Update README.md

README.md

@@ -1,11 +1,82 @@
 ---
-license: apache-2.0
+base_model:
+- LeroyDyer/SpydazWeb_AI_CyberTron_Ultra_7b
+- LeroyDyer/LCARS_AI_StarTrek_Computer
+- LeroyDyer/_Spydaz_Web_AI_ActionQA_Project
+- LeroyDyer/_Spydaz_Web_AI_ChatML_512K_Project
+- LeroyDyer/SpyazWeb_AI_DeepMind_Project
+- LeroyDyer/SpydazWeb_AI_Swahili_Project
+- LeroyDyer/_Spydaz_Web_AI_08
+- LeroyDyer/_Spydaz_Web_AI_ChatQA_001
+- LeroyDyer/_Spydaz_Web_AI_ChatQA_001_SFT
+- LeroyDyer/_Spydaz_Web_AI_ChatQA_003
+- LeroyDyer/_Spydaz_Web_AI_ChatQA_004
+- LeroyDyer/_Spydaz_Web_AI_ChatQA_ReAct_Project_UltraFineTuned
+library_name: transformers
+language:
+- en
+- sw
+- ig
+- so
+- es
+- ca
+- xh
+- zu
+- ha
+- tw
+- af
+- hi
+- bm
+- su
 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
+- HuggingFaceFW/fineweb
+- occiglot/occiglot-fineweb-v0.5
+- omi-health/medical-dialogue-to-soap-summary
+- keivalya/MedQuad-MedicalQnADataset
+- ruslanmv/ai-medical-dataset
+- Shekswess/medical_llama3_instruct_dataset_short
+- ShenRuililin/MedicalQnA
+- virattt/financial-qa-10K
+- PatronusAI/financebench
+- takala/financial_phrasebank
+- Replete-AI/code_bagel
+- athirdpath/DPO_Pairs-Roleplay-Alpaca-NSFW
+- IlyaGusev/gpt_roleplay_realm
+- rickRossie/bluemoon_roleplay_chat_data_300k_messages
+- jtatman/hypnosis_dataset
+- Hypersniper/philosophy_dialogue
+- Locutusque/function-calling-chatml
+- bible-nlp/biblenlp-corpus
+- DatadudeDev/Bible
+- Helsinki-NLP/bible_para
+- HausaNLP/AfriSenti-Twitter
+- aixsatoshi/Chat-with-cosmopedia
 - xz56/react-llama
 - BeIR/hotpotqa
-- arcee-ai/agent-data
-language:
-- en
+- YBXL/medical_book_train_filtered
+- SkunkworksAI/reasoning-0.01
+- THUDM/LongWriter-6k
+- WhiteRabbitNeo/WRN-Chapter-1
+- WhiteRabbitNeo/Code-Functions-Level-Cyber
+- WhiteRabbitNeo/Code-Functions-Level-General
 ---
 # SpydazWeb AI React Project
 Quote for Motivation:
@@ -21,6 +92,54 @@ The SpydazWeb AI React Project was initiated to build advanced AI agents capable
 
 ## Training Methodology:
 
+
+# NEW PROMPTING METHOD DEPLOYED : 
+I have discovered that its possiblle to generatre graphs on the fly internallly within a model with a simple Prompt :slight_smile:
+
+here is an example in which i invoke the ReaCt Prompt Loop !
+```yal
+1. **Question**: {Insert user question here}
+2. **Thought**: Think step by step about how to approach this question.
+3. **Action**: Determine what action to take next:
+   - [Search]: Look for relevant information online.
+   - [Analyze]: Break down the problem into smaller parts.
+   - [Summarize]: Provide a summary of known facts related to the question.
+4. **Action Input**: Specify any details needed for the action.
+5. **Observation**: Describe what was found or learned from the action taken.
+
+Repeat steps 2-5 as necessary to refine your answer.
+
+6. **Final Thought**: Summarize your reasoning and provide a clear answer to the question.
+```
+
+In this prompt you will note an inner prompt !
+this is the prompt within the action !
+here we can state a methodology ad even a loop , so we can deploy a refiner in the loop or even a tester component : like so !
+```yaml
+1. **Question**: {Insert user question here}
+2. **Thought**: Think step by step about how to approach this question.
+3. **Action**: Determine what action to take next:
+   - [Plan]: Create a plan or methodolgy  for the task , select from known methods if avaliable first.
+   - [Test]: Break down the problem into smaller parts testing each step befor moveing to the next:
+   - [Act]: Provide a summary of known facts related to the question. generate full answere from sucessfull steps :
+4. **Action Input**: Specify any details needed for the action.
+5. **Observation**: Describe what was found or learned from the action taken.
+
+Repeat steps 2-5 as necessary to refine your answer.
+
+6. **Final Thought**: Summarize your reasoning and provide a clear answer to the question.
+```
+# TRSAINING METHODS:
+### STEP 1
+I often train the model with 500 samples MyTrainSet ! First !::: Then i overfit these samples ! to 0.001 :Here i train all heads ! and Gates!  Sometimes if the task is not accepting is switch to lm-head 
+### STEP 2
+Then i use the next bulk 5000 samples ! just to add Mass examples until they diverges to 0.3/4  : here i only trai the ATTENTION HEADS ONLY:
+this give the modle the attention required to solve all future tasks
+i dont not add more samples than 10k of a single task !
+### ONGOING !
+so i keep taining these samples while i train other methods : to keep these training sets in place ! ie the model stays aligned to the old sets while training the new: so you will see i used over 48 datasets they dont seem to be changing but they are :
+
+# Past Method : 
 ### Foundation Building:
 
 The initial phase involved training the model on binary yes/no questions without any explicit methodology. This was crucial in establishing a baseline for the model’s decision-making capabilities.