--- 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 --- [ 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](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [](https://github.com/unslothai/unsloth) # [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_LeroyDyer__Mixtral_AI_CyberTron_DeepMind_III_UFT) | Metric |Value| |---------------------------------|----:| |Avg. |64.37| |AI2 Reasoning Challenge (25-Shot)|61.86| |HellaSwag (10-Shot) |83.15| |MMLU (5-Shot) |61.95| |TruthfulQA (0-shot) |49.41| |Winogrande (5-shot) |77.98| |GSM8k (5-shot) |51.86|