if you choose to incorperate the model into a decoder/encoder model (vision) its ready !))

VERY HAPPY! (need more good data (my problem acually is not data (its converting it to json from CSV and other forms! (pre-structured ))))

here we begin the models for Deep mind : Whoop! as we move forwards we have begun to let the model teach itself like a child and optimize!

this model created from the first trained models : deepmind! these models contain:

thoughts and processes :

SelfRAG:

Agent Generation:

Chain of thoughts :

Deep thinking and memory recall:

Training Prompt version - Working GREAT! -(cant blow my own horn enough!!!!)

checks itsef discussing complex questions (question it does not know the answer to ... it trys to discuss with itself to find a result(sometimes unsucessfully))

It generates Mini agents to perform small tasks such as entity recognition; step by step definitions, write psuedo codebases , generare uscases... perform calculations, analize content

It thinks.... sometimes sarcasim , sometimes reflection... sometimes random thoughts ...

it has personalitys : by installing various long discussions with chat gpt in persona it weas able to generate role coversation data, which was added to its conversation chat Q/A; as well as a datset from the samantha tv show ... and HER!.... so it is a personal assistant and very friendly;

It has been really training mainly on coding datasets and medical information : from experiments to research to patient/doctor .. to diagnosis ... to problem solving :

it has been trained to be a counseller and assist with psycological problems :: empathtetic discussion :

this one has its own thoughts despite the prompt given : (if you allow the thought prompt it will display the thoughts)

this is a highly focused model :

Methodology:

many functions such as defining words andnlp task we also added via datsets and very complexed datstructures and prompts : These prompts are removed after training and standard alpaca training given on top:(this enables for the previous highly over fit task to become embedded underneath the previous layer): its important to Change Lora configuration for Embedding layers within the model as well as fine tuning above previous training: Usually i deploy a factor of 8 calcuculation for my loras by this one i chose factor of 9 (9-18/18/36) .... which actually trained so smoothly that i was able to train many different datsets in a signle sitting ; to below 0.9 all varioations of the alpaca prompt ! after testing the was absolutly 0 loss from previous knowledge as well as enhancing some responses and providing comparitive responses for others; I personally use a topK of 1000.... this allows the model to have many choices (this is the context window of results), i put my topP to 0.68(68%).... hence it will select from that percentage of probabiltys... enabling for my temp to be 1 .. therfore it will normalize the selected quartile of next probablity selection enabling for the lower probabiltys to have a scaled chace in being selected : It is important to have a degree of randomness in the respopnse or you will ask the same question and get the same answer ! .... we need varied answer to ome querys and focues for other ? how do we do this ?..... Duplicates!!!!! raising the probability of some information by repetition : as this is how the human learns truth ! truth is that which has been repeated so many times it cannot be disputed! hence some information being absolute and others being transient and constantly updateing: As a predictve model it needs to be ables to have the ability to calculate and predicte and cclassify as wel as recall exact information : hence when utilizing a rag : the conversation history is the dats to be fine tuned into the model as frequent data! as well as producing multiple simular querys to query the rag system for Q/A pairs : also to be updted onto the model : as we are in this development period we are focused on BRAIN cureently .......

Uploaded model

• Developed by: LeroyDyer
• License: apache-2.0
• Finetuned from model : LeroyDyer/Mixtral_AI_CyberTron_DeepMind_III

This mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.

👁 Image

Open LLM Leaderboard Evaluation Results

Detailed results can be found here