Skip to main content

Wrestling with an Old Acer Laptop to Install ALBERT—And Winning!



You know that feeling when you take an old, battle-worn laptop and make it do something it was never meant to handle? That’s exactly what we did when we decided to install ALBERT (A Lite BERT) on an aging Acer laptop. If you’ve ever tried deep learning on old hardware, you’ll understand why this was part engineering challenge, part act of stubborn defiance.

The Challenge: ALBERT on a Senior Citizen of a Laptop

The laptop in question? A dusty old Acer machine (N3450 2.2 GHz, 4gb ram), still running strong (well, kind of) but never meant to handle modern AI workloads. The mission? Get PyTorch, Transformers, and ALBERT running on it—without CUDA, because, let’s be real, this laptop’s GPU is more suited for Minesweeper than machine learning.

Step 1: Clearing Space (Because 92% Disk Usage Ain’t It)

First order of business: making room. A quick df -h confirmed what we feared—only a few gigabytes of storage left. Old logs, forgotten downloads, and unnecessary packages were sent to digital oblivion. We even had to allocate extra space to /tmp just to prevent massive .whl files from failing mid-download.

Step 2: Installing PyTorch and Transformers (Not Without a Fight)

Installing PyTorch should have been easy, but nope. The first attempt ended with a familiar [Errno 28] No space left on device error. After a bit of cursing and some clever pip --no-cache-dir installs, we finally got PyTorch 2.6.0+cu124 up and running—minus CUDA, of course.

Next up: Transformers. This should have been smooth sailing, but Python had other plans. Running our import transformers test script threw a ModuleNotFoundError. Turns out, sentencepiece (a required dependency) didn’t install correctly. The culprit? Failed to build installable wheels for some pyproject.toml based projects (sentencepiece).

We switched gears, manually installed sentencepiece, and—drumroll—it finally worked! At this point, the laptop had already earned a medal for resilience.

Step 3: Running ALBERT on CPU (The Moment of Truth)

With everything installed, it was time for the grand test:

from transformers import AlbertTokenizer, AlbertModel
import torch

tokenizer = AlbertTokenizer.from_pretrained("albert-base-v2")
model = AlbertModel.from_pretrained("albert-base-v2")

text = "This old Acer laptop is a legend."
inputs = tokenizer(text, return_tensors="pt")
output = model(**inputs)

print(output.last_hidden_state)

Watching the model download and process our test sentence felt like a scene from an underdog sports movie. Would it crash? Would it catch fire? Would it just refuse to work? None of the above! ALBERT, against all odds, successfully generated embeddings for our text.

Final Thoughts: A Victory for Old Hardware

The takeaway? You don’t need cutting-edge hardware to experiment with AI. Sure, this setup won’t be training billion-parameter models anytime soon, but for learning, testing, and small-scale experimentation, it’s proof that old machines still have some life left in them.

So, if you have an aging laptop lying around, give it a second chance. It might just surprise you. And if it doesn’t, well… at least you tried. 😉

Labels:

Popular

Understanding Large Language Models (LLMs) Using First-Principles Thinking

Instead of memorizing AI jargon, let’s break down Large Language Models (LLMs) from first principles —starting with the most fundamental questions and building up from there. Step 1: What is Intelligence? Before we talk about AI, let’s define intelligence at the most basic level: Intelligence is the ability to understand, learn, and generate meaningful responses based on patterns. Humans do this by processing language, recognizing patterns, and forming logical connections. Now, let’s apply this to machines. Step 2: Can Machines Imitate Intelligence? If intelligence is about recognizing patterns and generating responses, then in theory, a machine can simulate intelligence by: Storing and processing vast amounts of text. Finding statistical patterns in language. Predicting what comes next based on probability. This leads us to the core function of LLMs : They don’t think like humans, but they generate human-like text by learning from data. Step 3: How Do LLMs Wor...
Read more

Contextual Stratification - Chapter 8: Scales

  The Microscope Analogy Imagine looking at a painting. Stand close, inches from the canvas and you see individual brushstrokes, texture, the physical application of paint. Step back a few feet, and you see the image: a face, a landscape, a composition. Step back further, across the room, and you see how the painting relates to its frame, the wall, the space it occupies. Step back outside the building, and the painting disappears entirely into the larger context of the museum, the city, the culture. Same painting. Different scales of observation. And at each scale, different features become visible while others disappear. The brushstrokes that dominated up close are invisible from across the room. The composition that emerged at medium distance fragments into meaningless marks up close. Neither view is "wrong". They're both accurate descriptions of what's observable at that scale. This is what scale means in contextual stratification: the resolution of observation, th...
Read more

Contextual Stratification - Chapter 6: A Different Possibility

The Uncomfortable Question We've spent five chapters documenting a pattern: frameworks work brilliantly within their domains, then break down at boundaries. Physics, economics, psychology, medicine, mathematics; everywhere we look, the same story. We've examined why the standard explanations fail to account for this pattern. Now we must ask the question that makes most scientists uncomfortable: What if the boundaries are real? Not artifacts of incomplete knowledge. Not gaps waiting to be filled. Not temporary inconveniences on the road to unified understanding. What if reality itself is genuinely structured into domains, each operating under different rules, each requiring different frameworks to understand? This is not the answer we want. We want unity. We want simplicity. We want one elegant equation that explains everything from quarks to consciousness. The history of science seems to promise this; each generation unifying more, explaining more with less, moving toward that ...
Read more