The ongoing revolution in generative AI owes much to the advent of large language models (LLMs). Leveraging transformers, a robust neural architecture, LLMs serve as indispensable AI systems designed to model and process human language. Their "large" designation stems from their massive parameter counts, often numbering in the hundreds of millions or even billions, pre-trained on vast corpora of text data.

Foundational to popular chatbots like ChatGPT and Google Bard, LLMs, particularly exemplified by GPT-4 from OpenAI and Google's PaLM 2 model, power these conversational agents. However, it's important to note that these LLMs are proprietary, meaning they're owned by companies and accessible to customers through licensing agreements. While such licenses afford rights, they also entail potential usage restrictions and limited insights into the underlying technology.

In response to concerns regarding transparency and accessibility, particularly with Big Tech's dominance in controlling proprietary LLMs, a burgeoning movement in open-source LLMs is rapidly gaining momentum. Promising increased accessibility, transparency, and innovation, open-source LLMs aim to democratize the realm of LMMs and generative AI.

This article sets out to delve into the top open-source LLMs available as of 2023. Despite only a year passing since the emergence of ChatGPT and the popularization of proprietary LLMs, the open-source community has made significant strides, offering a variety of LLMs tailored for diverse applications. Join us as we explore the most prominent ones!

Benefits of Using Open-Source LLMs

1. Enhanced Data Security and Privacy Concerns about data leaks and unauthorized access to sensitive information by proprietary LLM providers have sparked controversies. Open-source LLMs shift responsibility for data protection solely to companies, granting them full control over personal data.
2. Cost Savings and Reduced Vendor Dependency Unlike proprietary LLMs, which often entail licensing fees, open-source alternatives are typically free. However, utilizing LLMs, even for inference, necessitates significant resources, often requiring payment for cloud services or robust infrastructure.
3. Code Transparency and Language Model Customization Access to open-source LLM workings, including source code, architecture, and training mechanisms, enables transparency and customization. Companies leveraging open-source models can tailor them to their specific use cases, thanks to accessibility to source code.
4. Active Community Support and Fostering Innovation The open-source ethos democratizes access to LLM and generative AI technologies, fostering innovation worldwide. Open access to LLM workings enables developers to scrutinize and enhance models, reducing biases and enhancing performance.
5. Addressing the Environmental Impact of AI As concerns mount regarding the environmental footprint of LLMs, open-source alternatives provide insights into resource consumption. Unlike proprietary models, open-source LLMs offer transparency, enabling researchers to explore methods to mitigate AI's environmental impact.

Top Open-Source Large Language Models For 2024

1. LlaMA 2

In the realm of Large Language Models (LLMs), many industry leaders have chosen to develop their models in secrecy. However, Meta is defying this trend by introducing its powerful open-source LLM, Meta AI (LLaMA), signaling a significant shift in the market landscape.

Launched for both research and commercial purposes in July 2023, LLaMA 2 represents a milestone in the domain of LLMs. Boasting 7 to 70 billion parameters, this pre-trained generative text model has been fine-tuned using Reinforcement Learning from Human Feedback (RLHF). LLaMA 2 serves as a versatile generative text model suitable for various natural language generation tasks, including programming. Meta has already unveiled two customized versions of LLaMA 2: Llama Chat and Code Llama.

Furthermore, Mark Zuckerberg recently announced Meta's plans to release LLaMA3 in early 2024. With this upcoming release, Meta aims to advance toward artificial general intelligence, marking another significant development in the AI landscape.

2. BLOOM

Launched in 2022 following a year-long collaborative effort involving volunteers from over 70 countries and researchers from Hugging Face, BLOOM stands as an autoregressive LLM designed to extend text from a prompt across extensive text datasets, utilizing industrial-scale computational resources.

The unveiling of BLOOM marked a significant leap forward in democratizing generative AI. Boasting an impressive 176 billion parameters, BLOOM emerges as one of the most potent open-source LLMs, capable of generating coherent and precise text in 46 languages and 13 programming languages.

At the core of BLOOM lies transparency, a principle that underpins the entire project. With access to both the source code and training data, individuals can freely run, analyze, and enhance BLOOM to foster further advancements.

BLOOM is readily accessible through the Hugging Face ecosystem, offering users the opportunity to leverage its capabilities at no cost.

3. BERT

The foundational technology behind Large Language Models (LLMs) stems from a neural architecture known as transformers, introduced in 2017 by Google researchers in the seminal paper "Attention is All You Need." Among the earliest experiments to explore the capabilities of transformers was BERT.

Debuted in 2018 as an open-source LLM by Google, BERT, short for Bidirectional Encoder Representations from Transformers, quickly established itself as a leader, showcasing state-of-the-art performance across various natural language processing tasks.

Recognized for its pioneering features during the nascent stages of LLM development and its open-source framework, BERT has emerged as one of the most renowned and extensively utilized LLMs. Notably, in 2020, Google disclosed its integration of BERT into Google Search across more than 70 languages.

Presently, a plethora of open-source, freely accessible, and pre-trained BERT models cater to diverse use cases, including sentiment analysis, clinical note examination, and toxic comment detection.

4. Falcon 180B

If the Falcon 40B made waves within the open-source LLM community, earning the top spot on Hugging Face's leaderboard for open-source large language models, the arrival of the Falcon 180B signals a significant stride toward narrowing the gap between proprietary and open-source LLMs.

Unveiled by the Technology Innovation Institute of the United Arab Emirates in September 2023, the Falcon 180B is undergoing training with a staggering 180 billion parameters and processing 3.5 trillion tokens. Leveraging this formidable computational power, the Falcon 180B has already showcased superior performance over LLaMA 2 and GPT-3.5 across various NLP tasks. Hugging Face even suggests that it poses a potential challenge to Google's PaLM 2, the LLM fueling Google Bard.

While available for both commercial and research purposes at no cost, it's essential to acknowledge that the Falcon 180B demands substantial computing resources to operate efficiently.

5. OPT-175B

The release of the Open Pre-trained Transformers Language Models (OPT) in 2022 marked another important milestone in Meta’s strategy to liberate the LLM race through open source.

OPT comprises a suite of decoder-only pre-trained transformers ranging from 125M to 175B parameters. OPT-175B, one of the most advanced open-source LLMs in the market, is the most powerful brother, with similar performance to GPT-3. Both pre-trained models and the source code are available to the public.

Yet, if you’re thinking in developing an AI-driven company with LLMs, you’d better think in another one, as OPT-175B is released under a non-commercial license, allowing only the use of the model for research use cases.

6. XGen-7B

More and more companies are entering the competitive arena of Large Language Models (LLMs). One of the latest contenders to join the fray was Salesforce, which introduced its XGen-7B LLM in July 2023.

In contrast to many open-source LLMs that prioritize providing expansive responses with limited context, the XGen-7B aims to support longer context windows, offering more comprehensive information. Particularly noteworthy is the most advanced iteration, XGen-7B-8K-base, which facilitates an 8K context window, encompassing both input and output text.

Efficiency stands as another key focus in XGen's design, employing a mere 7B parameters for training, significantly fewer than many powerful open-source LLMs such as LLaMA 2 or Falcon.

Despite its relatively compact size, XGen demonstrates remarkable performance capabilities. The model is available for both commercial and research purposes, with the exception of the XGen-7B-{4K,8K}-inst variant, trained on instructional data and RLHF, which is released under a noncommercial license.

7. GPT-NeoX and GPT-NeoX

Developed by researchers at EleutherAI, a non-profit AI research lab, GPT-NeoX and GPT-J emerge as two noteworthy open-source alternatives to GPT.

With GPT-NeoX boasting 20 billion parameters and GPT-J featuring 6 billion parameters, these models offer substantial capabilities despite their parameter count falling short of the over 100 billion parameters seen in more advanced LLMs.

Trained on 22 high-quality datasets sourced from diverse origins, GPT-NeoX and GPT-J demonstrate versatility across multiple domains and a wide range of use cases. Notably, unlike GPT-3, neither GPT-NeoX nor GPT-J have undergone training with RLHF.

From text generation and sentiment analysis to research endeavors and marketing campaign development, GPT-NeoX and GPT-J are equipped to handle various natural language processing tasks.

Both LLMs are freely accessible through the NLP Cloud API, facilitating accessibility and usability across different projects and applications.

8. Vicuna 13-B

Vicuna-13B, an open-source conversational model, emerges from the fine-tuning of the LLaMa 13B model using user-shared conversations sourced from ShareGPT.

As an intelligent chatbot, Vicuna-13B boasts myriad applications across various industries including customer service, healthcare, education, finance, and travel/hospitality.

In a preliminary evaluation where GPT-4 served as a judge, Vicuna-13B demonstrated remarkable performance, achieving over 90% quality compared to ChatGPT and Google Bard. Furthermore, it outperformed other models like LLaMa and Alpaca in more than 90% of cases.

Choosing the Right Open-Source LLM for Your Needs

The open-source LLM landscape is undergoing rapid expansion, with more open-source models available than proprietary ones. As developers worldwide collaborate to enhance existing LLMs and develop optimized ones, the performance gap between open-source and proprietary models may soon narrow.

Amidst this dynamic environment, selecting the right open-source LLM can be challenging. Here are some factors to consider before choosing a specific open-source LLM:

1. Purpose: Determine what you intend to achieve. While most open-source LLMs are freely available, some may have restrictions for commercial use. Ensure compatibility with your goals and be mindful of licensing limitations if starting a business.
2. Necessity: Assess whether you truly need an LLM for your project. While LLMs offer vast potential, they may not be essential for every application. If feasible, explore alternatives to save costs and resources.
3. Accuracy: Consider the level of accuracy required for your task. Generally, larger LLMs with more parameters and training data offer higher accuracy. Models like LLaMA or Falcon are suitable for tasks demanding high precision.
4. Costs: Evaluate the financial investment needed. Larger models entail higher training and operational costs, whether through increased infrastructure or cloud service charges. Factor in these expenses when planning your project.
5. Pre-trained Models: Explore pre-trained models tailored to specific use cases. Leveraging existing models can save time and resources compared to training a model from scratch. Many open-source LLMs cater to diverse applications, offering solutions for various tasks.

By considering these factors, you can make an informed decision and choose the most suitable open-source LLM for your project.

The open-source LLM community is experiencing a thrilling evolution. As these models rapidly progress, it becomes evident that the generative AI arena won't be monopolized solely by major players with extensive resources.

While we've explored just eight open-source LLMs, the actual count is significantly higher and continues to expand swiftly. At Digital Bricks, we're committed to keeping you informed about the latest developments in the LLM landscape, offering courses, articles, and tutorials to deepen your understanding. Embark on your AI journey today with our AI Mastery course. Join us as we delve into the fascinating world of LLMs and beyond.

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