--- 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 language: - en - sw - ig - so - es - ca - xh - zu - ha - tw - af - hi - bm - su license: apache-2.0 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 - 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 tags: - mergekit - merge - Mistral_Star - Mistral_Quiet - Mistral - Mixtral - Question-Answer - Token-Classification - Sequence-Classification - SpydazWeb-AI - chemistry - biology - legal - code - climate - medical - LCARS_AI_StarTrek_Computer - text-generation-inference - chain-of-thought - tree-of-knowledge - forest-of-thoughts - visual-spacial-sketchpad - alpha-mind - knowledge-graph - entity-detection - encyclopedia - wikipedia - stack-exchange - Reddit - Cyber-series - MegaMind - Cybertron - SpydazWeb - Spydaz - LCARS - star-trek - mega-transformers - Mulit-Mega-Merge - Multi-Lingual - Afro-Centric - African-Model - Ancient-One --- # Some Blurb ! # 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 : i have not listed the newer planning and reflexion datasets added : ### ALLIGNMENTS Hence i often find retrianing and alighment the most important parts hence the low numbers of samples : this also make the model more methodolgy oriented : answer problems with a method ! and thoughts and steps etc : ### SPEEDUP! i also when i can find data sets with single anwser also use these for speeedig up the model ! so it can be blunt if required ! and give a flat answer like a tool .. as it maybe required to functionn asa tooll wich juist returns the respnse ! i dont not train nfor multiple choices ! ie a,b,c,d answer as this spoils the model and it will not perform complexed answer in the future !( your basically teaching the model to guess 1/4 = 25% corerectness !) ALL models are UNLOCKED and UNRESTRICTED ! Although i do not train for lwednass or bad stuff the model could contain any type of request possible so its for you to apply the guard rails as you belive : as its important he actual model is not rstricted and the Harness used (GUI ) Restricts the model instead ! Guard Railing BLocks the model from defining the corerect response as well imposes the phylosophys of the guardrailer ... and not the model : so we have heard chat gpt be pro palestine or pro israel but i truth its the guardrails in conflict with the models knowledge base output ! Hence i belive in putting eveyr opinion in and letting th emodel do its own deciding on its opinion as its more intersertig ther possibilitys ! Especially with programming ansd coding : i don not need a model bringing me the same code and cannot innovate a neew idea ! this is what the model is for ! seeing patterns where humans fail to see ! ( we may call the hallucenations only because we cannot see the logic !) the model has answered questions which i could not possibly oknow the answrr too ! so its and effective team ! [](https://github.com/unslothai/unsloth)