Listen to this Post
If you want to truly understand how Large Language Models (LLMs) like ChatGPT work without diving into complex academic papers or advanced mathematics, Andrej Karpathy’s in-depth video is the ultimate resource. This 3-hour and 31-minute masterclass breaks down the inner workings of LLMs in an accessible way.
🔗 Watch the full video here: https://www.youtube.com/watch?v=7xTGNNLPyMI&t=1392s
For those who prefer a condensed version, NeoSage’s newsletter provides distilled insights from this talk and other foundational resources.
🔗 Subscribe to NeoSage: https://blog.neosage.io
You Should Know: Key Commands & Practical Steps for Working with LLMs
To experiment with LLMs locally or in the cloud, here are some essential commands and steps:
1. Setting Up a Local LLM Environment
If you want to run an open-source LLM like LLaMA or GPT-2 locally:
Install Python and required libraries
sudo apt update && sudo apt install python3 python3-pip -y
pip3 install torch transformers sentencepiece
Download and run a small GPT-2 model
python3 -c "from transformers import pipeline; generator = pipeline('text-generation', model='gpt2'); print(generator('Hello, how are you?', max_length=50))"
2. Fine-Tuning an LLM with Custom Data
For those looking to train or fine-tune an LLM:
Install Hugging Face’s datasets library
pip3 install datasets
Example fine-tuning script (simplified)
python3 -c "
from transformers import GPT2Tokenizer, GPT2LMHeadModel, Trainer, TrainingArguments
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
model = GPT2LMHeadModel.from_pretrained('gpt2')
training_args = TrainingArguments(output_dir='./results', per_device_train_batch_size=2)
trainer = Trainer(model=model, args=training_args)
trainer.train()
"
3. Deploying an LLM API with FastAPI
To create a simple API for text generation:
Install FastAPI and Uvicorn
pip3 install fastapi uvicorn
Create a basic API (save as <code>api.py</code>)
from fastapi import FastAPI
from transformers import pipeline
app = FastAPI()
generator = pipeline('text-generation', model='gpt2')
@app.get("/generate")
def generate_text(prompt: str):
return generator(prompt, max_length=100)
Run the API
uvicorn api:app --reload
4. Monitoring GPU Usage for LLM Training
If you’re running LLMs on a GPU:
Check NVIDIA GPU stats nvidia-smi Monitor system resources htop
What Undercode Say
Understanding LLMs goes beyond theory—applying them practically is key. Whether you’re running inference on a local machine, fine-tuning models, or deploying APIs, hands-on experimentation solidifies knowledge.
🔹 Key Linux/Windows Commands for LLM Workflows:
- Linux:
nvidia-smi,htop, `pip install transformers` - Windows (WSL):
wsl --install, `python -m pip install torch` - Cloud (AWS/GCP):
gcloud compute instances create, `aws s3 cp`
🔹 Expected Output:
A fully functional LLM setup, from local experimentation to API deployment, enabling deeper learning and real-world application.
🔗 Further Reading:
References:
Reported By: Shivanivirdi If – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
