5 essential tools and frameworks for getting started with Large Language Models (LLMs):

4 min readNov 28, 2024

When I first started working with Large Language Models (LLMs), I was both excited and overwhelmed. The potential of these models to generate text, summarize documents, or even translate languages was mind-blowing — but where to begin?

That’s when I discovered a handful of Python libraries that made experimenting with LLMs approachable and fun. These libraries didn’t just help me build cool applications — they became my bridge to understanding the true potential of LLMs.

Let me walk you through the five libraries that shaped my early journey with LLMs.

1. Hugging Face Transformers: The Gateway to LLMs

If you’re working with LLMs, Hugging Face is the starting point. Their transformers library provides pre-trained models like BERT, GPT, and T5 with a simple, beginner-friendly API.

With just a few lines of code, I could:

Load models like GPT-2 or BERT for text generation and classification.
Fine-tune pre-trained models on my own datasets.
Tokenize text with utilities that handled the complex preprocessing under the hood.

What I loved most was the community support. The Hugging Face Hub is a treasure trove of pre-trained models that saved me hours of work.

2. LangChain: Building LLM-Powered Applications

After mastering the basics, I wanted to build more complex workflows with LLMs. That’s where LANGCHAIN came in. This library made it easy to connect LLMs with other tools and data sources, enabling me to create intelligent pipelines.

Here’s what LangChain helped me with:

Building conversational agents with memory, so they remembered the context of previous interactions.
Combining LLMs with APIs or custom databases for question-answering systems.
Creating structured workflows where different components (e.g., prompt engineering, retrieval) worked seamlessly together.

LangChain truly unlocked my ability to move from playing with LLMs to building real-world applications.

3. OpenAI API: The Power of GPT at Your Fingertips

The OpenAI gave me access to models like GPT-3 and GPT-4 — powerful tools for generating human-like text. What stood out was how straightforward it was to use.

With just an API key, I could:

Generate creative content like blog posts or stories.
Summarize lengthy documents in seconds.
Build chatbots capable of natural conversations.

Even as I explored more advanced libraries, the OpenAI API remained my go-to for quick prototyping.

4. Sentence Transformers: For Semantic Search and Embeddings

While working with LLMs, I quickly realized the importance of @embedding — dense vector representations of text that LLMs generate. That’s where Sentence Transformers came to the rescue.

Using this library, I could:

Create embeddings for sentences and paragraphs for tasks like semantic search.
Cluster text documents based on similarity.
Build recommendation systems using cosine similarity.

Sentence Transformers brought the “understanding” aspect of LLMs to life, showing me how models could grasp not just text but meaning

5. FastAPI: Deploying LLM Applications with Ease

Once I started building LLM-based tools, I needed a way to share them with others. Fastapi was the perfect library for creating lightweight, high-performance APIs.

With FastAPI, I could:

Wrap my LLM workflows into REST APIs.
Deploy applications like chatbots, text summarizers, or custom NLP tools.
Integrate easily with frontend frameworks to create user-friendly interfaces.

FastAPI made deploying my projects a breeze, allowing me to showcase what LLMs can do.

Looking Back

These five libraries — Hugging Face, LangChain, OpenAI API, Sentence Transformers, and FastAPI — transformed how I approached LLMs. They helped me go from curiosity to competence, allowing me to build applications that truly showcased the power of these models.

If you’re just starting with LLMs, I can’t recommend these libraries enough. They’re intuitive, well-documented, and backed by strong communities, making your learning curve much smoother.

What tools or libraries have been your favorites when working with LLMs? Let’s share and inspire others in the comments!

If you found this post helpful, feel free to share it with anyone exploring the world of Large Language Models. Together, we can unlock the full potential of AI.

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