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AI is rapidly progressing. It has given way to a host of very powerful LLMs that are now not only within the domain of large tech giants but are accessible through open-source initiatives as well. Applications of such models range from NLP (Natural Language Processing) to code generation and they are fast becoming basic needs in health, education, and customer service. By 2024, some open-source LLMs, without any doubt, would be outstanding in performance, community support, and versatility. This article delves into the details of top 10 open-source LLMs for 2024.
Open-Source LLMs can be used for controllability and transparency. Cost is not necessarily a positive variable, as self-hosting with all ad hoc tooling and maintenance it requires, is very expensive. However, Managed Services like AWS Bedrock, OctoAI, Replicate, or similar still can't compete in performance and cost with the best of breed Proprietary offerings.
Generally, open- source models are superior in terms of debuggability, explanation, and the ability to extend their capabilities through fine-tuning. This will help you steer the LLMs toward your specific needs, defined by the problem domain.
LLaMA is one of the most resource-efficient open-source LLMs (Large Language Model) developed by Meta AI. Resource efficiency was one of the key focuses while designing LLaMA; it outperforms many of its predecessors with respect to computational requirements without much compromise in performance. This model is highly adaptable and can be easily fine-tuned on a wide range of NLP tasks ranging from text classification to machine translation.
Key Features
a. Multiple model sizes ranging from 7 billion to 65 billion parameters.
b. Fine tuning on smaller datasets is possible.
c. Active community with tons of documentation.
GPT-NeoX by EleutherAI aims to be a very flexible and powerful LLM, demonstrating all the complete functionalities of the models in the ranks of OpenAI's GPT-3 can achieve. EleutherAI constructed it upon the Megatron-LM framework. Various configuration options are also made available to address a wide variety of use cases. GPT-NeoX will be very useful to researchers and developers, seeking an extremely powerful model that can be customized to cater to a wide spectrum of NLP tasks.
Key Features
a. It supports models with up to 20 billion parameters.
b. Extensive API for easy deployment and integration.
c. Regular updates and strong community support.
Bloom is the ambitious project from BigScience, a worldwide research initiative to democratize AI. It's known in particular for its collaborative model development process in which hundreds of researchers contributed to the project worldwide. Bloom was designed to be multilingual, and thus especially useful for global applications.
Key Features
a. Supports over 60 languages.
b. Ethical AI practices, focused on transparency and inclusivity.
c. High model sizes, optimized for both research and production.
As a spin-off from Meta's LLaMA model, the open-source community is working on Open LLaMA to increase its capacity even more. The focus of this version will be on easier access and applicability to a broader field of applications. Therefore, this model is especially applicable in academic research and small applications in industries.
Key Features
a. Community-driven improvements and optimizations.
b. Improved support for fine-tuning and transfer learning.
c. High-frequency updates and improvements.
Cerebras Systems, well-known for their specialized AI hardware, has open-sourced a family of LLMs called Cerebras-GPT and optimized them for their wafer-scale engine. Notably, the models have speed and efficiency that makes them ideally positioned for applications in real-time.
Key Features
a. Optimized performance on specialized AI hardware.
b. Supports large-scale deployment with minimal latency.
c. Well-suited for both research and commercial use.
OPT is Open Pretrained Transformer, which is an open-source, large-scale language model developed by the Facebook AI Research (FAIR) unit for general applicability in uses ranging from text generation to sentiment analysis. Lastly, fairness is also considered and infused with various strategies in reducing bias.
Key Features
a. Small to extra-large model sizes are available.
b. Infused with fairness strategies and bias mitigation techniques.
c. Attention to ethical AI practices is strong.
This is an open-source model developed by Google Research. It considers all NLP tasks as a text-to-text problem. Therefore, it can be easily fine-tuned and applied to tasks ranging from translation and summarization, to answering of questions. Since it is open-source, it has also witnessed rapid adoption in research and industry use.
Key Features
a. Unified framework for diverse NLP tasks.
b. Pre-Trained models of different sizes are available.
b. Highly extensible and adaptable towards the applications at hand.
RedPajama is an open-source project of Together AI, which provides a model that has been trained on the scale and level of competence similar to some of the propriety models like GPT-4. The model emphasizes accessibility and contributions from the community, resulting as a research-friendly LLM. This project is taken by both educators and developers because of the ease of use and extensive documentation.
Key Features
a. It allows multimodal tasks, such as text and image generation.
b. Comes with a lot of API and developer-friendly libraries.
c. It's adding contributions to the AI research community daily.
BLOOMZ is an extension to the Bloom project and helps in the process of zero-shot and few-shot learning. It is thus very useful in tasks where labelled data is insufficient or nearly non-existent. So, BLOOMZ would be a great choice for developers working on niche domains where the AI they train does not require any extensive training data.
Key Features
a. Strong zero-shot and few-shot learning capabilities.
b. Multilingual support, emphasizes low-resource languages.
c. Ethical considerations are involved during its model design.
Falcon is one of the highly performing LLMs developed at the Technology Innovation Institute at Abu Dhabi. Nonetheless, the most special core selling point is the ability to run efficiently at large-scale industrial applications. Furthermore, Falcon has been optimized for accuracy and high-speed performance, ideal for the most demanding NLP tasks.
Key Features
a. Highly scalable, suitable to be deployed in large-scale forms.
b. Optimized for both CPU and GPU hardware.
c. Strong focus on industrial applications and commercial use cases.
The landscape of open-source LLMs has never been so dynamic before 2024. These models are used not just tools by researchers but have become fundamental in industries and applications across the world. From Meta's LLaMA to the collaborative efforts of the Bloom project on open-source LLMs, they provide an accessible, ethical, and powerful alternative to the proprietary models.
These models will undoubtedly be of paramount importance in developing the future of technology and society as AI evolves. By diving into some of these top open-source LLMs, whether you are a developer, researcher, or business leader, you would be well-equipped with the very requirements to cause innovation and be the best in your respective industry.
1. What is a Large Language Model (LLM)?
A: A Large Language Model (LLM) is a type of artificial intelligence model designed to understand and generate human-like text based on large datasets. These models are used for tasks such as text generation, translation, summarization, and more.
2. Why are open-source LLMs important?
A: Open-source LLMs are important because they provide accessible and transparent AI tools to the broader community, allowing researchers, developers, and businesses to leverage advanced language processing without relying on proprietary solutions.
3. How do open-source LLMs compare to proprietary models?
A: Open-source LLMs are often comparable to proprietary models in terms of performance and versatility. However, they offer the added benefits of transparency, community support, and customization, making them more flexible for specific use cases.
4. Can someone use open-source LLMs for commercial purposes?
A: Yes, many open-source LLMs are licensed for commercial use, but it's important to review the specific licensing terms of each model to ensure compliance.
5. What are the key factors to consider when choosing an open-source LLM?
A: When choosing an open-source LLM, consider factors such as the model's performance, scalability, language support, community support, and compatibility with your existing infrastructure.
