Understand how the best open-source LLMs are on the verge of taking generative AI to new dimensions to provide unprecedented model transparency, customization, and innovation—and why these models are fast positioned as indispensable tools for developers and researchers.
LLMs Drive the Generative AI Revolution: These AI systems, based on transformers, process human language and are called “large” because they have hundreds of millions or even billions of parameters, all prepare-trained on huge text data sets.
LLMs are the backbones of popular chatbots—some of which include ChatGPT and Google Bard. For instance, ChatGPT deploys GPT-4 developed under the sleeve of OpenAI, while Google’s bard uses Google’s PaLM 2. Such chatbots have backbones based on some proprietary LLM. Therefore, these bot models are owned by companies and are ridden with licenses to apply them, hence a lot of restrictions and very little insight into how they go about their work.
It is the transparency and accessibility concerns that fuel an openness movement for LLMs. If open-sourced, LLMs would reportedly make AI much more accessible to use, more transparent, and innovative. Everybody can evolve and improve these models for use.
This paper will discuss some of the top open-source LLMs available in 2023. Although a pretty new field, the open-source community has made huge strides. It is an exciting perspective for developers and researchers to have multiple open-source LLMs at their disposal for various purposes. Scroll down to learn about some of the most actively deployed open-source LLMs today and how they are most likely going to shape the future of AI.
Benefits of Using Open-Source LLMs
Choosing open-source LLMs over proprietary ones has a lot of benefits, from short-term to long-term. Open-source models provide better data security and privacy since companies can fully be in charge of their data. They save costs and reduce vendor dependencies, as they are generally free to use. Other than that, open-source LLMs bring openness and possible customization: firms will be able to tailor models according to their needs. Moreover, it facilitates collaboration among various people for innovating and improving in the open-source community. Not least among others, they aid in saving resource usage through the illumination of environmental concerns.
1) Enhanced data security and privacy
One of the major concerns is data security, especially since there is a risk of possible data leakage or unauthorized access by the LLM provider. There has been a good deal of controversy gained widespread attention to the use of personal and private data for these models’ training. On the other hand, open-source LLMs can offer companies full control over their data for more protection and privacy. Because open-source models are kept in-house, companies avoid the risk of depending on external providers for data security. That makes open-source LLM more secure.
2) Cost savings and reduced vendor dependency
Proprietary LLMs are usually supposed to be under an expensive license, which may be too heavy a financial burden for a company to bear, let alone small and medium enterprises. On the other side, open-source LLMs are usually free, hence they contribute to cost minimization and reduce dependence on vendors. However, it should not remain without mention that even though this software is at no charge, conducting such models requires enormous resources, such as Cloud Services or powerful infrastructure. That means that though you save by licensing accrued fees, you’ll still have to spend on the right tech to get value from open-source LLMs.
3) Code transparency and language model customization
Source code, architecture, and training data are open in open-source LLMs. This transparency avails the opportunity to get into the inner details of how they work, so you can customize them for your purposes. Whether you want to tinker with the model for better performance or apply it to a particular use case, access to all the goings-on at the interior makes it much easier. This means that if companies really want to wring their AI tools dry, at will, then a high degree of flexibility is involved since one can change and upgrade the models without being limited by the constraints and limitations of any proprietary systems.
4) Active community support and fostering innovation
The open-source movement democratizes LLM and generative AI technologies by opening access for all and letting developers from all parts of the world take up such models and improve them. In this way, it works as a driver of innovation and reduces bias, making models more accurate and at par with their performance. Opening up access, open-source LLMs invite a wide array of ideas and improvements, which is what makes for better, more inclusive AI systems. It’s a community-driven approach. The technology is driven to the forefront in areas that no single proprietary model can ever achieve. Basically, open-source LLMs are about shared progress and collective growth in the AI world.
5) Addressing the environmental footprint of AI
People are becoming concerned about the ecological impact of LLMs concerning carbon footprint and water usage. Most commercial LLMs do not make public the resources required to train and run them, thereby making their real impact difficult to assess. However, open-source LLMs are much more transparent concerning information needed for research purposes in pursuit of ways through which AI can be made eco-friendly. In so doing, AI developers can contribute to the community’s effort to reduce their ecological footprint by working with open-source models for more sustainable and responsible AI development.
1. LLaMA2:
It is in the openness of its Large Language Model Meta AI, LLaMA, and an improved model referred to as LLaMA 2 that Meta has done a great deal of work. LLaMA 2 is a generative text model with variable sizes ranging between 7 to 70 billion parameters under intensive fine-tuning using RLHF since July 2023; hence, very powerful for use, even as a chatbot, and mostly apt for many natural language generation tasks, including programming. This move by Meta, opening up such a robust model for use, has been gargantuan for advanced AI tooling and can further be democratized in many more ways for other use cases.
2. BLOOM:
Activated in 2022, BLOOM is a very exciting autoregressive LLM vision from Hugging Face, containing 176 billion parameters. It can understand 46 distinct languages and 13 programming languages, making it one of the most robust open-source LLMs available today. What makes BLOOM so special is its insistent transparency. Hugging Face shares the source code and data used for training BLOOM openly with users so that everyone can understand its functioning and even improve upon it. It is this kind of openness that will breed trust and, more importantly, act as a stimulus toward innovation and empiricism in the AI community. BLOOM is one unique model any person would find interesting to explore for LLM potential.
3. BERT:
In 2018, Google’s BERT dropped a bomb on the world of open-source LLM. It is known for setting advanced performance on many natural language processing tasks. This unique approach not only singled out BERT as one of the top models but was soon greatly accepted by the community. Even Google itself used BERT to enhance searching in over 70 languages on Google Search. Its success is an indicator of how far open-source LLMs can go in democratizing advanced language understanding tools for a wide segment of people and driving improvements in our interaction with technology.
4. Falcon 180B:
The Technology Innovation Institute in the UAE published the 180B version, Falcon 180B, in September 2023. This model is of 180 billion parameters with 3.5 trillion tokens; it is significantly powerful. It has already outperformed LLaMA 2 and GPT-3.5 on several NLP tasks. Best of all, Falcon 180B is free to use for commercial purposes and research. Note, however, that it requires substantial computing resources to run effectively. It’s just that, while it’s an excellent tool itself, you will need the proper setup to get the best out of it.
5. OPT-175B:
Sweep from 125 million to 175 billion: these are the dimensions of Meta’s Open Pre-trained Transformers, OPT. Among these, the most advanced is OPT-175B, making it an ultra-powerful open-source LLM. To be noted, however, OPT-175B has been under a non-commercial license upon its release; therefore, it can only be re-used under certain conditions. It thus serves as a really good tool for institutions in academia and research where they could play with some advanced AI models without the costs of proprietary models. A certain kind of restriction is being considered for commercial use, but OPT-175B would be of great help in providing resources to help push the limits of large language models.
6. XGen-7B:
In general, Salesforce’s July 2023 XGen-7B aims at longer context windows with native efficiency. To be constructed with 7 billion parameters, comparatively, it is not really small. One of the really positive aspects of XGen-7B is its availability for commercial and research use applications. Though small in size, this model is optimized to handle tasks efficiently, thus proving that sometimes size really doesn’t matter when it comes to LLMs. If you need a heavyweight, powerful, and efficient model, the XGen-7B isn’t one to turn a nose up toward.
7. GPT-NeoX and GPT-J
EleutherAI brought into existence two amazing open-source models: GPT-NeoX and GPT-J. Both have 20 billion and 6 billion parameters respectively. Equally as good as previous models in a wide array of tasks relating to NLP, from text generation to feeling analysis, these models do an extraordinary job. The best part is you can access them for free via the NLP Cloud API. You get strong alternatives without having to break the bank for their proprietary counterparts in this case. Unless someone wants to get into natural language processing on a shoestring budget, GPT-NeoX and GPT-J stand out as some of the coolest models.
Choosing the Right Open-Source LLM for Your Needs
The open-source LLM space is newer than the almost decadal-old proprietary ones, and today, there are more open-source LLMs available than there are proprietary; the performance gap corroborates this point: that collective genius of developers across the world in enhancing existing LLMs or creating better ones has reduced the difference substantially.
In this exciting environment, picking the right open-source LLM for your needs can become overwhelming. Here are some very critical things to consider before moving in with a certain open-source LLM:
- What purpose do you intend to use it for?
This is the very first thing you should know. Although the majority of open-sourced LLMs are available for any use, a few are only restricted to research use. If you’re planning on starting a company or using the model commercially, you should keep licensing limitations in mind. You want to make sure that the LLM you choose is suitable for your intended use case and avoids legal problems. - Why do you need an LLM?
This again is a very relevant question. Today, it is LLMs that are the hotcake, and everyone speaks about its huge potential. However, being in a craze does not necessarily mean that it is a must for all projects. If you can achieve your aims or goals without having to make use of an LLM, then go for it. You will save tons of money and resources if you just opt for a simple way out that gets the work done. - What is the accuracy to achieve?
One of the major factors when choosing an LLM is accuracy. Typically, model size and accuracy go hand in hand: the larger the model, the more parameters it has, resulting in increased accuracy. If your use case calls for a high degree of precision, then consider choosing the bigger LLM models, such as LLaMA or Falcon. These could provide the highly accurate degree that may be required by your use case. - How much would you like to invest?
Another critical factor is the cost. The larger a model is, the more resources it needs to be trained and run. This could mean additional infrastructure on your side or a larger bill from cloud service providers if you run your LLM in the cloud. While open-source LLMs are free to use, the cost of the resources used runs into money very fast. Be sure to budget accordingly for the same. - Can you achieve your goals with a pre-trained model?
Finally, ask yourself whether you can get by using a pre-trained model. The truth is that there exist enough pre-trained open-source LLMs, which are designed for different use cases. Compared with training an LLM from scratch, working with a pre-trained model uses a lot less of your own time and effort. If your project coincides with one of those existing models, then go ahead and use it.
Conclusion
Open-source LLMs become a part of an interesting movement. Fast development proves that the space of generative AI will not be set by big companies having the resources to develop and use these powerful tools. In contrast, open-source LLMs level the playing field. Advanced AI is reachable by more and more people and organizations.
While we Series: focused on just eight open-source LLMs, there are many more out there, with the list growing fast. It is in DataCamp’s commitment to keep you updated on what is transpiring in the LLM space. The team will still be there, renditioning relevant information in courses, articles, and tutorials that will help you successfully navigate and get the most out of these fast-changing technologies. Until time allows for more thoughts and learning!
FAQs
1. What are LLMs, and why are they important?
Large language models are AI systems based on transformers and process human language. They are at the center of the generative AI revolution that powers advanced chatbots like ChatGPT and Google Bard. These LLMs come with hundreds of millions or even billions of parameters, which make them great gear for various natural language processing tasks.
2. Why use open-source LLMs when a proprietary version is available? Open-source LLMs provide enhanced data security and privacy, cost savings from licensing, lesser vendor dependence, transparent code, and customized models. Not to mention that they provide innovation due to active community support and come out clear on resource usage, hence being more environmentally friendly.
3. How do open-source LLMs improve upon data security and privacy?
With open-source LLMs, the company has full control over its information, thereby avoiding any leaks or other risks that the data might be exposed to by external providers. This enables better protection and privacy of sensitive information.
4. What are the cost implications associated with using open-source LLMs?
Generally, open-source LLMs are free to use, but running them requires many resources, like cloud services or powerful infrastructure. This essentially translates to the fact that while you might be saving on licensing fees from their use, you still have to put resources into the technology necessary for them to work.
5. How is the user benefitting from the openness of open-source LLMs?
By open-sourcing their source code, architecture, and training data, open-source LLMs enable users to have a better understanding of how to customize models. Transparency can make better performance in specific use cases or larger flexibility achievable compared to a proprietary system.
6. What role does community support play during the development process of open-source LLMs?
The open-source movement democratizes LLM and generative AI technologies by providing access to the worldwide developer community, who can work on and finetune these models. This collaborative approach drives innovation, reduces bias, and enhances the accuracy and performance of such models.
7. How do open-source LLMs address environmental concerns?
Open-source LLMs are much more transparent about their resources—which in turn makes it much easier for researchers to come up with improvements that bring down their environmental footprint. This again enhances transparency toward more sustainable and more responsible development in AI.
8. What should I look out for to choose an open-source LLM correct for my use case?
The main factors to be considered are intended use, the need for an LLM for your project, the accuracy required, resource expense, and whether a pre-trained model could cover your need. These factors will help you in choosing the right open-source LLM to use with respect to your application.
