Update README.md

README.md

@@ -9,7 +9,127 @@ tags:
 - 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
 ---
+  
+
+# ::: DEEP MIND PROJECT :::
+
+here we begin the models for Deep mind :
+
+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! -
+
+
+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