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

Conversations with Grok (Part 1): Introducing Agile Transportation System

Me: What do you think of agile Transportation System? Grok : It seems like you might be referring to "Agile Transportation System." Did you mean a specific system, like a company, a concept, or something else? I’ll assume you’re asking about agile transportation in a general sense—like how agility applies to transportation systems—and go from there. If I’ve got it wrong, just let me know! Agile transportation typically refers to a flexible, responsive approach to moving goods or people. Think of it as a system that can quickly adapt to changes—whether that’s shifting demand, unexpected delays, or new tech. In practice, it’s about efficiency and speed without sacrificing reliability. For example, logistics companies might use real-time data to reroute trucks around traffic, or cities could tweak public transit schedules based on passenger patterns. It’s all about staying nimble in a world where disruptions—like weather, supply chain hiccups, or even pandemics—can throw a wrenc...
Read more

recipe: pinesang itlog (souped egg)

i love to eat and cook. so, i always stay in the kitchen to get the first taste on any food cooked by my mom. eventually, i learned some of them. and here's one: pinesang itlog ingredients: 5 thick slices ginger 2 cloves garlic 1/2 bulb onion, diced 5 tablespoon fish sauce (patis) 1-3 eggs pechay/dahong sibuyas half liter water directions: suate garlic till brown. add onion and ginger. pour fish sauce and let sizzle. add water. let it boil for 5 minutes. add the vegetables. let it boil for 2 minutes. add eggs and let boil for few minutes till the eggs harden. serve and enjoy!
Read more

Retrieval-Augmented Generation (RAG) Using First-Principles Thinking

Instead of just learning how Retrieval-Augmented Generation (RAG) works, let's break it down using First-Principles Thinking (FPT) —understanding the fundamental problem it solves and how we can optimize it. Step 1: What Problem Does RAG Solve? Traditional AI Limitations (Before RAG) Large Language Models (LLMs) like GPT struggle with: ❌ Knowledge Cutoff → They can’t access new information after training. ❌ Fact Inaccuracy (Hallucination) → They generate plausible but false responses. ❌ Context Limits → They can only process a limited amount of information at a time. The RAG Solution Retrieval-Augmented Generation (RAG) improves LLMs by: ✅ Retrieving relevant information from external sources (e.g., databases, search engines). ✅ Feeding this retrieved data into the LLM before generating an answer. ✅ Reducing hallucinations and improving response accuracy. Core Idea: Instead of making the model remember everything, let it look up relevant knowledge when needed....
Read more