Ram – BGPTrace

Model Training on AMD 16-core CPU with 8GB RAM running in a virtual machine for Bitcoin Price Prediction – Part 2 – Updated

Continuing with Over 500,000+ Data Points for Bitcoin (BTC) Price Prediction

Using the Python program, the first method I tried was SVR (Support Vector Regression) for prediction. However… how many steps should I use for prediction? 🤔

Previously, I used a Raspberry Pi 4B (4GB RAM) for prediction, and… OH… 😩
I don’t even want to count the time again. Just imagine training a new model on a Raspberry Pi!

So, I switched to an AMD 16-core CPU with 8GB RAM running in a virtual machine to perform the prediction.

60 steps calculation: Took 7 hours 😵
120 steps: …Man… still running after 20 hours! 😫 Finally !!! 33 Hours

Do I need an M4 machine for this? 💻⚡

ChatGPT provided another approach.
OK, let’s test it… I’ll let you know how it goes! 🚀

🧪 Quick Example of More Time Steps Effect

Time Step (X Length)	Predicted Accuracy	Notes
30	⭐⭐⭐	Quick but less accurate for long-term trends.
60	⭐⭐⭐⭐	Balanced context and performance.
120	⭐⭐⭐⭐½	Better for long-term trends but slower.
240	⭐⭐	Risk of overfitting and slower training.

#SVR #Prediction #Computing #AI #Step #ChatGPT #Python #Bitcoin #crypto #Cryptocurrency #trading #price #virtualmachine #vm #raspberrypi #ram #CPU #CUDB #AMD #Nvidia

Install and Run OLLAMA on Linux Machine

So many tech guys already share how to install the OLLAM. I wont say too details. Just a brief step for you.

Prepare a Machine with good GPU, CPU and > 16G RAM. (Raspberry Pi can run with the Deepseek 1.5B, other………. Please chec my last post)
Install update your linux repos.
sudo apt-get update -y sudo apt-get upgrade -y
Install Ollam by the follow command
curl -fsSL https://ollama.com/install.sh | sh
Run the LLM model, if you wont have the model at your machine, it will be download automatically.
ollama run <model> e.g.: ollama run deepseek-r1:8b
The model will be downloaded to /usr/share/ollama/.ollama/models/
what model you can run? Check here
https://ollama.com/search
OLLAMA command line is a little bit similar with Docker, check this.

PS: You also can install OLLAMA at WINDOWS, please also check OLLAMA website.

Lets try your own AI locally!

#OLLAMA #Model #AI #CPU #GPU #CUDB #RAM #RaspberryPI #Docker

Raspberry Pi 4b 4G Ram with Deepseek 8b….

Answer is ……. Fail………..

I cannot run Deepseek 8b on my Raspbeery Pi 4b 4G Ram version…….

OLLAMA keeps loading and crash and load again….. Suspect the storage full…..

Anyone?

2025-02-06

Hooray!!! Model directory MAP to share drive.

ERROR!!!!!!!! Not enough Memory!!!!!!!!!!

#AI #deepseek #raspbeerypi #R1-8B #memory #ram #ERROR

Deepseek 1.5b vs 8b version

Well, we all expect that 1.5b and 8b may have a different of AI’s knowledge.

We made a test,
1. 1.5b we are on Raspberry PI 4B 4G Ram.,
2. 8b on virtual machine with AMD Radeon and 16G Ram on Ubuntu.

We only ask a question.

“what is the difference between you and chatGPT”

1. 1.5b versions

2. 8b versions

The knowledge base of course 8b will be better. However, we will most concern of the resource usage. Can Raspberry PI CPU base can process this efficient?

#deepseek #AI #CPU #raspberrypi #GPU #nvidia #CUDB #AMD

Deepseek on Raspberry PI?????

Tech guys are interested in how AI and LLM model processing on an IOT, low power devices such as Raspberry PI.

But??!!!!

NO GPU!!!!!!!!!!!!

How to run the AI model????

OK, We dont want to talk about how to install and run on OLLAMA.

We have tried on 1.5b version of Deepseek on our PI4 4G RAM device.

Amazing that it works! However, you cannot expect the response time and token would be good enough for fast response.

By this kind of success, we can imagine that more other model can be running on CPU based IOT device. Therefore, will the home assistant widely adopt?

Let see……………….