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It can be a challenge to engage students when they’re at school only a few days a week—station rotations and flipping the classroom can help.

How do you plan your class when students are allowed to come to school only a few times a week? That’s the question many teachers are facing as they confront the reality of a hybrid model where students attend school in person two or three days a week and spend the rest of their time learning online. It can be a challenge to engage all students in the same classroom activity when half are present virtually.

Blended learning represents an opportunity to personalize learning and reclaim instructional time in a hybrid schedule.

Flip the Classroom

Many teachers know the flipped classroom—where traditional lessons are delivered via video for students to watch at home while class time is reserved for students to collaborate and apply their learning. In a hybrid schedule, flipping the classroom is a great way to maintain instructional momentum. Students watch a lesson on remote days, then come in and apply their new knowledge in the classroom.

As in the classroom, short and direct mini-lessons can be most effective. Chunk longer content into shorter videos. Tools like Screencastify allow you to quickly record audio while talking over slides. Camera-shy teachers can remain present for their students by using the tool to help them find the important links and resources central to the daily work of the class.

Video is not the only tool for flipping the classroom. Try flipping with a text, image, or website for students to learn from at home. For example, the class might be asked to read and annotate an article about a current event and contribute to a group document where students share their thoughts. The next day, students meet in their groups and talk through and further develop the product they began virtually.

Encourage students to use a graphic organizer or something similar to collect and synthesize information. Gathering information in a central place can help students distill information and engage with it meaningfully. Asking students to assess and examine information, through an evaluative blog post for example, prior to live instruction time can improve engagement and help students think critically about the material.

Use in-person class time for students to work through an assignment first modeled online. Circulate in a socially distanced way, and talk with individual students about their work. After six months of remote work, many students are hungry for what English teacher Dave Stuart calls moments of genuine connection—small instances when teachers connect individually with a student. Students need to be respected, seen, and heard. Moving direct instruction online allows time for more connections in class.

Rotation Models

In a traditional blended learning format, students rotate through a series of stations within the classroom, including small group instruction and computer-based applications. In the hybrid schedule, spread these stations out over several days.

Catlin Tucker suggests that stations take several forms, including small group instruction, collaborative challenges, or other group work activities. After checking for understanding, create re-teaching stations for students struggling with a particular concept, while other students pursue enrichment or extension work. During face-to-face days, reserve some time to confer with students on their at-home work.

It might take some practice to envision what this could look like in the hybrid schedule. Consider reducing the total number of stations or splitting stations between online and in person. In school, students rotate through a teacher-led station and a group station; and at home, students work through individual materials at one station and videoconference with their peers in another.

Alternatively, use collaborative stations in class and save remote time for individual practice. Older students can use tools like Jamboard or Padlet to contribute to a collaborative product—either in the classroom or at home.

The Individual Rotation model provides personalization by allowing students to alternate between small group work at school and online learning at home. Students work through digital lessons to move along a skills progression tailored to their individual needs. For example, a teacher might assign a specific series of online math or literacy problems for a student to complete at home. While the learning sequence adheres to the class curriculum, students move through at their own pace and need. In class, students apply their learning through collaborative projects or other tasks. As with other strategies, reserve time to conference with individual students about their learning, hold them accountable, and discuss next steps. Many programs exist to support this work, including mastery-based learning modules on free sites such as Khan Academy.

Don’t be afraid to mix the methods. For example, station work in class could include a flipped video explaining a concept. Group rotations could be differentiated such that students completed different adaptive exercises based on their individual needs. Flexibility is key in experimenting with new strategies. 

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3 Effective Strategies For Non

Marketing your non-profit is essential to the success of your cause and luckily, the internet offers numerous possibilities – you just need to make the most of these opportunities

Non-profits and charities often have small budgets and few people to help with their marketing strategies. But without a good marketing strategy in place, how do you raise awareness of your cause? How do you attract more supporters and donors to help support your cause? Marketing your non-profit is essential to the success of your cause and luckily, the internet offers numerous possibilities – you just need to make the most of these opportunities.

In this blog post, I’m going to share 3 effective strategies for non-profits and charities.

Advertise online…for free?

Google Ads can be extremely successful; they appear right at the top of search results so they’re the first result people see when doing a Google search.

The issue with that, of course, is that it costs money. And although it might not be a lot, many non-profits and charities simply can’t afford this kind of investment.

Download our Business Resource – Social media communications for not-for-profit organizations guide

This guide aims to give a clear structure to help you grow internal confidence and buy-in so that the benefits of social media can become visible and it becomes more accountable, serving the long-term strategic aims of charities.

Access the Social media communications for not-for-profit organizations

Go to the link mentioned earlier to apply for the ad grant; first, though, you’ll have to check whether you are eligible for a grant – it’s not too complicated and you can check what exactly it entails here.

If you are eligible and you’re given the grant, make sure to do everything possible to follow Google’s instructions so that you can remain eligible.

Invest in visual content

There is nothing more powerful than visual content – particularly images and videos. They can say so much in just a few seconds and most importantly, they can have a big emotional impact on the viewer. So whether it’s your website, your blog, your social media, or any other digital channel, try to use as much visual content as possible to help attract a wider audience and most importantly, get them to take action (share your updates with your friends, make a donation, buy something from you to help promote the cause, and so on).

Here are a few useful ideas of what types of visual content you can create:

Tell a story with a video

Videos are incredibly powerful – especially when they tell a story that people can empathize with. As a charity or non-profit, you can use video to show how you’re making an impact on the world; for example, Charity: Water often publishes videos of success stories, including a little backstory to paint a full picture.

Motivational images

These super easy to create (just use something like Canva, Crello, or Pablo) and they’re highly shareable because social media users love them. One way to use these types of images is to grab quotes not only from known personalities but even from the people you’ve worked with or helped – and to make them even more impactful, include a short story or link to a blog post/video/etc. where you tell a story, like TWLOHA do in this post:

Behind the scenes photos and videos

Candid and behind the scenes visuals can help generate good engagement on social media and they help show the human side of your organization; even though your organization might be there to help people or make a real change in the world, you’re still, in effect, a brand. Here’s how UNICEF share selfies from their campaigns.

The power of storytelling

Previously, we’ve touched a bit on storytelling when we talked about videos and motivational quotes, and we’ve seen some examples of storytelling from charities and non-profits marketing strategies.

Storytelling is incredibly important on social media (and with all content in general, in fact), especially for non-profits, because it doesn’t just help inform them, but it pulls at people’s emotions. And when you can impact people’s emotions, you make them want to take action: to share your posts and help spread the word or your organization, to donate their time and/or money, and to help support you in your cause.

Stories can be told in a variety of mediums and channels; for example:

A blog post detailing the success stories of your work

A video of someone you helped or impacted sharing their own story

Powerful images with text updates telling the complete story (like the earlier example from TED Talks)

As to how to tell powerful stories, here’s what you can learn from this post from TED Talks:

Here’s what you can learn from it (as well as the previous example from Charity: Water):

You need a hero/heroine: like Shameem Akthar who had to change who she was in order to thrive in an extremely conservative environment

The villain: any good story has a villain. It can be a person, a country, an organization, and anything in between. In this case, the villain is most likely the society that she was born in – a society that didn’t allow her to get the education she wanted and needed.

A conflict: you need to conflict in order to build suspense. And people love success stories, they want to see the good one win – and the bad one lose. For example, the conflict in this particular story is that she was a girl and as a girl, she couldn’t go to school – and then comes the solution to the conflict: her uncle raising her as a boy so she could have more opportunities in life

A happy end – although that is debatable; sometimes there is no happy end – yet – but that just means the story isn’t over yet. You can still tell those stories and ask people for help so you can actually reach a happy ending for your story. But if you do have a happy ending, like the story above (with Shameem Akthar not only managing to play the system but also ultimately getting a PhD and helping change other girls’ life as a social worker) that too can have a big impact on people. Happy endings like these make us feel good while at the same time, they also inspire us to be better, to do better, and to try to make our own mark on the world, just like people like Shameem Akthar have done.


Non-profits and charities might have small budgets and few people to help them market their organization, but by using strategies like the above you can make a real impact on your success without spending all of your budget.

All you need is a great content strategy to help; follow the tips and ideas outlined in this article and try to incorporate more storytelling and more visual content (videos and live videos, images – both candid and created by you – and so on) on any digital channel you’re using.

How To Schedule A Text Message On Android

Even with the rise of online messengers that you can use to chat with someone for free, sending a text message remains one of the fastest ways to reach someone on their phone. What if the text you want to send isn’t urgent, and you intentionally want to postpone sending it? 

What if you don’t want to forget wishing someone a happy birthday the next day or need to reach someone in a different time zone and don’t want to wake them up with a text? On Android, there are several ways to schedule a text message to be sent at a later time or date. Learn how to use your native Messages app, Google Messages, or a third-party app to schedule a text message to be sent in the future. 

Table of Contents

How to Schedule a Text Message on Android in Your Native Messages App

So you wrote a text but decided to send it another time. The good news is, you can schedule and send your message later, no matter what Android device you have. However, depending on your model, you’ll either need to use a third-party app to do it or not. 

For example, if you have a Samsung phone, you can use your native Messages app called Samsung Messages to schedule your texts. To do that, follow the steps below.

Open the Samsung Messages app, find the contact you want to message, and write down your text.

Select the arrow icon on the left side of the text, then select the plus icon to reveal additional options.

From the options, select Schedule message.

Choose the time and date when you want your message to be sent. Select Done to confirm.

To finish scheduling your message, select Send. 

How to Schedule a Text Message Using Google Messages

On many models of Android smartphones, Google Messages is the native Messages app. If that’s the case, scheduling text messages on Android becomes even easier. 

To schedule a text message in Google Messages, follow the steps below.

Open Google Messages and type your text.

Hold down the Send button until the Scheduled send option appears and select it.

Pick a date and time when you want the text to be sent.

Select Send to confirm. The button will now display a little clock icon that means your message is scheduled. 

What to Do if You Can’t Schedule a Text in Google Messages 

If you opened your native Messages app and didn’t find the Scheduled send option, it could mean one of the two things. Your native Messages app is different from Google Messages, in which case you can simply download and install Google Messages to make it work. 

Alternatively, the feature may not have rolled out for you yet. You can either wait for the update to reach you or find Google Messages on the Play Store and join the beta program on the product page. The beta version of the app will include the feature. 

Use Third-Party Apps to Schedule a Text Message on Android 

You can also use a third-party app to be able to schedule your text messages as well as use other workarounds.

Use Pulse SMS to Postpone Your Text Messages

If you don’t like using Google Messages, Pulse SMS is a good alternative. The Pulse SMS logo even looks similar to that of Google Messages. Naturally, the process of scheduling a text in Pulse SMS mimics Google Messages too. 

After you download and install the app, open it and select the plus icon in the bottom-right corner of the app. Choose the contact that you want to send a text to. Then, hold down the Send button on the right until you see the option to schedule the text. 

Select the date and time when you want your message sent and select OK. Write down your message and select Save. 

Pulse SMS will send it on a day and time that you chose. 

Schedule Your Texts Using Do It Later

Do It Later sounds like an app for procrastination, but it’s an app for automating your messages. Aside from scheduling your emails and text messages to be sent later, Do It Later allows you to automatically reply to calls, texts, WhatsApp messages, and even emails. 

To schedule a text in Do It Later, open the app, select the plus icon in the bottom-right corner of your screen, then choose Message. Next, add the recipient, write down your message and select when you want the app to send it. 

To confirm, select the tick icon in the upper-right corner of the screen. Do It Later will take care of the rest for you. 

Write Down Now, Send Later

Writing down a thought when it crosses your mind always helps to remember it later. The same is true for text messages. So write down your message the minute you think about it, and if it doesn’t seem like the right time to send it, schedule it to be sent later. 

3 Tips For Creating Effective Pd

Just as teachers design lessons with student needs in mind, facilitators of professional development need to design learning experiences with adult needs in mind. Educators walk into our meetings with years of life experiences that have shaped their beliefs, mindset, and values. Much time and effort have gone into creating systems in classrooms that work for these teachers and their students, so asking a teacher to shift an aspect of their system without keeping their needs in mind can feel insulting or undoable. Teachers are also busy, their minds filled with to-do lists, which can result in a resistance to slowing down and reflecting during meetings.

As facilitators, we can use the following principles of adult learning to help our educators create the mental space to learn, reflect, and shift practices.

3 Principles of Adult Learning

1. Be clear on the why: This is the most important principle of planning for adult learning. Find a way to connect to the reason teachers came to the profession in the first place, whether it be impacting lives, helping students fulfill their potential, guiding students to deeper thinking, or developing our future leaders. If you can’t find that connection, reevaluate the shift you’re asking teachers to make.

Consider starting your meeting by providing participants with time to reflect in writing and share with colleagues. Good opening possibilities include asking each teacher to reflect on a challenge from their day, or to list the traits or skills they want their students to have as adults. Another method would be starting the meeting by discussing an inspirational quote or video clip. Whatever the tactic, the goal is to tap into the reason teachers became educators in the first place, so they have the motivation and energy to consider a shift in their methods.

2. Provide voice and choice: Adult professionals should have a say in the work they do. Think about how you can let go of some control as a facilitator and put your teachers in the driver’s seat. Ask them how they learn best, and then give choices and be responsive, even when those choices go against the plans you’ve made as a facilitator. Do your teachers need more time on an agenda item? Would they rather work in pairs, or shift to applying to their own context sooner than you had planned? The best facilitators know when to give up control. I’ve learned to shelve my ego and create space for the adults in the room to challenge my plans.

Even small bits of relinquished control—like asking teachers to give input on agenda items and the pacing of the session—can make a difference in investment. Ask for input and feedback early and often. Start your meeting by eliciting input, and write a reminder to yourself to ask mid-session what is working and not working for your teachers.

By creating opportunities for teachers to share their thoughts, you are communicating that their input is valued—an important adult need. One new principal I know asks for anonymous feedback after every professional development session and meeting via Google Forms. This constant communication allows for a safe venting of frustration and an opportunity to share thoughts, so there is no festering or buildup of resentment, and voices are heard.

3. Balance new learning with reflection: Adults are motivated to learn when they have an immediate use for the skill or knowledge being taught, when they can try something new in their classroom tomorrow. It’s important, however, to strike the right balance of time spent learning new information and time spent role-playing or explicitly planning for implementation. After your first stage of planning, you might look through your plans to ensure you have:

A limited number of objectives, all clear and concise.

A plan to spend about a third of the session on new information and the other two thirds on reflecting or practicing through role-playing, planning, sharing ideas, and discussing with colleagues.

Time to process at the end.

We acknowledge that our students need time to process, practice, and transfer, but we often need reminders that adults, with their more complex histories and belief systems, need even more time to integrate what they are learning with what they already know. Without time to reflect on a change, adults often will find a way to dismiss a suggestion for change and continue on the path they are already taking.

Protocols and structures for reflection time with colleagues often work best. Consider adapting one of Jennifer Gonzalez’s discussion strategies for adults, or ask your teachers to reflect in writing. I recently watched Meghan Hargrave, an independent literacy consultant, end each of her sessions by asking teachers to write one thing they would try the next day, one they would try in a couple weeks, and one they will try next year.

Whichever method you use, resist the temptation to squeeze in more agenda items. Embrace the quiet sound of pens or the louder sound of lively discussion as you provide participants with adequate time for processing and reflecting.

Dig further into adult needs by checking out Elena Aguliar’s collection of research-based adult learning principles. Keep your adult learners’ needs in mind and you will find yourself with invested, engaged educators.

What Is Hybrid Cloud Computing?

Hybrid cloud computing is clearly on its way – and by some accounts is already there – to being the dominant cloud computing model. It shares the cloud spotlight, so to speak, with public cloud computing, with leading vendors such Amazon, Microsoft, Google, IBM, and a host of other firms. The private cloud is essentially a firm’s on-premises data center systems, configured more in the manner of the public cloud than traditional data center setup. Private cloud leverages virtualization and automation to pool and optimize resources.

Gartner defines a hybrid cloud computing as a cloud service that is composed of some combination of private, public and community cloud services, from different service providers. The hybrid cloud is the most widely-used choice for cloud services among enterprise IT firms for a variety of key reasons, all of which center around flexibility and scalability. Furthermore, the hybrid cloud is a good fit with the expanded functionality of Platform as a Service, or PaaS. For help decided which type of cloud service to use for your business, read our comprehensive guide to cloud computing.

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A hybrid cloud service means the company gets some of its hybrid IT services from a public cloud provider but retains some of its private, on-premises systems. The reasons range from governance and regulation requiring sensitive data to remain on-premises to the services needed are not available from a public cloud provider.

“The fact is most companies moving from on-prem to the cloud simply can’t move the entire back office to the cloud. Functionality doesn’t exist or is too complicated or all of the above,” said Joshua Greenbaum, principal analyst with Enterprise Applications Consulting.

In some cases, it’s due to very vertical functionality not being available. A manufacturing firm, for example, can move basic functions to the cloud like Enterprise Resource Planning (ERP) or Customer Resource Management (CRM), but if you’re a supplier to aerospace and defense or serve a specific geographic firm with their own requirements with customizations and regulation to deal with, some of the needed functionality isn’t available out of the box from a cloud vendor and possibly never will be.

For one reason or another, on-premises and cloud data have to be kept separate. For example, a medical company might need to keep patient records on-premises in a secured database, but it can use Oracle or Peoplesoft for HR and accounting. Keeping the data separate is actually easy. It’s not like medical records will suddenly fall into the HR app, assuming you configure everything properly.

The hybrid cloud world by definition requires that there are varying levels of integration between on-premises apps and their data and the cloud. The two are functionally separate systems and should be walled off. That is carefully conceived of and engineered and can be done wrong. Then you end up with unanticipated problems that suddenly expose you to some kind of governance or regulatory risk.

More likely, though, it’s human error that causes data to move from one system to another. “Data does migrate between the two freely and needs to be well regulated. You might have tightly controlled data in your system and an employee has a Dropbox account that you don’t know about. If it’s not managed that’s a security violation. So that leakage exists,” said Greenbaum.

Hybrid cloud computing enables enterprises to run apps in a private or public cloud infrastructure.

The main benefit of hybrid is the best of both worlds. You get to keep your legacy systems for archival or historical data, or whatever reason you need to hang on to it, and you get the benefits of the cloud. It’s scalability and elastic on demand, the primary appeal of the cloud.

If you find yourself in need of computing capacity, you have two choices: requisition, purchase, and deploy a server, which could take months, and then it sits idle and unused when the task is done but you still pay for it. Or you rent some capacity on AWS for the time needed and shut it down when done and cease paying for it.

One reason everyone is moving to the cloud at a fantastic rate is security and privacy and you should, too, said Greenbaum. “It’s now abundantly clear on-premises data centers are vulnerable and IT staffs are under the gun to security concerns. Your stuff is much more secure to have a Microsoft or Amazon doing it for you, but they still interface with [your] older systems and if they are not properly locked down, that becomes a vulnerability vector,” he said.

Hybrid cloud computing allows companies to expand their computing capabilities by linking in-house infrastructure to public cloud resources.

Moving to the cloud, even partially, has its challenges, said King. “The biggest problem that companies or cloud providers and businesses face using hybrid cloud is coordinating apps so that an app can work seamlessly in both environments, whatever that is. On the plus side we’re far enough along on those that the companies focusing on hybrid cloud have developed the tools and management processes that are necessary for customers to successfully manage those problems,” said King.

Another challenge is that switching to a cloud app is a big disruption. When you move to a cloud app, especially from an older, on-premises application, you are migrating data from an older system into a thoroughly modern new system, which can be fraught with complexity. Moving databases means a new schema, managing data, where everything can be different, which will undoubtedly break existing apps.

Another key concern is you are also upgrading and changing the user experience. The way you do things is different, and people tend to resist change, especially if there is no clear gain, and people don’t respond well to radical change. So you are introducing change and complexity in the already complex world of cloud, you have to ask if it’s worth it.

In choosing a provider, hybrid cloud providers assume you are maintaining your own data center and some IT functions. So the question becomes what are you looking to get out of hybrid cloud. Some might want it for times when they need a lot of compute power, such as during a compile. Others might use it as a disaster recovery solution.

“It pays to pay attention to what services are offered, what benefits you gain. It’s not as often in clear in hybrid as some traditional methodologies,” said King. “You are certainly going to be maintaining assets around data centers. So companies have to spend a lot of time on logistics and planning and make sure the benefits they think they are going to get are actually achievable.”

The market for hybrid cloud providers is growing as more and more enterprise realize that some form of hybrid cloud likely their best strategy. Clearly, the hybrid cloud market is a diverse as the companies that serve it. AWS, for instance, so heavily promotes the concept of the public cloud as the answer to any enterprise problem that it’s not known as a hybrid provider. Though strictly speaking, a company could cobble together a hybrid cloud using its offerings. Microsoft is particularly strong in hybrid cloud, and many cloud experts predict that this strength will be a big competitive edge for its Azure offering in the years ahead.

These hybrid cloud providers each take a different approach to adding hybrid cloud services.

One of the knocks on cloud service providers like Salesforce, NetApp, Oracle, and ServiceNow is that they don’t offer significant custom fits. They are for the most part strictly off the shelf, one size fits all. They allow for some customizations or extensions to fit your business but by and large, they are fairly vanilla.

There is a watchword for the cloud called fit to standard, which means the cloud does by definition require you to accept certain business practices as standard. The cloud apps are fundamentally multitenant, so there is a requirement you do not do customization in the software.

“It’s not meant to be a common denominator approach, these are best practices, but in the cloud you have to do business as everyone else does business and if you want to deviate you do it in a hybrid environment and either build custom apps or build in the cloud but not a multitenant apps,” said Greenbaum. Also, many of the leaders of on-premises software have jumped into the cloud, offering SaaS versions of their once on-premises apps, but they haven’t always had parity. For example, Microsoft’s SQL Server initially was available to Azure subscribers as a partial implementation, but up until recently, it lacked some features of the on-premises version. It was only with the recent release of SQL Server 2024 that the on-premises version and Azure version are identical.

Right now, commercial enterprise software design has shifted to cloud-first and then the on-premises version, but Gartner predicts that by 2023 or 2023, it will be cloud-only for software development, supporting either public or private cloud, or both.

So the public cloud is growing and, one way or another, companies IT infrastructure will leverage it. Moving your entire data center to the cloud may be unsuitable, especially if there are legacy systems that will not be available in the cloud. More likely, the public cloud will be a piece of your overall computing systems, and in that scenarios, you are a hybrid cloud user.

What Are Large Language Models (Llms)?

Large Language Models (LLMs) are foundational machine learning models that use deep learning algorithms to process and understand natural language. These models are trained on massive amounts of text data to learn patterns and entity relationships in the language. LLMs can perform many types of language tasks, such as translating languages, analyzing sentiments, chatbot conversations, and more. They can understand complex textual data, identify entities and relationships between them, and generate new text that is coherent and grammatically accurate.

Learning Objectives

Understand the concept of Large Language Models (LLMs) and their importance in natural language processing.

Know about different types of popular LLMs, such as BERT, GPT-3, and T5.

Discuss the applications and use cases of Open Source LLMs.

Hugging Face APIs for LLMs.

Explore the future implications of LLMs, including their potential impact on job markets, communication, and society as a whole.

This article was published as a part of the Data Science Blogathon.

What is a Large Language Model?

In contrast, the definition of a language model refers to the concept of assigning probabilities to sequences of words, based on the analysis of text corpora. A language model can be of varying complexity, from simple n-gram models to more sophisticated neural network models. However, the term “large language model” usually refers to models that use deep learning techniques and have a large number of parameters, which can range from millions to billions. These models can capture complex patterns in language and produce text that is often indistinguishable from that written by humans.

How a Large Language Model Is Built?

A large-scale transformer model known as a “large language model” is typically too massive to run on a single computer and is, therefore, provided as a service over an API or web interface. These models are trained on vast amounts of text data from sources such as books, articles, websites, and numerous other forms of written content. By analyzing the statistical relationships between words, phrases, and sentences through this training process, the models can generate coherent and contextually relevant responses to prompts or queries.

ChatGPT’s GPT-3 model, for instance, was trained on massive amounts of internet text data, giving it the ability to understand various languages and possess knowledge of diverse topics. As a result, it can produce text in multiple styles. While its capabilities may seem impressive, including translation, text summarization, and question-answering, they are not surprising, given that these functions operate using special “grammars” that match up with prompts.

General Architecture

The architecture of Large Language Models primarily consists of multiple layers of neural networks, like recurrent layers, feedforward layers, embedding layers, and attention layers. These layers work together to process the input text and generate output predictions.

The embedding layer converts each word in the input text into a high-dimensional vector representation. These embeddings capture semantic and syntactic information about the words and help the model to understand the context.

The feedforward layers of Large Language Models have multiple fully connected layers that apply nonlinear transformations to the input embeddings. These layers help the model learn higher-level abstractions from the input text.

The recurrent layers of LLMs are designed to interpret information from the input text in sequence. These layers maintain a hidden state that is updated at each time step, allowing the model to capture the dependencies between words in a sentence.

The attention mechanism is another important part of LLMs, which allows the model to focus selectively on different parts of the input text. This mechanism helps the model attend to the input text’s most relevant parts and generate more accurate predictions.

Examples of LLMs

Let’s take a look at some popular large language models:

GPT-3 (Generative Pre-trained Transformer 3) – This is one of the largest Large Language Models developed by OpenAI. It has 175 billion parameters and can perform many tasks, including text generation, translation, and summarization.

BERT (Bidirectional Encoder Representations from Transformers) – Developed by Google, BERT is another popular LLM that has been trained on a massive corpus of text data. It can understand the context of a sentence and generate meaningful responses to questions.

XLNet – This LLM developed by Carnegie Mellon University and Google uses a novel approach to language modeling called “permutation language modeling.” It has achieved state-of-the-art performance on language tasks, including language generation and question answering.

T5 (Text-to-Text Transfer Transformer) – T5, developed by Google, is trained on a variety of language tasks and can perform text-to-text transformations, like translating text to another language, creating a summary, and question answering.

RoBERTa (Robustly Optimized BERT Pretraining Approach) – Developed by Facebook AI Research, RoBERTa is an improved BERT version that performs better on several language tasks.

Open Source Large Language Models

The availability of open-source LLMs has revolutionized the field of natural language processing, making it easier for researchers, developers, and businesses to build applications that leverage the power of these models to build products at scale for free. One such example is Bloom. It is the first multilingual Large Language Model (LLM) trained in complete transparency by the largest collaboration of AI researchers ever involved in a single research project.

With its 176 billion parameters (larger than OpenAI’s GPT-3), BLOOM can generate text in 46 natural languages and 13 programming languages. It is trained on 1.6TB of text data, 320 times the complete works of Shakespeare.

Bloom Architecture

The architecture of BLOOM shares similarities with GPT3 (auto-regressive model for next token prediction), but has been trained in 46 different languages and 13 programming languages. It consists of a decoder-only architecture with several embedding layers and multi-headed attention layers.

Bloom’s architecture is suited for training in multiple languages and allows the user to translate and talk about a topic in a different language. We will look at these examples below in the code.

Other LLMs

We can utilize the APIs connected to pre-trained models of many of the widely available LLMs through Hugging Face.

Hugging Face APIs Example 1: Sentence Completion

Let’s look at how we can use Bloom for sentence completion. The code below uses the hugging face token for API to send an API call with the input text and appropriate parameters for getting the best response.

import requests from pprint import pprint headers = {'Authorization': 'Entertheaccesskeyhere'} # The Entertheaccesskeyhere is just a placeholder, which can be changed according to the user's access key def query(payload): response =, headers=headers, json=payload) return response.json() params = {'max_length': 200, 'top_k': 10, 'temperature': 2.5} output = query({ 'inputs': 'Sherlock Holmes is a', 'parameters': params, }) pprint(output)

Temperature and top_k values can be modified to get a larger or smaller paragraph while maintaining the relevance of the generated text to the original input text. We get the following output from the code:

[{'generated_text': 'Sherlock Holmes is a private investigator whose cases ' 'have inspired several film productions'}]

Let’s look at some more examples using other LLMs.

Example 2: Question Answers

We can use the API for the Roberta-base model which can be a source to refer to and reply to. Let’s change the payload to provide some information about myself and ask the model to answer questions based on that.

headers = {‘Authorization’: ‘Entertheaccesskeyhere’}

def query(payload): response =, headers=headers, json=payload) return response.json()

params = {‘max_length’: 200, ‘top_k’: 10, ‘temperature’: 2.5} output = query({ ‘inputs’: { “question”: “What’s my profession?”, “context”: “My name is Suvojit and I am a Senior Data Scientist” }, ‘parameters’: params })


The code prints the below output correctly to the question – What is my profession?:

{'answer': 'Senior Data Scientist', 'end': 51, 'score': 0.7751647233963013, 'start': 30} Example 3: Summarization

We can summarize using Large Language Models. Let’s summarize a long text describing large language models using the Bart Large CNN model. We modify the API URL and added the input text below:

headers = {‘Authorization’: ‘Entertheaccesskeyhere’}

def query(payload): response =, headers=headers, json=payload) return response.json()

params = {‘do_sample’: False}

full_text = ”’AI applications are summarizing articles, writing stories and engaging in long conversations — and large language models are doing the heavy lifting.

A large language model, or LLM, is a deep learning model that can understand, learn, summarize, translate, predict, and generate text and other content based on knowledge gained from massive datasets.

Large language models – successful applications of transformer models. They aren’t just for teaching AIs human languages, but for understanding proteins, writing software code, and much, much more.

In addition to accelerating natural language processing applications — like translation, chatbots, and AI assistants — large language models are used in healthcare, software development, and use cases in many other fields.”’

output = query({ ‘inputs’: full_text, ‘parameters’: params })


The output will print the summarized text about LLMs:

[{'summary_text': 'Large language models - most successful ' 'applications of transformer models. They aren’t just for ' 'teaching AIs human languages, but for understanding ' 'proteins, writing software code, and much, much more. They ' 'are used in healthcare, software development and use cases ' 'in many other fields.'}]

These were some of the examples of using Hugging Face API for common large language models.

Future Implications of LLMs

In recent years, there has been specific interest in large language models (LLMs) like GPT-3, and chatbots like ChatGPT, which can generate natural language text that has very little difference from that written by humans. While LLMs have seen a breakthrough in the field of artificial intelligence (AI), there are concerns about their impact on job markets, communication, and society.

One major concern about LLMs is their potential to disrupt job markets. Large Language Models, with time, will be able to perform tasks by replacing humans like legal documents and drafts, customer support chatbots, writing news blogs, etc. This could lead to job losses for those whose work can be easily automated.

However, it is important to note that LLMs are not a replacement for human workers. They are simply a tool that can help people to be more productive and efficient in their work. While some jobs may be automated, new jobs will also be created as a result of the increased efficiency and productivity enabled by LLMs. For example, businesses may be able to create new products or services that were previously too time-consuming or expensive to develop.

LLMs have the potential to impact society in several ways. For example, LLMs could be used to create personalized education or healthcare plans, leading to better patient and student outcomes. LLMs can be used to help businesses and governments make better decisions by analyzing large amounts of data and generating insights.


Key Takeaways:

Large Language Models (LLMs) can understand complex sentences, understand relationships between entities and user intent, and generate new text that is coherent and grammatically correct

The article explores the architecture of some LLMs, including embedding, feedforward, recurrent, and attention layers.

The article discusses some of the popular LLMs like BERT, BERT, Bloom, and GPT3 and the availability of open-source LLMs.

Hugging Face APIs can be helpful for users to generate text using LLMs like Bart-large-CNN, Roberta, Bloom, and Bart-large-CNN.

LLMs are expected to revolutionize certain domains in the job market, communication, and society in the future.

Frequently Asked Questions

Q1. What are the top large language models?

A. The top large language models include GPT-3, GPT-2, BERT, T5, and RoBERTa. These models are capable of generating highly realistic and coherent text and performing various natural language processing tasks, such as language translation, text summarization, and question-answering.

Q2. Why use large language models?

A. Large language models are used because they can generate human-like text, perform a wide range of natural language processing tasks, and have the potential to revolutionize many industries. They can improve the accuracy of language translation, help with content creation, improve search engine results, and enhance virtual assistants’ capabilities. Large language models are also valuable for scientific research, such as analyzing large volumes of text data in fields such as medicine, sociology, and linguistics.

Q3. What are LLMs in AI?

A. LLMs in AI refer to Language Models in Artificial Intelligence, which are models designed to understand and generate human-like text using natural language processing techniques.

Q4. What are LLMs in NLP?

A. LLMs in NLP stand for Language Models in Natural Language Processing. These models support language-related tasks, such as text classification, sentiment analysis, and machine translation.

Q5. What is the full form of LLM model?

A. The full form of LLM model is “Large Language Model.” These models are trained on vast amounts of text data and can generate coherent and contextually relevant text.

Q6. What is the difference between NLP and LLM?

A. NLP (Natural Language Processing) is a field of AI focused on understanding and processing human language. LLMs, on the other hand, are specific models used within NLP that excel at language-related tasks, thanks to their large size and ability to generate text.

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