How Superteams.ai helps companies deploy private large language models to create corporate content

Introduction

Many companies want to make customer service better by using generative AI.

In the past three years, venture capital firms have given more than $1.7 billion to invest in generative AI solutions. The most funded areas are AI for finding new drugs and AI for making software.

At the heart of generative AI's capabilities are Large Language Models (LLMs), empowering AI systems to craft fresh content based on existing data. 

Let's delve into the ways generative AI can amplify your business operations and gain deeper insights into the role of large language models.

What are large language models(LLM)?

LLM models handle language-related jobs, such as changing human language into computer terms and chatting with chatbots. That's why they are favoured in various industries.

LLMs are machine-learning models that have been present for a while. However, the introduction of ChatGPT has directed businesses' focus to it.

LLM serves as a basic model in NLP. It uses advanced learning formulas to understand human language and create sensible text in response to user questions.

Some power top open-source large language models to consider in 2023

#1 Llama 2

At Microsoft Inspire, an exciting collaboration between Meta and Microsoft was unveiled, showcasing their joint support for the expansive Llama 2 family of large language models (LLMs) on both the Azure platform and Windows operating system. 

Llama 2 stands as an innovative solution designed to empower developers and organizations in crafting generative AI-powered tools and immersive experiences.immersive experiences.

It is also trained 40% more than the previous version and provides twice the content length.

#2 Falcon 40B

Introducing Falcon LLM, a fundamental large language model (LLM) boasting an impressive 40 billion parameters, meticulously trained on an astounding one trillion tokens. 

TII (Technology Innovation Institute) has proudly unveiled Falcon LLM, presenting the world with a remarkable 40B model.

Employing a mere 75 percent of GPT-3's training computational resources, 40 percent of Chinchilla's, and 80 percent of PaLM-62B's, the model showcases remarkable efficiency. Source

#3 Bloom by BigScience

BLOOM, the Big Open-science Open-access Multilingual Language Model, is a noteworthy language model developed by BigScience using transformer technology. 

A community of over 1000 AI researchers collaborated to create BLOOM, offering unrestricted access to a substantial language model for anyone interested in utilizing it.

#4 MPT 30B

MosaicML stands as a pioneering generative AI firm, recognized for its offerings in AI deployment and scalability solutions. 

The recent introduction of their cutting-edge large language model (LLM), MPT-30B, has sent ripples through the AI community. MPT-30B emerges as a decoder-based LLM that marries open-source accessibility with commercial licensing, boasting remarkable potency. 

Notably, with a mere 30 billion parameters, which is just 17% of GPT-3's expansive 175 billion, MPT-30B outshines GPT-3 across various tasks. Source  

#5 Vicuna

Vicuna, a chat assistant of note, has been skillfully honed through the fine-tuning of LLaMA using conversational data sourced from ShareGPT's user interactions. 

Initial assessment, with GPT-4 as the discerning arbiter, illustrates Vicuna-13B achieving a remarkable quality level surpassing 90%* when juxtaposed with the prowess of OpenAI's ChatGPT and Google Bard. 

Additionally, Vicuna outshines its peers in the field, such as LLaMA and Stanford Alpaca, in over 90%* of cases. Source

You can also read our blog on ‘How Superteams.ai Helps Cloud GPU Companies Attract Developers’

What are steps to training large language models?

LLM training involves two phases: pre-training and task-specific training. 

Pre-training imparts general language understanding but demands substantial data, computational resources, and time. 

These large models need supercomputer setups with AI chips, which, along with maintenance and power expenses, result in investment for pre-training.

Now, let us break this in simple four steps to training of LLMs:

#1 Data collection and preprocessing 

To begin, collect the training data set that the LLM will learn from. 

This data can originate from diverse sources like books, websites, articles, and open datasets.

Here are some well-known public sources for finding datasets:

• Kaggle

• Wikipedia database

• Hugging Face

• Data.gov

• Google Dataset Search

Next, the data should be cleaned and prepped for training. 

This might include tasks like converting text to lowercase, eliminating stop words, and breaking down the text into token sequences.

#2 Selecting the right model and configuration

Big models like Google's BERT and OpenAI's GPT-3 employ the transformer deep learning structure, widely favoured for advanced NLP tasks. Several critical aspects of the model include:

• Count of layers in transformer blocks

• Quantity of attention heads

• Loss function

• Hyperparameters

When setting up a transformer neural network, you must define these aspects. The setup can vary based on your intended purpose and the training data. 

Moreover, the model's configuration has a direct impact on how long the training process takes.

#3 Model training

The model learns from the prepared text by using a method called supervised learning. While learning, the model looks at groups of words and learns how to guess the next word. 

It tweaks its knowledge based on how different its guess is from the actual next word. This happens many times until the model gets really good.

Because models and data are really big, training them needs lots of computer power. To make training faster, a technique called model parallelism is used. 

This spreads different parts of the big model across several computer chips, so it can be trained faster using many chips.

#4 Evaluation and fine-tuning

Lastly, once training is done, the model is checked with a separate test dataset that wasn't used during training. This helps see how well the model works. 

Depending on the test results, the model might need some adjustments. This could mean changing settings, trying different structures, or even training with more data to make it perform better.

What kind of content can be created through LLMs?

Large language models (LLMs) offer a diverse range of applications, encompassing:

• Language Transformation: 

LLMs proficiently convert text from one language to another, fostering cross-language comprehension.

• Chatbot and Natural Conversations

 LLMs play a crucial role in crafting chatbots and conversational AI platforms that engage users in authentic conversations, simplifying interaction.

• Creative Content Generation

LLMs are harnessed to produce varied content forms, such as articles, summaries, and product descriptions, ensuring grammatical accuracy and meaningful relevance.

• Condensed Textual Summaries

LLMs aid in compressing lengthy texts, such as news articles or research papers, into concise and easily digestible summaries, ideal for swift referencing.

• Emotion Insight

 LLMs conduct sentiment analysis, enabling businesses to grasp customer sentiments towards their products or services, fostering enhancements.

• Speech Comprehension Advancement

 LLMs elevate speech recognition systems by decoding the context and significance of spoken words, catering to specific requirements.

How Superteams.ai helps companies deploy open source LLMs?

At this juncture, a common thought arises: Is the domain of LLMs truly dominating the scene? Some might have anticipated the buzz to level off, yet it remains on a steady ascent. 

More investments are flowing into LLMs due to their substantial demand.

Beyond their effective performance, LLMs exhibit adaptability across a range of NLP tasks like translation and sentiment analysis. 

At Superteams.ai, we offer a team of skilled content creators who will assist your company in effectively using large language models (LLMs) in production, with a range of user-friendly features.

If you have further questions on large language models, book a call with Superteams.ai. We are happy to answer your questions.