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language: |
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- en |
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license: apache-2.0 |
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tags: |
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- text-generation-inference |
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- transformers |
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- unsloth |
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- mistral |
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- trl |
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base_model: LeroyDyer/Mixtral_AI_CyberTron_DeepMind_III |
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datasets: |
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- gretelai/synthetic_text_to_sql |
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- HuggingFaceTB/cosmopedia |
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- teknium/OpenHermes-2.5 |
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- Open-Orca/SlimOrca |
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- Open-Orca/OpenOrca |
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- cognitivecomputations/dolphin-coder |
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- databricks/databricks-dolly-15k |
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- yahma/alpaca-cleaned |
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- uonlp/CulturaX |
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- mwitiderrick/SwahiliPlatypus |
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- swahili |
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- Rogendo/English-Swahili-Sentence-Pairs |
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- ise-uiuc/Magicoder-Evol-Instruct-110K |
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- meta-math/MetaMathQA |
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- abacusai/ARC_DPO_FewShot |
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- abacusai/MetaMath_DPO_FewShot |
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- abacusai/HellaSwag_DPO_FewShot |
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- HaltiaAI/Her-The-Movie-Samantha-and-Theodore-Dataset |
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- gretelai/synthetic_text_to_sql |
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- HuggingFaceTB/cosmopedia |
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- teknium/OpenHermes-2.5 |
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- cognitivecomputations/dolphin-coder |
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- databricks/databricks-dolly-15k |
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- yahma/alpaca-cleaned |
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- uonlp/CulturaX |
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- mwitiderrick/SwahiliPlatypus |
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- swahili |
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- Rogendo/English-Swahili-Sentence-Pairs |
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- ise-uiuc/Magicoder-Evol-Instruct-110K |
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- meta-math/MetaMathQA |
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metrics: |
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- accuracy |
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- bertscore |
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- bleu |
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- brier_score |
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- cer |
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- character |
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- charcut_mt |
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- chrf |
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- code_eval |
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y-Gene: |
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- LeroyDyer/Mixtral_AI_DeepMind |
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- LeroyDyer/Mixtral_AI_CyberUltron_DPO |
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- LeroyDyer/Mixtral_AI_Chat_2.0 |
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- LeroyDyer/Mixtral_AI_DeepMedicalMind |
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- LeroyDyer/Mixtral_AI_Samantha |
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x-Gene: |
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- LeroyDyer/Mixtral_AI_Chat_2.0 |
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- LeroyDyer/Mixtral_BioMedical |
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- LeroyDyer/Mixtral_AI_Medic |
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- LeroyDyer/Mixtral_Cyber_BioMedic |
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- LeroyDyer/Mixtral_AI_DeepMedicalMind |
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Variant: |
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- LeroyDyer/MetaMath_LLM |
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- LeroyDyer/TruthfulQA_LLM |
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- LeroyDyer/HellaSwag_LLM |
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- LeroyDyer/Mixtral_AI_DeepMedicalMind |
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--- |
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# ::: DEEP MIND PROJECT ::: |
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OH MY GOSH , GOOD WOW! |
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ARE WE MAKING BRAINS NOW!!!!! (Contact me to Sponser me PLEASE) |
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---- I NEED A CLOUD TO DESIGN THIS MIND! --(freeColab takes years! - i need the large data-sets in... |
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which need a few days on a server fine tuning until fully complete ! i NEED A COLABORATOR!! ) |
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- Mistral models are GREAT!!!!!!! - we have supassed ChatGPT : (- without langchain!!!! ) |
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- I now have amethodolgy to add any functionality to the model ! |
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- we are in the future now : |
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- we do not want to code or buy software! |
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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 ! |
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good for oneshot prompts and it actually uses the history in the chat !!! ) |
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but we have TASKS! |
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we can now ask the model to perform these tasks and get the right output without special programming ! |
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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 !)) |
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VERY HAPPY! (need more good data (my problem acually is not data (its converting it to json from CSV and other forms! (pre-structured )))) |
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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! |
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this model created from the first trained models : deepmind! |
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these models contain: |
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## thoughts and processes : |
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## SelfRAG: |
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## Agent Generation: |
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## Chain of thoughts : |
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## Deep thinking and memory recall: |
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## Training Prompt version - Working GREAT! -(cant blow my own horn enough!!!!) |
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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)) |
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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 |
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It thinks.... sometimes sarcasim , sometimes reflection... sometimes random thoughts ... |
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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; |
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It has been really training mainly on coding datasets and medical information : from experiments to research to patient/doctor .. to diagnosis ... to problem solving : |
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it has been trained to be a counseller and assist with psycological problems :: empathtetic discussion : |
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this one has its own thoughts despite the prompt given : (if you allow the thought prompt it will display the thoughts) |
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this is a highly focused model : |
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### Methodology: |
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many functions such as defining words andnlp task we also added via datsets and very complexed datstructures and prompts : |
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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): |
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its important to Change Lora configuration for Embedding layers within the model as well as fine tuning above previous training: |
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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 ! |
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after testing the was absolutly 0 loss from previous knowledge as well as enhancing some responses and providing comparitive responses for others; |
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I personally use a topK of 1000.... |
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this allows the model to have many choices (this is the context window of results), |
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i put my topP to 0.68(68%).... |
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hence it will select from that percentage of probabiltys... |
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enabling for my temp to be 1 .. |
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therfore it will normalize the selected quartile of next probablity selection enabling for the lower probabiltys to have a scaled chace in being selected : |
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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! |
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hence some information being absolute and others being transient and constantly updateing: |
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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 : |
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hence when utilizing a rag : the conversation history is the dats to be fine tuned into the model as frequent data! |
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as well as producing multiple simular querys to query the rag system for Q/A pairs : also to be updted onto the model : |
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as we are in this development period we are focused on BRAIN cureently ....... |
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# Uploaded model |
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- **Developed by:** LeroyDyer |
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- **License:** apache-2.0 |
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- **Finetuned from model :** LeroyDyer/Mixtral_AI_CyberTron_DeepMind_III |
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This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. |
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[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth) |
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