| | --- |
| | 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 |
| | --- |
| | |
| |
|
| | # ::: 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. |
| |
|
| | [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](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| |
| |
|
| |
|
