| --- |
| 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 |
| --- |
| [<img src="https://huggingface.co/proxy/cdn-avatars.huggingface.co/v1/production/uploads/65d883893a52cd9bcd8ab7cf/tRsCJlHNZo1D02kBTmfy9.jpeg" width="200"/> |
| https://github.com/spydaz |
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|
| # ::: 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! |
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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 ! |
| 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! |
| 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: |
| 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 ....... |
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| # Uploaded model |
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|
| - **Developed by:** LeroyDyer |
| - **License:** apache-2.0 |
| - **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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| # [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) |
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| | 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| |
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