Open Source LLMs: Navigating the Landscape

In today’s fast-paced global business environment, organizations are constantly looking for innovative solutions to improve operational efficiency and gain a competitive edge over their competitors. One such technology that has received much attention in the business world is open-source large language models (LLMs). LLMs have emerged as powerful tools that can easily transform the way we interact with modern technology and handle various tasks.

According to a recent survey by Cutter Consortium, approximately 34% of organizations worldwide plan to integrate LLMs into their operations. [1] This is quite impressive, especially when you consider that many organizations outside the leading tech giants have little to no experience working with large language models. The strong interest in LLMs, open-source ones in particular, is due to the fact that these models can understand and generate human-like text, answer questions, and perform a wide variety of other language-processing tasks.

This post will provide an in-depth review of what open-source large language models are, how they work, and a list of the top LLMs available today.

Open Source LLMs Defined: What They Mean

A Large Language Model (LLM) basically refers to a type of advanced Artificial Intelligence (AI) model trained using huge amounts of data, mostly from the internet, to comprehend and generate human-like text. LLMs rely on deep learning techniques like the transformer model architecture to process and analyze language patterns. This way, large language models are able to understand and recognize the relationships between various words and concepts using a self-attention mechanism. [2]

Generally, there are two types of large language models (LLMs); open-source and closed/proprietary. [3] Unlike closed LLMs like ChatGPT, Bard, and GPT-3, open-source LLMs make their training datasets, source code, model architectures, and weights publicly available.

This means that open-source LLMs can be accessed, utilized, modified, and distributed by anyone. The accessibility, availability, and transparency of LLMs help promote the reproducibility and decentralization of Artificial Intelligence (AI) systems.

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Open Source LLMs: Understanding How They Work

Understanding how open-source large language models work is vital for harnessing their power. That said, here is a step-by-step guide on how they work:

Pre-training

As aforementioned, open-source LLMs require huge amounts of data for training. In fact, most state-of-the-art LLMs are trained using diverse data sources such as books, websites, articles, and other written content on the internet.

Open-source LLMs usually undergo pre-training, where they’re exposed to a wide variety of unlabeled text data. During this period, these models learn to predict masked words within sentences, which enables them to understand grammar, semantics, contextual representations, and syntax. This process may take several days or even weeks.

Tokenization

During pre-training, the text input is tokenized using a tokenizer. In this stage, text input is split into smaller units called tokens. A token can be as small as a single character or as long as a whole word. The main idea behind tokenization is to allow the open-source LLM to handle uncommon words, characters, and phrases effectively. Every token is usually assigned a unique numerical ID for subsequent processing.

Fine-tuning of open-source LLM

Once an open-source LLM has been trained on a large dataset, it proceeds to be fine-tuned on specific tasks. This process involves training the open-source LLM on a smaller task-specific dataset to help adapt its parameters to perform various tasks such as sentiment analysis, language translation, and text summarization. As a model is being fine-tuned, its weights and biases are updated based on the task-specific dataset used.

Input encoding

During fine-tuning, the input text is encoded to help preserve the context and relation between various words and sentences. This way, a machine will be able to understand and identify the pattern in any given text as well as the context of sentences.

Special tokens such as SEP (for separate text segments) and CLS (for classification) are then added to the encoded input. Notably, the input sequences are cut to a fixed length to ensure consistent sample processing.

Training and optimization

After an open-source LLM has been fine-tuned and equipped with a classification head, it undergoes further training on the labeled dataset. This training process involves forward-passing the encoded text input through the LLM, calculating the value of the output layers from the input data, and backpropagating to minimize losses and make the model more accurate. In this stage, you can use any optimization technique you want, including RMSprop, Adam, and Stochastic Gradient Descent (SGD). [4]

Inference

When using an open-source large language model, you provide it with input text, and it generates text outputs by predicting the next token in sequence. This process is known as inference and can be performed in real time. Inferences are important because they allow the model to generate output texts that are more relevant and appropriate based on input prompts.

Benefits of open-source LLMs for organizations

These tools offer organizations several benefits, including the following:

Enhanced data security and privacy

Open-source LLMs implementation strategy provide organizations with the option to deploy the models on their own infrastructure (on-premises or in a private cloud setting). This gives the organizations full control over their data and ensures sensitive information remains in the organization.

Cost savings

Generally, these LLMs are much cheaper than closed-source/proprietary LLMs. This is mainly because there are licensing fees associated with using open-source LLMs.

Reduces vendor dependency

When using closed-source/proprietary LLMs, users become increasingly dependent on one vendor for updates, ongoing maintenance, and support. However, by adopting open-source LLMs, users can easily benefit from community contributions and rely on multiple vendors for support and updates.

Code transparency

Open-source LLMs are widely renowned for offering transparency into their underlying code. As a result, it’s easier for users to know how a certain model works and validate its functionality before integrating it into their existing systems.

Which LLM model is the best?

Here is a curated list of the top LLMs organizations can use for their data science and machine learning (ML) projects:

BERT

BERT is an open-source LLM created by Google and generally used for a wide variety of natural language processing tasks. It can also be used to generate embeddings to train other models. With a model size of approximately 340 million parameters, tie LLM has been trained using a huge and diverse dataset comprising over 3.3 billion words from Wikipedia, BookCorpus, and other sources across the internet.

LLaMA (Large Language Model Meta AI)

LLaMA is an open-source LLM developed by Microsoft and Meta AI with the inference code available under the GPL-3 license. [5] This means that users can study LLaMA’s architecture and use the inference code to run the model and even generate text outputs. Users can also make changes or improvements to the existing code and share it with other users.

In addition to understanding and generating text, LLaMA can also understand images, making it useful for many multimodal tasks. Currently, this open-source LLM comes in three major model sizes trained on 7,13, and 70 billion parameters.

Vicuna

Vicuna is a state-of-the-art open-source large language model developed by Large Model Systems (LMSys), a popular AI research organization. This fine-tuned LLM is based on ShareGPT and demonstrates comparable performance to closed-source LLMs such as Google’s Bard and OpenAI’s ChatGPT.

BLOOM

BLOOM is a cutting-edge multilingual open-source LLM developed by BigScience. This model is built using an architecture similar to GPT-3.5 and is ideally designed to foster scientific collaborations and breakthroughs. With a massive model size of approximately 176 billion parameters, BLOOM outranks many LLMs in terms of scale. Some of BLOOM’s best features include cultural sensitivity, inclusive language, multilingual competence, and ethical communication.

FALCON-40B

Falcon-40B is the brainchild of the Technology Innovation Institute, released under an Apache 2.0 license, which permits commercial use. As the name suggests, Falcon-40B has an impressive model size of 40 billion parameters and has been trained on 1000 billion tokens of the RefinedWeb dataset. This model works by predicting the next word in a sequence and is expected to revolutionize several natural language processing tasks.

Final thoughts

Open-source large language models have the potential to reshape and revolutionize AI-driven organizations. By encouraging transparency, collaboration, and ethical development, open-source LLMs have what it takes to build a more inclusive and innovative AI community.

Additionally, their adaptability, versatility, and affordability make them a great option for organizations that do not have the budget to train their own models from scratch.

References

[1] Cutter.com. Enterprises Are Keen on Adopting LLMs, But Issues Exist. URL: https://www.cutter.com/article/enterprises-are-keen-adopting-large-language-models-issues-exist. Accessed September 14, 2023
[2] Medium.com. Attention Networks: A Simple Way To Understand Self Attention. URL: https://medium.com/@geetkal67/attention-networks-a-simple-way-to-understand-self-attention-f5fb363c736d. Accessed September 14, 2023
[3] Medium.com. Types of Open Source LLMs. URL: bit.ly/3Rv3wnC. Accessed September 14, 2023
[4] Analyticsvidhya.com. Comprehensive guide on Deep Learning Optimizers. URL: https://www.analyticsvidhya.com/blog/2021/10/a-comprehensive-guide-on-deep-learning-optimizers/, Accessed September 14, 2023
[5] Fossa.com. Open Source Software Licenses 101. URL: https://bit.ly/3Ru6ybB. Accessed September 14, 2023