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Chapter B: Introduction to LLMs And Free LLM Resources
1. What Are LLMs (Large Language Models)?
Imagine a system that doesn’t just store information like a database, but can converse, summarize, translate, write code, and even reason through problems. That’s what an LLM (Large Language Model) does.
At the core of most modern LLMs is the Transformer architecture (Vaswani et al., 2017).
Unlike older models that processed text one word at a time, transformers look at whole sequences in parallel and figure out which words matter most to each other. Here are the essentials:
That’s the backbone: a stack of transformer blocks working together, with more layers = more power.
Want to dig deeper? Microsoft has a great on the same.
3. Types of LLMs
Hugging Face hosts 100,000+ models. Some are fully open, others are gated.
from huggingface_hub import login
login("YOUR_HF_TOKEN")
5. Running a Free LLM (Google AI Studio)
Instead of heavy Hugging Face models, you can start quickly with Google AI Studio → free API keys, fast responses.
Try it here:
Step 1: Get API Key
!pip install -q -U google-genai
from google import genai
# The client gets the API key from the environment variable `GEMINI_API_KEY`.
client = genai.Client(api_key="your_api_key")
response = client.models.generate_content(
model="gemini-2.5-flash", contents="Explain All about LLMS"
)
print(response.text)
Example Notebooks:
1)
1)
6. Free LLM Resources Table
Just pick a platform, follow the quickstart, and you can chat or code with an LLM in minutes!
7. Limitations of Free LLMs
1. What Are LLMs (Large Language Models)?
Imagine a system that doesn’t just store information like a database, but can converse, summarize, translate, write code, and even reason through problems. That’s what an LLM (Large Language Model) does.
- To a business owner: You can think of them as engines that can draft reports, analyze long documents, summarize meetings, or even generate marketing content at scale,cutting both cost and time.
- To a student or fresher: It’s helpful to imagine them as a much smarter autocomplete. They’ve been trained on massive datasets, so they can predict the “next word” in a way that feels surprisingly natural, whether you’re writing code, a paragraph, or even a story.
At the core of most modern LLMs is the Transformer architecture (Vaswani et al., 2017).
Unlike older models that processed text one word at a time, transformers look at whole sequences in parallel and figure out which words matter most to each other. Here are the essentials:
- Embeddings – Words (or tokens) are turned into numerical vectors that capture meaning.
- Positional Encoding – Adds information about word order (since transformers don’t read sequentially by default).
- Self-Attention – Each word decides which other words in the sentence it should pay attention to.
- Multi-Head Attention – Multiple attention mechanisms run in parallel, capturing different patterns (syntax, context, semantics).
- Feed-Forward Layers + Residuals – Nonlinear layers stacked deep, with shortcut connections to keep training stable.
- Output Layer – Predicts the most likely next token, repeating the process to generate full sentences.
That’s the backbone: a stack of transformer blocks working together, with more layers = more power.
Want to dig deeper? Microsoft has a great on the same.
3. Types of LLMs
- Decoder-only (GPT-style) → Text generation, chat, coding.
- Encoder-only (BERT-style) → Text classification, embeddings, search.
- Encoder-Decoder (T5/FLAN-style) → Translation, summarization, Q&A.
- Instruction-tuned models → Optimized for the following natural language prompts (e.g., Mistral-Instruct, Falcon-Instruct,Gemini).
Hugging Face hosts 100,000+ models. Some are fully open, others are gated.
- To use gated models like Mistral or LLaMA:
- Visit the model’s page (e.g., )
- Click “Access repository” and accept the license.
- Generate a Read token here →
- Authenticate in notebook:
from huggingface_hub import login
login("YOUR_HF_TOKEN")
5. Running a Free LLM (Google AI Studio)
Instead of heavy Hugging Face models, you can start quickly with Google AI Studio → free API keys, fast responses.
Try it here: Step 1: Get API Key
- Go to
- Generate a free API key.
- Copy it.
!pip install -q -U google-genai
from google import genai
# The client gets the API key from the environment variable `GEMINI_API_KEY`.
client = genai.Client(api_key="your_api_key")
response = client.models.generate_content(
model="gemini-2.5-flash", contents="Explain All about LLMS"
)
print(response.text)
Example Notebooks: 1)
1)
6. Free LLM Resources Table
- Free & Fun LLM Access for Students
- Free LLM Tools for Business Owners
- Hands-On & Learning (For All)
Just pick a platform, follow the quickstart, and you can chat or code with an LLM in minutes!
7. Limitations of Free LLMs
- Rate limits → Free APIs (Google AI, Hugging Face) restrict daily usage.
- Model size → Smaller free/open models may give weaker answers vs GPT-4/Gemini Pro.
- Latency → Free cloud GPUs can be slow (Colab queues, Hugging Face load times).
- Privacy → Using free APIs means your inputs may be logged. For sensitive use cases, local/offline models are safer.
Now that you know what LLMs are, how they work, and how to get free access, the next step is learning how to talk to them effectively — that’s where Prompt Engineering comes in.