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 and Huggingface's TRL library.
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| 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 |