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Model: LeroyDyer/SpyazWeb_AI_DeepMind_Project
Source: Original Platform

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README.md

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+---
+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://cdn-avatars.huggingface.co/v1/production/uploads/65d883893a52cd9bcd8ab7cf/tRsCJlHNZo1D02kBTmfy9.jpeg" width="200"/>
+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.
+
+[<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|
+

config.json

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+{
+  "_name_or_path": "LeroyDyer/Mixtral_AI_CyberTron_DeepMind_III",
+  "architectures": [
+    "MistralForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "max_position_embeddings": 32768,
+  "model_type": "mistral",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 8,
+  "rms_norm_eps": 1e-05,
+  "rope_theta": 10000.0,
+  "sliding_window": 4096,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float16",
+  "transformers_version": "4.40.1",
+  "unsloth_version": "2024.4",
+  "use_cache": true,
+  "vocab_size": 32000
+}

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+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "transformers_version": "4.40.1"
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+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,44 @@
+{
+  "add_bos_token": true,
+  "add_eos_token": false,
+  "add_prefix_space": true,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "additional_special_tokens": [],
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "</s>",
+  "legacy": true,
+  "model_max_length": 32768,
+  "pad_token": "<unk>",
+  "padding_side": "left",
+  "sp_model_kwargs": {},
+  "spaces_between_special_tokens": false,
+  "tokenizer_class": "LlamaTokenizer",
+  "unk_token": "<unk>",
+  "use_default_system_prompt": false
+}