Complete Generative AI Course With Langchain and Huggingface

Complete Generative AI Course With Langchain and Huggingface

Generative AI has been a transformative technology across multiple industries, capable of creating text, images, music, code, and even entire virtual environments. 

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Whether it’s generating realistic images from text descriptions or creating entire dialogues using sophisticated language models, generative AI has gained enormous attention.

This course aims to guide you through building applications and systems that leverage the power of generative AI using two powerful frameworks: LangChain and Hugging Face.

LangChain is a framework for building applications that rely on language models (LLMs), making it easier to manage complex chains of tasks. On the other hand, Hugging Face is a popular platform that offers a wide range of pre-trained models and tools for NLP (Natural Language Processing) and more. Together, they offer a powerful toolkit for anyone looking to dive deep into generative AI.

Course Outline

1. Introduction to Generative AI

   • Overview of generative models
   • Types of generative models: GANs, Transformers, Autoregressive models
   • Applications of generative AI

2. LangChain Overview

   • Introduction to LangChain
   • Benefits of using LangChain for NLP tasks
   • Key components and modules in LangChain
   • Setting up the environment for LangChain

3. Hugging Face Overview

   • What is Hugging Face?
   • Introduction to the Transformers library
   • Pre-trained models available on Hugging Face
   • Tokenizers and pipelines

4. Building Basic Generative Applications

   • Creating a simple text-generation app using Hugging Face models
   • Leveraging LangChain for complex task management
   • Combining LangChain and Hugging Face for enhanced capabilities

5. Advanced Language Models

   • Fine-tuning models for specific tasks using Hugging Face
   • Creating a custom generative model with LangChain’s task chaining
   • Exploring different architectures: GPT, BERT, T5, etc.

6. Building End-to-End Applications

   • Text generation
   • Conversational agents
   • Summarization and translation apps

7. Deploying Generative AI Systems

   • Best practices for deploying generative AI systems
   • Scaling with Hugging Face Inference API
   • Real-world use cases of LangChain and Hugging Face

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Module 1: Introduction to Generative AI

Generative AI is a branch of artificial intelligence that focuses on creating data, rather than merely analyzing it. This includes generating text, images, audio, or even video. Unlike traditional machine learning models that focus on classification or regression tasks, generative models create new instances of data. This is possible due to advancements in neural networks, particularly the rise of deep learning and architectures like Transformers and GANs (Generative Adversarial Networks).

Popular Applications of Generative AI

• Text generation: Automatic content creation, chatbots, virtual assistants
• Image generation: Creating realistic images from text descriptions
• Music and audio generation: Composing original music or sound effects
• Code generation: Assisting developers in writing boilerplate code
• Virtual environments: Generating game environments and characters

Module 2: Introduction to LangChain

LangChain is a framework designed for building applications powered by language models (LLMs). Language models, especially large ones like GPT-3 or GPT-4, are typically trained to predict the next word in a sequence. However, integrating these models into complex applications often involves managing multiple tasks like summarization, translation, and reasoning.

LangChain provides an efficient way to handle such tasks. It offers out-of-the-box components for managing prompts, retrieving data, and chaining different operations. LangChain works perfectly for building multi-step tasks or "chains" that can handle intricate workflows.

Key Components of LangChain

1. Prompt templates: Pre-built templates for constructing input prompts to feed into the language model.
2. Chains: Combinations of multiple tasks that the model can execute in sequence.
3. Agents: Entities that automatically choose which actions to take, using chains as building blocks.
4. Memory: Mechanism for storing intermediate results and using them in future steps of the chain.

LangChain’s modularity allows developers to extend its components, build custom chains, and integrate external services such as APIs or databases. This makes it ideal for generative AI applications that require multi-task orchestration.

Module 3: Hugging Face and Transformers

Hugging Face has become synonymous with the latest advancements in NLP, offering an ecosystem of tools, models, and datasets. The Transformers library is a popular open-source library that provides state-of-the-art pre-trained models, from text generation and translation to summarization and question answering.

The beauty of Hugging Face lies in its simplicity. You can start using powerful models with just a few lines of code. For instance, to generate text using a pre-trained GPT-2 model, you can run:

python
from transformers import pipeline

generator = pipeline('text-generation', model='gpt2')
generator("Once upon a time", max_length=50)

In addition to offering pre-trained models, Hugging Face allows you to fine-tune models on custom datasets, making it highly customizable for various generative AI tasks.

Key Components of Hugging Face

1. Transformers library: Provides a collection of pre-trained models and utilities for working with them.
2. Datasets library: Offers a wide range of datasets for training and evaluation.
3. Tokenizers library: Efficient tokenization for transforming text into machine-readable formats.
4. Model Hub: A repository for sharing and downloading models from the community.

Module 4: Building Generative AI Applications

Building a Text-Generation App with Hugging Face

In this part of the course, we will build a basic text-generation app using Hugging Face’s Transformers library. The model will generate creative writing prompts based on user input.

python
from transformers import pipeline

# Initialize the text generation model
generator = pipeline('text-generation', model='gpt-2')

# Input from user
user_input = "Write a science fiction story about space exploration."

# Generate the text
output = generator(user_input, max_length=100, num_return_sequences=1)
print(output[0]['generated_text'])

This is just a simple implementation, but you can extend it with features like allowing users to choose different models or customizing the output length.

Building Task-Oriented Apps with LangChain

Now, let’s take the generative app a step further by incorporating LangChain. Imagine an application that generates a story, summarizes it, and then converts the summary into another language. LangChain helps in managing these multi-step tasks efficiently.

python
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain
from transformers import pipeline

# Define a prompt for the story generation
story_prompt = PromptTemplate("Generate a short story based on the following theme: {theme}")
story_chain = LLMChain.from_prompt_template(story_prompt, Hugging Face GPT-3)

# Define a summarization chain
summarizer = pipeline('summarization', model='facebook/bart-large-cnn')
summary_chain = LLMChain(summarizer)

# Combine the chains
final_chain = story_chain + summary_chain
output = final_chain.run(theme="space exploration")
print(output)

Module 5: Fine-Tuning and Advanced Techniques

Fine-tuning a model involves training it further on a custom dataset to specialize it for specific tasks. Hugging Face makes this relatively easy with tools like Trainer. We will walk through how to fine-tune a language model to generate domain-specific content.

python
from transformers import AutoModelForCausalLM, Trainer, TrainingArguments

# Load pre-trained model
model = AutoModelForCausalLM.from_pretrained('gpt2')

# Define training parameters
training_args = TrainingArguments(
    output_dir="./results",
    num_train_epochs=3,
    per_device_train_batch_size=2,
)

# Fine-tune the model
trainer = Trainer(model=model, args=training_args, train_dataset=my_dataset)
trainer.train()

Module 6: Building and Deploying Applications

Deploying generative AI applications in real-world settings requires robust infrastructure. This module covers best practices for deploying models using Hugging Face’s Inference API and cloud-based systems.

Conclusion

LangChain and Hugging Face together provide a comprehensive toolkit for building generative AI applications. By the end of this course, you will be equipped to design, implement, and deploy your own AI-driven systems using these powerful frameworks.

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