large language model

Data Science Dojo Large Language Models Bootcamp

The Data Science Dojo Large Language Models Bootcamp is a 5-day in-person bootcamp that teaches you everything you need to know about large language models (LLMs) and their real-world applications.

Link to Bootcamp -> Large Language Models Bootcamp 

Test your large language models and generative AI knowledge

Key topics covered:

• Generative AI and LLM Fundamentals
• A comprehensive introduction to the fundamentals of generative AI, foundation models and Large language models
• Canonical Architectures of LLM Applications
• An in-depth understanding of various LLM-powered application architectures and their relative tradeoffs
• Embeddings and Vector Databases with practical experience
• Prompt Engineering with practical experience
• Orchestration Frameworks: LangChain and Llama Index with practical experience
• Deployment of LLM Applications
• Learn how to deploy your LLM applications using Azure and Hugging Face cloud
• Customizing Large Language Models
• Practical experience with fine-tuning, parameter efficient tuning and retrieval parameter-efficient + retrieval-augmented approaches
• Building An End-to-End Custom LLM Application
• A custom LLM application created on selected datasets

 

Instructor details:

The instructors at Data Science Dojo are experienced experts in the fields of LLMs and generative AI. They have a deep understanding of the theory and practice of LLMs, and they are passionate about teaching others about this exciting new field.

This bootcamp offers a comprehensive introduction to getting started with building a ChatGPT on your own data. By the end of the bootcamp, you will be capable of building LLM-powered applications on any dataset of your choice.

 

Location and duration:

The Data Science Dojo LLM Bootcamp has been held in Seattle, Washington D.C and Austin. The upcoming Bootcamp is scheduled in Seattle for Jan 29th – Feb 2nd, 2024. The large language model bootcamp lasts for 5 days. It is a full-time bootcamp, so you can expect to spend 8-10 hours per day learning and working on projects.

Cost:

The Data Science Dojo LLM Bootcamp costs $3,499. There are a number of scholarships and payment plans available.

Prerequisites:

There are no formal prerequisites for the Data Science Dojo LLM Bootcamp. However, it is recommended that you have some basic knowledge of programming and machine learning.

Who should attend?

The Data Science Dojo LLM Bootcamp is ideal for anyone who is interested in learning about LLMs and building LLM-powered applications. This includes software engineers, data scientists, researchers, and anyone else who wants to be at the forefront of this rapidly growing field.

Application process:

To apply for the Data Science Dojo LLM Bootcamp, you will need to complete an online application form here.

AI Planet’s LLM Bootcamp

• Key topics covered: This bootcamp is structured to provide an in-depth understanding of large language models (LLMs) and generative AI. Students will start with the basics and gradually delve into advanced topics. The curriculum encompasses:
  1. Building your own LLMs
  2. Fine-tuning existing models
  3. Using LLMs to create innovative applications
• Duration: 7 weeks, August 12–September 24, 2023.
• Location: Online—Learn from anywhere!
• Instructors: The bootcamp boasts experienced experts in the field of LLMs and generative AI. These experts bring a wealth of knowledge and real-world experience to the classroom, ensuring that students receive a hands-on and practical education. Additionally, the bootcamp emphasizes hands-on projects where students can apply what they’ve learned to real-world scenarios.

• Who should attend: The AI Planet LLM Bootcamp is ideal for anyone who is interested in learning about LLMs AI. This includes software engineers, data scientists, researchers, and anyone else who wants to be at the forefront of this rapidly growing field.

For a prospective student, AI Planet’s LLM Bootcamp offers a comprehensive education in the domain of large language models. The combination of experienced instructors, a hands-on approach, and a curriculum that covers both basics and advanced topics makes it a compelling option for anyone looking to delve into the world of LLMs and AI.

Xavor Generative AI Bootcamp

The Xavor Generative AI Bootcamp is a 3-month online bootcamp that teaches you the skills you need to build and deploy generative AI applications. You’ll learn about the different types of generative AI models, how to train them, and how to use them to create innovative applications.

Link to Bootcamp -> Xavor Generative AI Bootcamp

Key topics covered:

• Introduction to generative AI
• Different types of AI models
• Training and deploying AI models
• Building AI applications
• Case studies of generative AI applications in the real world

Instructor details:

The instructors at Xavor are experienced practitioners in the field of generative AI. They have a deep understanding of the theory and practice, and they are passionate about teaching others about this exciting new field.

Location and duration:

The Xavor Generative AI Bootcamp is held online and lasts for 3 months. It is a part-time bootcamp, so you can expect to spend 4-6 hours per week learning and working on projects.

Cost:

The Xavor Bootcamp is free.

Prerequisites:

There are no formal prerequisites for the Xavor Bootcamp. However, it is recommended that you have some basic knowledge of programming and machine learning.

Who should attend:

The Xavor Bootcamp is ideal for anyone who is interested in learning about generative AI and building its applications. This includes software engineers, data scientists, researchers, and anyone else who wants to be at the forefront of this rapidly growing field.

Application process:

To apply for the Xavor Generative AI Bootcamp, you will need to complete an online application form. The application process includes a coding challenge and a video interview.

 

Full Stack LLM Bootcamp

The Full Stack Deep Learning (FSDL) LLM Bootcamp is a 2-day online bootcamp that teaches you the fundamentals of large language models (LLMs) and how to build and deploy LLM-powered applications.

Link to Bootcamp -> Full Stack LLM Bootcamp

Key topics covered:

• Introduction to LLMs
• Natural language processing (NLP)
• Machine learning (ML)
• Deep learning
• TensorFlow
• Building and deploying LLM-powered applications

Instructor details:

The instructors at FSDL are experienced experts in the field of LLMs and generative AI. They have a deep understanding of the theory and practice of LLMs, and they are passionate about teaching others about this exciting new field.

Location and duration:

The FSDL LLM Bootcamp is held online and lasts for 2 days. It is a full-time bootcamp, so you can expect to spend 8-10 hours per day learning and working on projects.

Cost:

The FSDL LLM Bootcamp is free.

Prerequisites:

There are no formal prerequisites for the FSDL LLM Bootcamp. However, it is recommended that you have some basic knowledge of programming and machine learning.

Who should attend?

The FSDL LLM Bootcamp is ideal for anyone who is interested in learning about LLMs and building LLM-powered applications. This includes software engineers, data scientists, researchers, and anyone else who wants to be at the forefront of this rapidly growing field.

Application process:

There is no formal application process for the FSDL LLM Bootcamp. Simply register for the bootcamp on the FSDL website.

AI & Generative AI Bootcamp for end users course overview

The Generative AI Bootcamp for End Users is a 90-hour online bootcamp offered by Koenig Solutions. It is designed to teach beginners and non-technical professionals the fundamentals of artificial intelligence (AI) .

Link to Bootcamp -> Generative AI Bootcamp

Key topics covered:

• Introduction to AI
• Machine learning
• Deep learning
• Natural language processing (NLP)
• Computer vision
• Generative adversarial networks (GANs)
• Diffusion models
• Transformers
• Practical applications of AI

Instructor details:

The instructors at Koenig Solutions are experienced industry professionals with a deep understanding of generative AI. They are passionate about teaching others about this rapidly growing field and helping them develop the skills they need to succeed in the AI workforce.

Location and duration:

The Bootcamp for End Users is held online and lasts for 90 hours. It is a part-time bootcamp, so you can expect to spend 4-6 hours per week learning and working on projects.

Cost:

The Generative AI Bootcamp for End Users costs $999. There are a number of scholarships and payment plans available.

Prerequisites:

There are no formal prerequisites for the Generative AI Bootcamp for End Users. However, it is recommended that you have some basic knowledge of computers and the internet.

Who should attend?

The AI & Generative AI Bootcamp for End Users is ideal for anyone who is interested in learning about AI and generative AI, regardless of their technical background. This includes business professionals, entrepreneurs, students, and anyone else who wants to gain a competitive advantage in the AI-powered world of tomorrow.

Application process:

To apply for the AI & Generative AI Bootcamp for End Users, you will need to complete an online application form. The application process includes a short interview.

Additional information:

This Bootcamp for End Users is a certification program. Upon completion of the bootcamp, you will receive a certificate from Koenig Solutions that verifies your skills in AI and generative AI.

The bootcamp also includes access to a variety of resources, such as online lectures, tutorials, and hands-on projects. These resources will help you solidify your understanding of the material and develop the skills you need to succeed in the AI workforce.

Which LLM bootcamp will you join?

Generative AI is being used to develop new self-driving car algorithms, to create personalized medical treatments, and to generate new marketing campaigns. LLMs are being used to improve the performance of search engines, to develop new educational tools, and to create new forms of art and entertainment.

Overall, generative AI and LLMs are two of the most exciting and promising technologies of our time. By learning about these technologies, we can position ourselves to take advantage of the many opportunities they will create in the years to come.

 

LlamaIndex is an orchestration framework for large language model (LLM) applications. LLMs like GPT-4 are pre-trained on massive public datasets, allowing for incredible natural language processing capabilities out of the box. However, their utility is limited without access to your own private or domain-specific data. 

LlamaIndex solves this problem by providing a way to ingest, structure, and access your own data for use with LLMs. It supports a variety of data sources, including APIs, databases, and PDFs.

Once your data is indexed, it provides a number of ways to interact with it, including: 

• Natural language querying: You can ask LlamaIndex questions about your data in plain English. For example, you could ask “What are the top 10 revenue-generating products?” or “What are the most common customer complaints?” 
• Conversation with LLM-powered data agents: It can be used to create chatbots or other conversational interfaces that can access and process your data in real-time. This allows you to build applications that can provide personalized assistance to your users or answer their questions in a comprehensive and informative way. 

• LLM-powered data analytics: It can also be used to power LLM-based data analytics applications. For example, you could use it to build a system that can automatically generate reports or insights from your data. 

 

Key components of LlamaIndex: 

The key components of LlamaIndex are as follows:  

• Data connectors: These components allow LlamaIndex to ingest data from a variety of sources, such as APIs, databases, and PDFs. The data is converted into a simple document format that is easy for LlamaIndex to process. 
• Data index: A data structure that stores the data in a way that makes it easy for LlamaIndex to find the relevant information when a user asks a question or starts a conversation. 

• Retrievers: Retrievers are responsible for finding the most relevant information in the data index based on the user’s query or chat message. 
• Query engines: Allow users to ask questions about their data in natural language. They accept natural language queries and provide comprehensive and informative responses. 
• Chat engines: Allow users to have interactive conversations with their data. They maintain a contextual understanding of the conversation history and can provide answers that consider the relevant past context. 

 

 

 

In this tutorial, we will delve into the technical intricacies of constructing intelligent chatbots that leverage advanced technologies. Our example code will illustrate the development of a PDF Q&A chatbot that incorporates the OpenAI language model, VectorStoreIndex for document indexing and Streamlit for user interface design.

 

Furthermore, the chatbot will be equipped with the Llama Index’s Conversational Retrieval Chain, enabling it to furnish precise responses based on user queries. Let’s embark on this journey into the technical aspects of crafting a highly capable chatbot. 

Importing necessary libraries  

To commence our chatbot project, we need to import crucial libraries and functions. Here’s a breakdown of the libraries we will be utilizing: 

• LlamaIndex: We harness the power of the Llama Index, a comprehensive framework tailored for developing applications enriched by language models. 
• Streamlit: Streamlit, a Python library, serves as our toolkit for swiftly constructing web applications with an intuitive interface that facilitates user interaction. 

 

Setting OpenAI API key  

To access OpenAI’s language models effectively, it is imperative to configure our API key. Replace the placeholder with your actual OpenAI API key, obtainable from the OpenAI API platform. This key will act as our gateway to the powerful language models offered by OpenAI. Also you can use the dotenv route where you place your OPENAI key in the .env file. 

Setting up the user interface: 

This section delves into the creation of our user interface using Streamlit. The interface is meticulously designed to be clean, user-friendly, and feature-rich. It encompasses a title and a minimalist sidebar, providing an entry point for users to engage with our Q&A chatbot seamlessly.
 

 

 

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Main function and data loading: 

At the core of our chatbot lies the main function, which orchestrates the entire application logic. We initiate the process by loading data from a specified directory using a SimpleDirectoryReader. This data will serve as the knowledge repository from which our chatbot will draw answers to user inquiries. 

 

Creating a service context: 

To enable the advanced natural language processing capabilities of our chatbot, we established a ServiceContext. This context is pre-configured with default settings and an OpenAI language model (llm). It lays the groundwork for our chatbot’s ability to understand and generate responses to user queries effectively. 

 

 

 

 

Building the LlamaIndex: 

The pivotal component of our chatbot’s capabilities is the Llama Index. We construct this index using VectorStoreIndex, a versatile tool that optimizes the stored documents for efficient searching. This step ensures that our chatbot can rapidly retrieve pertinent information when faced with user queries. 

 

User input and chat engine: 

Our user interface empowers users to input questions related to the provided data through a text input field. The chat engine processes these queries by harnessing the capabilities of the Llama Index. Subsequently, it generates responses based on the content indexed from the documents. This interaction constitutes the core functionality of our Q&A chatbot. 

 

 

Running the application: 

With all the components in place, we culminate our code by executing the main function. This pivotal step transforms our project into an interactive chatbot. Users can seamlessly pose questions, and the chatbot, equipped with the Llama Index, responds with precise answers drawn from the indexed documents. 

 

 

Benefits of using LlamaIndex 

There are a number of benefits to using LlamaIndex to create custom LLM applications: 

• It is easy to use: Provides a simple and intuitive API for interacting with your data. 
• It is flexible: Supports a variety of data sources and formats. It also provides a number of plugins and integrations that can be used to extend its functionality. 
• It is scalable: Scaled to handle large datasets and high traffic volumes. 

In conclusion, this guide has offered a comprehensive roadmap for creating personalized Q&A chatbots with the Llama Index at their core.

By integrating cutting-edge technologies such as OpenAI for language processing, VectorStoreIndex for efficient document indexing, and the Llama Index’s Conversational Retrieval Chain, we have unlocked the potential for engaging, informative, and highly interactive question-answering experiences.

Feel encouraged to explore and expand upon this chatbot project, extending its capabilities to tackle more intricate tasks and challenges within the realm of AI-driven conversational systems. 

The recently unveiled Falcon Large Language Model, boasting 180 billion parameters, has surpassed Meta’s LLaMA 2, which had 70 billion parameters.

 

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Falcon 180B: A game-changing open-source language model

The artificial intelligence community has a new champion in Falcon 180B, an open-source large language model (LLM) boasting a staggering 180 billion parameters, trained on a colossal dataset. This powerhouse newcomer has outperformed previous open-source LLMs on various fronts.

Falcon AI, particularly Falcon LLM 40B, represents a significant achievement by the UAE’s Technology Innovation Institute (TII). The “40B” designation indicates that this Large Language Model boasts an impressive 40 billion parameters.

Notably, TII has also developed a 7 billion parameter model, trained on a staggering 1500 billion tokens. In contrast, the Falcon LLM 40B model is trained on a dataset containing 1 trillion tokens from RefinedWeb. What sets this LLM apart is its transparency and open-source nature.

 

Falcon operates as an autoregressive decoder-only model and underwent extensive training on the AWS Cloud, spanning two months and employing 384 GPUs. The pretraining data predominantly comprises publicly available data, with some contributions from research papers and social media conversations.

Significance of Falcon AI

The performance of Large Language Models is intrinsically linked to the data they are trained on, making data quality crucial. Falcon’s training data was meticulously crafted, featuring extracts from high-quality websites, sourced from the RefinedWeb Dataset. This data underwent rigorous filtering and de-duplication processes, supplemented by readily accessible data sources. Falcon’s architecture is optimized for inference, enabling it to outshine state-of-the-art models such as those from Google, Anthropic, Deepmind, and LLaMa, as evidenced by its ranking on the OpenLLM Leaderboard.

Beyond its impressive capabilities, Falcon AI distinguishes itself by being open-source, allowing for unrestricted commercial use. Users have the flexibility to fine-tune Falcon with their data, creating bespoke applications harnessing the power of this Large Language Model. Falcon also offers Instruct versions, including Falcon-7B-Instruct and Falcon-40B-Instruct, pre-trained on conversational data. These versions facilitate the development of chat applications with ease.

Hugging Face Hub Release

Announced through a blog post by the Hugging Face AI community, Falcon 180B is now available on Hugging Face Hub.

This latest-model architecture builds upon the earlier Falcon series of open-source LLMs, incorporating innovations like multiquery attention to scale up to its massive 180 billion parameters, trained on a mind-boggling 3.5 trillion tokens.

Unprecedented Training Effort

Falcon 180B represents a remarkable achievement in the world of open-source models, featuring the longest single-epoch pretraining to date. This milestone was reached using 4,096 GPUs working simultaneously for approximately 7 million GPU hours, with Amazon SageMaker facilitating the training and refinement process.

Surpassing LLaMA 2 & commercial models

To put Falcon 180B’s size in perspective, its parameters are 2.5 times larger than Meta’s LLaMA 2 model, previously considered one of the most capable open-source LLMs. Falcon 180B not only surpasses LLaMA 2 but also outperforms other models in terms of scale and benchmark performance across a spectrum of natural language processing (NLP) tasks.

It achieves a remarkable 68.74 points on the open-access model leaderboard and comes close to matching commercial models like Google’s PaLM-2, particularly on evaluations like the HellaSwag benchmark.

Falcon AI: A strong benchmark performance

Falcon 180B consistently matches or surpasses PaLM-2 Medium on widely used benchmarks, including HellaSwag, LAMBADA, WebQuestions, Winogrande, and more. Its performance is especially noteworthy as an open-source model, competing admirably with solutions developed by industry giants.

Comparison with ChatGPT

Compared to ChatGPT, Falcon 180B offers superior capabilities compared to the free version but slightly lags behind the paid “plus” service. It typically falls between GPT 3.5 and GPT-4 in evaluation benchmarks, making it an exciting addition to the AI landscape.

Falcon AI with LangChain

LangChain is a Python library designed to facilitate the creation of applications utilizing Large Language Models (LLMs). It offers a specialized pipeline known as HuggingFacePipeline, tailored for models hosted on HuggingFace. This means that integrating Falcon with LangChain is not only feasible but also practical.

Installing LangChain package

Begin by installing the LangChain package using the following command:

This command will fetch and install the latest LangChain package, making it accessible for your use.

Creating a pipeline for Falcon model

Next, let’s create a pipeline for the Falcon model. You can do this by importing the required components and configuring the model parameters:

Here, we’ve utilized the HuggingFacePipeline object, specifying the desired pipeline and model parameters. The ‘temperature’ parameter is set to 0, reducing the model’s inclination to generate imaginative or off-topic responses. The resulting object, named ‘llm,’ stores our Large Language Model configuration.

PromptTemplate and LLMChain

LangChain offers tools like PromptTemplate and LLMChain to enhance the responses generated by the Large Language Model. Let’s integrate these components into our code:

In this section, we define a template for the PromptTemplate, outlining how our LLM should respond, emphasizing humor in this case. The template includes a question placeholder labeled {query}. This template is then passed to the PromptTemplate method and stored in the ‘prompt’ variable.

To finalize our setup, we combine the Large Language Model and the Prompt using the LLMChain method, creating an integrated model configured to generate humorous responses.

Putting it into action

Now that our model is configured, we can use it to provide humorous answers to user questions. Here’s an example code snippet:

In this example, we presented the query “How to reach the moon?” to the model, which generated a humorous response. The Falcon-7B-Instruct model followed the prompt’s instructions and produced an appropriate and amusing answer to the query.

This demonstrates just one of the many possibilities that this new open-source model, Falcon AI, can offer.

A promising future

Falcon 180B’s release marks a significant leap forward in the advancement of large language models. Beyond its immense parameter count, it showcases advanced natural language capabilities from the outset.

With its availability on Hugging Face, the model is poised to receive further enhancements and contributions from the community, promising a bright future for open-source AI.

 

 

 

The world is riding the wave of generative AI, but can non-profit organizations hop on the bandwagon? The answer is yes! The latest technology, in particular, generative AI and LLM (Large Language Models), is a ticket to innovation.

From climate change and social justice to women empowerment and education, non-profit organizations are at the forefront of a plethora of the globe’s pressing issues. Despite their larger-than-life persona, non-profit organizations often have limited resources and staff, so they need to find ways to be as efficient and effective as possible.  

Navigating the non-profit maze: Common business problems

Nonprofits and NGOs face unique challenges and business problems due to their social missions and operational structures. Some common business problems faced by nonprofits and NGOs include: 

1. Limited funding and resources  

One of the biggest challenges that nonprofits face is limited funding resources. Nonprofits often must make do with less money, staff, and other resources than for-profit businesses. This is because they typically rely on donations, grants, and fundraising efforts to sustain their operations. Hence, limited funding can restrict their ability to expand programs, hire staff, or invest in infrastructure. 

2. Donor retention

Nonprofits need to maintain strong relationships with donors to secure ongoing financial support. Attracting and retaining donors can be challenging, as donors’ priorities and interests may change over time. 

3. Volunteer recruitment and retention  

Nonprofits often rely on volunteers to carry out their work. Recruiting and retaining dedicated volunteers can be a struggle, as individuals may have limited availability, fluctuating commitment levels, or require specific skill sets. 

4. Complex regulations 

Next on the challenge list, we have complex regulations that nonprofits must comply with, including those related to fundraising, financial reporting, and government contracting. These regulations can be time-consuming and expensive to comply with, and they can also make it difficult for nonprofits to innovate. 

5. Changing demographics 

Changing demographics pose challenges for nonprofits. The aging population requires adaptations in programs and services for seniors.

Despite these challenges, nonprofits play a significant role in society. They provide essential services to those in need, and they help to make the world a better place. By overcoming these challenges, nonprofits can continue to make a difference in the world. 

Closing the gap: Cue Generative AI and Large Language Models for non-profit organizations 

That is where generative AI comes in. Taking the world by storm, generative AI is a type of artificial intelligence that can create new data. This means that nonprofits can use generative AI to create personalized content for donors, automate tasks, analyze data, and create new products and services. 

Generative AI and large language models are emerging technologies that have the potential to help non-profits and NGOs overcome some of these challenges.  While generative AI can be used to create new content, LLMs can be used to analyze data and identify trends, which can help nonprofits make better decisions about their work.

How can generative AI and LLMs help non-profits run more effectively? 

1. Fundraising 

Grant writing: Generative AI can be used to help nonprofits write grant proposals. This can save nonprofits time and money, and it can also help them to write more effective proposals. 

RFP reviews: Generative AI can be used to help nonprofits review RFPs (requests for proposals). This can help nonprofits to identify opportunities to apply for funding, and it can also help them to ensure that their proposals are responsive to the RFPs. 

Funding thesis: Generative AI can be used to help nonprofits develop funding theses. This can help nonprofits to articulate their vision for how they will use the funding to achieve their mission, and it can also help them to attract funding from donors and funders. 

2. Operations

Customer support: Generative AI can be used to help nonprofits provide customer support. This can free up staff time to focus on other important work, and it can also help nonprofits to provide more consistent and accurate customer support. 

Employee learning and development: Generative AI can be used to help nonprofits provide employee learning and development. This can help nonprofits to ensure that their employees are well-versed with the latest trends and best practices, and it can also help them to improve employee retention. 

3. Compliance

Tax, compliance, and regulatory requirements: Generative AI can be used to help nonprofits stay up to date on tax, compliance, and regulatory requirements. This can help nonprofits to avoid costly mistakes, and it can also help them to ensure that they are operating in compliance with the law. 

4. Public relations

Public relations, marketing, social media, and donor reach relations: Generative AI can be used to help nonprofits with public relations, marketing, social media, and donor reach relations. This can help nonprofits to raise awareness of their work, attract new donors, and build relationships with stakeholders.  

How can Data Science Dojo help?  

At Data Science Dojo, we believe in purpose and profit. We are dedicated to making a positive impact on the world by empowering individuals, businesses, and industries with innovative solutions, particularly generative AI and LLM. Our motto is “Data science for everyone,” and we are committed to making tech accessible and affordable to everyone.

We believe that generative AI science is a powerful tool, even for non-professionals. By incorporating the latest generative AI technology, our experts can create custom solutions tailored to your brand’s needs, accelerating your business, and streamlining your operations. 

Supercharge your business with generative AI. Take the first step towards success – explore our Generative AI, Large Language Models and Custom Chat Bot services now!