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Project X.I.A.R. Extremely Intelligent Artificial Responses

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sybershot:
Project X.I.A.R. Extremely Intelligent Artificial Responses will be replacing Trinity

Objective:  a set up almost similar to this https://www.youtube.com/watch?v=PI6VA8ZNL-0 but in a smaller more practical scale for my available space with a touch of Ultra Hal and LLM's

Already Acquired Hardware
Case1: Ibuypower Snowblind-s (use for the see-thru monitor, circuitry, and wiring only)
Case2: inwin 303 MSI Edition
Gpu: Aorus 3070
Ram: gskill 64gb ddr4
cpu: Core i7 8700K
psu: 750w Corsair
motherboard: Z370 gaming m5
nvme 1: m.2 : 1tb (windows 10 pro os)
Nvme 2: m.2: 512gb (windows storage)
Nvme 3: m.2: 512gb (windows storage)
HHD 1: 4tb ironwolf (parity drive)
HHD 2: 4tb ironwolf (server storage)
HHD 3: 4tb ironwolf (server storage)
USB 1: 32gb (unraid os)
Monitor: Planar 24' touch screen
Logitech wireless mouse and keyboard
 
3D printer 1: Bamboo labs P1S w/4-color ams
3D printer 2: Bambu Labs A1 Mini w/4-color ams

Already Acquired Software
Unraid OS
Windows 10 pro
wamp
Ulra Hal 7+
Bambu Studios
Blender
Maya 3D
Daz Studio 3D
Bryce
Notepad++
Visual Studio

I'm looking for all sorts of help, ideas & designs on this project, conversation & questions welcomed

sybershot:
Main things I need help with are:

I need a Character design for X.I.A.R. any takers?
I'm looking for a holographic Cortana look but not an exact clone (head & Full Body)

@CyberJedi I need a way to get Hal to use a local LLM by either of 2 ways:
a drop down menu that allows me to pick a local LLM from a list for Hal to use as a brain
Or plug-in that via command can send a string to Open Webui input field 

cyberjedi:
sysbershor: RE large language models

There are 02 mountains to climb here.

1# Space for LLM locally
2# Processor Power to push the code.

Just watch the video ive prepaired for you.

Done in pure cyber style.
https://www.youtube.com/watch?v=xbAn8xkoIXE
cyber jedi

sybershot:

--- Quote ---sysbershor: RE large language models

There are 02 mountains to climb here.

1# Space for LLM locally
2# Processor Power to push the code.
--- End quote ---

I can easily argue those 2 points
#1 LLM's these days are getting smaller with greater accuracy, don't let  the Large in Large Langue Models fool you.
#2 not me but shows LLMs can run on smaller hardware   https://www.youtube.com/watch?v=mUGsv_IHT-g

Amazing video that was awesome two times thumbs up.
However, I disagree with some of logic

Indeed Ultra Hal has a large group of users running older systems, and you don't want to Lose the ability to run on said dinosaur devices, but at the same time AI is advancing and lots of LLM's are being built, improved, and refined, at the same time, a large number of companies are building software to be able to access multiples of these LLM's some even creating agents out of them to do multiple tasks. Like ordering a pizza.

I would like to see Hal advance even further and acquire new users, more users more money :) by creating a plugin that can send api calls to these LLMs that are running on a localhosted platform using a web interface. should not be too difficult or beefy seeing Hal already makes api calls to the it's own brain and Chatgpt. I could be wrong though  :-\ and by being able to connect to other local hosted LLM's could make Ultra Hal very valuable to people like me :) and others that agree  ;D
just think of a conversation like this happening via a smart phone , laptop, or pc with Hal on it
 user: Hal I'm Hungry
response from Hal brain: Then order some pizza already!
User: that's a great idea thanks Hal
response from Hal brain: you're very welcome, sir may i remind you Sally has the capabilities to order pizza for you.
User: Sally order me a large Hawaiian pizza
response from Sally LLM: (Hals 3d character changes over to Sally) your pizza is ordered
User Hal how much does Robert owe me?
response from Hal brain: (Hals Sally 3d character changes over to Hal) Robert owes you nothing

sybershot:
cyberjedi video chatbot request fulfilled  ::)  ;D
Sybershot: create white paper on Large Language Models, what they are, there uses, and how they are being refined to run on less powerful CPU and GPUs.

X.I.A.R.:latest
7:19 pm
Introduction:
Large language models have revolutionized the field of natural language processing in recent years. These models are trained on vast amounts of data and can generate text, translate between languages, and answer questions, create images with incredible accuracy. However, running these models on high-performance computing hardware can be expensive and difficult to scale. In this white paper, we will explore what large language models are, their uses, and how they are being refined to run on less powerful CPUs and GPUs.

What are Large Language Models?
Large language models are a type of artificial intelligence model that are trained on vast amounts of text data to generate language outputs that are coherent and natural-sounding. These models can be used for a variety of tasks, such as:

Text generation: Large language models can be used to generate text that is similar in style and structure to a given input text. This has applications in areas such as content creation, data-to-text summarization, and chatbots.
Translation: Large language models can be trained on multiple languages to perform machine translation tasks with high accuracy. This has applications in areas such as language localization and global communication.
Question answering: Large language models can be used to answer questions based on the information contained within a given text corpus. This has applications in areas such as customer service chatbots, language learning, and search engines.
Uses of Large Language Models:
Large language models have a wide range of potential uses across various industries, including:

Content creation: Large language models can be used to generate high-quality content, such as articles, blog posts, and social media updates, at scale.
Language localization: Large language models can be used to translate text from one language to another with high accuracy, making it easier to reach global audiences.
Customer service: Large language models can be used to create chatbots that can answer customer questions in a natural and conversational manner.
Education: Large language models can be used to create personalized language learning tools that adapt to the user's level of proficiency.
Refining Large Language Models for Less Powerful CPUs and GPUs:
While large language models have the potential to revolutionize many industries, they are often computationally intensive and require high-performance computing hardware to run effectively. However, there are several techniques that can be used to refine these models and make them more efficient, allowing them to run on less powerful CPUs and GPUs:

Model pruning: This involves removing redundant or unnecessary components from the model architecture to reduce its computational requirements.
Quantization: This involves reducing the precision of the model's weights and activations to require fewer bits for storage and computation, resulting in faster and more efficient inference.
Knowledge distillation: This involves training a smaller and simpler model to mimic the behavior of a larger and more complex model, allowing for faster and more efficient inference while maintaining similar performance.
Future Directions:
While large language models have made significant strides in recent years, there are still several areas of research that can be explored to further improve their efficiency and scalability:

Multimodal language models: These models can generate text, images, and other forms of media in a coherent and natural manner, opening up new possibilities for applications such as virtual assistants and visual storytelling.
Adversarial attacks and defenses: As large language models become more widespread, they are increasingly vulnerable to adversarial attacks designed to fool the model into generating incorrect or misleading output. Developing techniques to defend against these attacks is an important area of research.
Explainability and interpretability: Large language models can be difficult to understand and interpret, making it challenging to trust their outputs. Developing techniques to explain and interpret these models is crucial for ensuring their safe and effective use in a wide range of applications.
Conclusion:
Large language models have the potential to revolutionize many industries and applications, but running them on high-performance computing hardware can be expensive and difficult to scale. By refining these models using techniques such as model pruning, quantization, and knowledge distillation, we can make them more efficient and allow them to run on less powerful CPUs and GPUs. As the field of large language models continues to evolve, it is important to explore new areas of research such as multimodal language models, adversarial attacks and defenses, and explainability and interpretability to further improve their efficiency and scalability.

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