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Introduction: LLMs

The ongoing revolution in generative AI owes its momentum to the formidable presence of large language models (LLMs). Built upon transformers, a robust neural architecture, LLMs serve as AI systems designed to comprehend and manipulate human language. Termed “large” due to their expansive parameter counts, ranging from hundreds of millions to billions, these models undergo pre-training with extensive text datasets.

LLMs serve as the cornerstone for renowned chatbots like ChatGPT and Google Bard. Notably, ChatGPT relies on GPT-4, an LLM crafted by OpenAI, while Google Bard operates on Google’s PaLM 2 model.

However, both ChatGPT and Bard, alongside other popular chatbots, share a commonality: their underlying LLMs are proprietary. This implies ownership by a company, with usage typically contingent upon purchasing a license. While such licenses confer rights, they may also impose usage restrictions and offer limited insight into the technology’s inner workings.

Yet, a parallel movement within the LLM realm is swiftly gaining momentum: open-source LLMs. In response to mounting concerns regarding the opacity and restricted accessibility of proprietary LLMs, predominantly controlled by industry giants like Microsoft, Google, and Meta, open-source LLMs aspire to democratize the burgeoning field of LMMs and generative AI, fostering transparency, accessibility, and innovation.

The Advantages of Embracing Open-Source LLMs

There exists a plethora of immediate and enduring advantages to selecting open-source LLMs over proprietary counterparts. Below, discover a compilation of the most compelling rationales:

Fortified Data Security and Privacy

The foremost concern with proprietary LLMs revolves around the peril of data breaches or unauthorized access to sensitive information by the LLM provider. Indeed, there have been numerous controversies regarding the alleged exploitation of personal and confidential data for training purposes.

By opting for open-source LLMs, organizations assume sole responsibility for safeguarding personal data, retaining complete control over its protection.

Cost Efficiency and Reduced Vendor Reliance

Many proprietary LLMs necessitate licensing for usage, posing a significant long-term expense that some companies, particularly SMEs, may find prohibitive. Conversely, open-source LLMs typically come without financial constraints, being freely available for utilization.

However, it’s essential to acknowledge that running LLMs demands substantial resources, even for inference alone, often entailing expenses for cloud services or robust infrastructure usage.

Transparency of Code and Model Customization

Enterprises embracing open-source LLMs gain access to comprehensive insights into their inner workings, including source code, architecture, training data, and training and inference mechanisms. This transparency constitutes the foundation for scrutiny and customization.

Given that open-source LLMs are accessible to all, including their source code, utilizing organizations can tailor them to their specific use cases.

Vibrant Community Backing and Stimulated Innovation

The open-source movement pledges to democratize LLM and generative AI technology’s utilization and accessibility. Permitting developers to scrutinize LLMs’ inner workings is pivotal for advancing this technology. By lowering entry barriers for coders globally, open-source LLMs can catalyze innovation, refining models to minimize biases and enhance accuracy and overall performance.

Tackling AI’s Environmental Footprint

With the proliferation of LLMs, researchers and environmental advocates are sounding alarms about the carbon emissions and water consumption necessary to sustain these technologies. Proprietary LLMs seldom disclose data regarding the resources required for training and operating LLMs, nor their associated environmental impact.

Through open-source LLMs, researchers gain greater visibility into this data, paving the way for innovative solutions aimed at mitigating AI’s environmental footprint.

1. LLaMA 2:

Meta’s groundbreaking open-source Large Language Model (LLM), LLaMA 2, signifies a shift in the LLM landscape, offering enhanced capabilities for research and commercial applications. With 7 to 70 billion parameters and fine-tuning through Reinforcement Learning from Human Feedback (RLHF), LLaMA 2 serves as a versatile generative text model, adaptable for tasks such as chatbots and natural language generation.

2. BLOOM:

Developed through a collaborative effort involving volunteers from over 70 countries and researchers from Hugging Face, BLOOM epitomizes the democratization of generative AI. Sporting an impressive 176 billion parameters, BLOOM seamlessly generates coherent and precise text in 46 languages and 13 programming languages. This emphasis on transparency is underscored by its readily accessible source code and training data.

3. BERT:

A pioneer in the LLM landscape, BERT (Bidirectional Encoder Representations from Transformers) showcases the potential of transformer-based architectures. Introduced by Google in 2018, BERT rapidly achieved state-of-the-art performance in various natural language processing tasks, boasting thousands of open-source pre-trained models for diverse applications.

4. Falcon 180B:

The Technology Innovation Institute of the United Arab Emirates introduces Falcon 180B, boasting impressive capabilities with 180 billion parameters and 3.5 trillion tokens. Outperforming predecessors in various NLP tasks, Falcon 180B bridges the gap between proprietary and open-source LLMs, offering commercial and research potential with robust computing resources.

5. OPT-175B:

Meta’s Open Pre-trained Transformers Language Models (OPT) initiative presents OPT-175B as a pinnacle in open-source LLMs. With similar performance to GPT-3, OPT-175B offers pre-trained models and accessible source code for research applications, although it is limited to non-commercial use.

6. XGen-7B:

Salesforce enters the LLM arena with XGen-7B, prioritizing extended context windows and efficiency with 7 billion parameters. Despite its compact size, XGen-7B delivers impressive results and is available for both commercial and research purposes, showcasing Salesforce’s commitment to advancing language technology.

7. GPT-NeoX and GPT-J:

Developed by EleutherAI, GPT-NeoX and GPT-J offer formidable alternatives to proprietary LLMs. With 20 billion and 6 billion parameters respectively, these models deliver high accuracy across various domains, available for free through the NLP Cloud API for diverse natural language processing t

Choosing the Right Open-Source LLM for Your Requirements

The open-source LLM ecosystem is rapidly expanding, offering a plethora of options for diverse applications. Amidst this dynamic landscape, selecting the ideal open-source LLM entails careful consideration of several key factors:

1. Purpose: Clarify your objectives and whether the chosen LLM aligns with your intended use. Be mindful of licensing restrictions, especially if considering commercial ventures.
   
2. Necessity: Evaluate whether leveraging an LLM is indispensable for your project. While LLMs offer extensive capabilities, alternative approaches may suffice, potentially saving costs and resources.
   
3. Accuracy: Assess the level of precision required for your tasks. Larger LLMs tend to yield higher accuracy due to their expansive parameters and training data.
   
4. Budget: Consider the financial implications associated with operating your chosen LLM. Larger models necessitate significant resources for training and operation, potentially increasing infrastructure costs.
   
5. Pre-trained Models: Explore pre-trained models tailored to specific use cases, as they offer a cost-effective and time-efficient alternative to training from scratch.

The Future of Language Technology

In Conclusion:

The open-source LLM landscape is characterized by innovation and accessibility, promising opportunities beyond the domain of major players. While this list highlights seven prominent LLMs, the breadth of options continues to expand rapidly.

FAQs: Understanding Open-Source LLMs

1. What is a Large Language Model (LLM)?

• Answer: An LLM is an AI system designed to understand and generate human language. These models are built on transformers, a type of neural network, and are trained on large text datasets. They can perform tasks like answering questions, generating text, and more.

2. How do LLMs like ChatGPT and Google Bard work?

• Answer: ChatGPT and Google Bard are powered by LLMs—specifically, ChatGPT uses GPT-4, developed by OpenAI, and Google Bard uses PaLM 2 from Google. These models process and generate text based on the input they receive.

3. What’s the difference between proprietary and open-source LLMs?

• Answer: Proprietary LLMs are owned by companies and require a license to use, which might come with restrictions. Open-source LLMs, on the other hand, are freely available to the public, offering more transparency, customization, and community support.

4. Why should I consider using open-source LLMs?

• Answer: Open-source LLMs offer several advantages, including better data security, cost efficiency, transparency, and the ability to customize the model to your needs. They also foster innovation by allowing developers to improve and adapt the models.

5. What are some examples of open-source LLMs?

• Answer: Examples include:
  • LLaMA 2: Developed by Meta, suitable for research and commercial use.
  • BLOOM: A collaborative project with 176 billion parameters, supporting multiple languages.
  • BERT: Introduced by Google, it’s widely used for various NLP tasks.
  • Falcon 180B: From the UAE’s Technology Innovation Institute, with 180 billion parameters.
  • OPT-175B: Another Meta model, similar to GPT-3.
  • XGen-7B: Developed by Salesforce, known for efficiency.
  • GPT-NeoX and GPT-J: Created by EleutherAI, offering free alternatives for NLP tasks.

6. How do I choose the right open-source LLM for my project?

• Answer: Consider the following:
  • Purpose: What do you want to achieve? Make sure the LLM fits your goals.
  • Necessity: Do you really need an LLM, or would a simpler solution work?
  • Accuracy: Larger models usually offer better accuracy.
  • Budget: Consider the costs of running the model, especially for larger ones.
  • Pre-trained Models: Check if there are pre-trained models that fit your needs, as they can save time and money.

7. What’s the future of open-source LLMs?

• Answer: The future looks promising, with ongoing innovation and more models becoming available. Open-source LLMs are likely to continue growing in importance, offering alternatives to the dominant models from major tech companies.

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learn more about The Future of Language Technology from Salesforce.