ths model will be my archive : Used for Text Recall: the archive series sits on top of a deep thinking mind!!!

we need to be able to upload information into the model and retrieve the data verbatum as well as enable the model to interogate that data: hence the archive:
Ie recall any infromation about John brown! or recall any information held about hugging face :: ... we would like to return the data held inside the model for this query!! verbatum and not sumarized in conversation :

The most important addition to this model as well as role play characters ; and profiles and scenarios : Is TRANSFORMERS!!! Now if asked to write a function to intiate a trasnfromer model etc he has the internal knowledge to determine the data:

I have personally found that if you truly need the model to be a full knowledge base on a particuar topic :

1: Firsg upload via simple tet dumping the manuals and information in flat format as a text dump : ie the bible ! 2: Upload any dictionary data for these models ; including synonyms; keyword associtions : so create a prompt to look up a name and return the data in a formatted way such as markdown, html, as an app etc: so create many tasks based around this topic: 3: begin entering discusions and coversations regarding the topic (ie usse another model to take on the perspective of the data and antoher to be the student or challenger or seeker ) to produce a dataset of conversations: 3: Begin instruct and response completions as well as creating any response chains and calculations: at this point the model will be able to handle many types of querys as well as querys not programmed: for this knowledge : as well as think about the topic from the various perspective chosen for conversive chats : 4: here again we begin dumping data with a prompt to recall specific subsets of important information : so that the model retains larger chunks of information as important: (due to the prompt to ask it to rememeber) ....as previously with dumping the probablitys ogf the data were create and when the chat was installed , the data was given reasoning and when the task was installed it was given the chain of thoughts : hence whole pattern of domain information:with the bible corpuses , its important to train with translations: ie translate this text to english and supply the spanish version of the text hence also giving the model the multi lingugal aspects:

If you choose to translate from english the modle outputs can become that languge so i suggest to use the forign text as an input : at the same time it learns the text: Simularly to code fragments : if possible multiu versions of the code should be used , or even markdown pages to html pages : or translate this list to json : these functions enable for later operations in which prompts are used via rag systems to extract these tasks : at the same time of training the model can learn the functionality and the data contained within:

Personally most of my training was done via instruct/chat ml,/ various custom prompts : and now i also wish i had concentrated more on the dumping of data!!... Its also as previously mentioned in other models to :variate the lora configs with each fine tuning as you can become accustomed to using the same config !!!! hence rewriting previous work ! as you should know lora has a direct relationship to the amount of parameters tuned .... so for ew tasks a large lora (with r256) and quick tuning less parameters (4,8,16); hence when tuning a model you should know if your in pretraining stage you need to open as many tensors as possible and for fine tuning only a specific subset, for repair just the targeted layers exposed by the anyalasis harness: (hence no need for DPO!! (just remove those layers by training them in the lora and merging the layers into thier places))

So the importance of ARCHIVING!::: yes we need to drop more data into the models FASTER! : we shuld not need to structure all data ! the model will find a way(well seasoned models)

so text generation is the task the model was trained for so as much tet or code as possible to dump in:::: (Pretraining) allows for the model to learn later tasks (using what it learn from the text gen task!!!!), if your model is not training .... Not enough data was dumped in as plain text for next word generation !! hence changing the task : only after the odel is generating sucess full random text can we begin to train it for real on chat and response and after that begins to function (ie hello , oh hi how are you?! then you can instruct the model !!: then you can customie it also !

my next seris after this will also focus on a model specifcally purposed to be STAR TREK COMPUTER!! >>>> I would also like to incorpertate the ability to runscripts from the model (ie execute a script file on the os!) for its generations it will ask to execute then it can be given access to the function of execution : or writing a script , saving it then executing it , then deleting it....( this would go on top of the generation: as it would detect the function call in the generation , if enabled it wwill auto execute if not it will present the argument, if no funtion call is detected then it will need to continue its generation without the call ! So it will need a Function calling HEAD!instead of Open interpretor!: These things should be a PLUGIN LAYER) we need to devise the plugin layer system to allow the model to use the lora to ADD it as a head or NEW LAYERS as even for difusion we only need a few new layers to succeed! (not a whole model we should reuse the model components we already have !!)

(find a balance)

LeroyDyer/Mixtral_AI_MasterMind_TheArchive-Q4_K_S-GGUF

This model was converted to GGUF format from LeroyDyer/Mixtral_AI_MasterMind_TheArchive using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

Use with llama.cpp

Install llama.cpp through brew.

brew install ggerganov/ggerganov/llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

llama-cli --hf-repo LeroyDyer/Mixtral_AI_MasterMind_TheArchive-Q4_K_S-GGUF --model mixtral_ai_mastermind_thearchive.Q4_K_S.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo LeroyDyer/Mixtral_AI_MasterMind_TheArchive-Q4_K_S-GGUF --model mixtral_ai_mastermind_thearchive.Q4_K_S.gguf -c 2048

Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.

git clone https://github.com/ggerganov/llama.cpp &&             cd llama.cpp &&             make &&             ./main -m mixtral_ai_mastermind_thearchive.Q4_K_S.gguf -n 128