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Early Days:
LLMs have been around for decades, but their capabilities were limited due to computational constraints and smaller datasets. Early models focused on statistical language processing techniques.
Their capabilities and popularity have surged in recent years
Around 2017: Advancements in deep learning architectures, particularly transformers, and the availability of massive datasets like Google Books and Common Crawl fueled significant progress in LLMs.
By 2018: OpenAI's Generative Pre-trained Transformer (GPT-2) demonstrated impressive capabilities in text generation, attracting widespread attention & is often considered a landmark due to its public release and capabilities.
GPT-2 was not fully open-source. OpenAI opted for a controlled release due to concerns about potential misuse. This sparked the debate about open vs. closed-source approaches in LLM development.
Universities have a long history of sharing research and code, fostering open collaboration. This philosophy naturally extended to the fields of AI and LLMs. The success of open-source software movements like Linux demonstrated the power of collaboration and community-driven development. This inspired researchers and developers to explore open-source approaches for LLMs.
Numerous research groups and independent developers are actively contributing to the open-source LLM landscape. This collaborative effort is constantly expanding the range of available models (OpenAI GPT-J, Meta AI Llama, EleutherAI Jurassic-1 Jumbo, Hugging Face Transformers,) and fostering innovation. A growing community of independent developers and companies are actively contributing to improve the open-source LLM landscape.
It's constantly evolving, with new models being developed and released frequently. The Hugging Face Transformers Library alone offers access to over 100 pre-trained models, and there are numerous independent projects launching new open-source LLMs all the time.
Companies like Google, OpenAI, Microsoft, Amazon, and Baidu are at the forefront of closed-source LLM development. These models are often shrouded in secrecy regarding their code and training data.
Closed-source models are probably more numerous. Big companies with vast resources often prioritize closed-source development for commercial gain and control over intellectual property.
Understanding these differences is crucial for users to choose the right LLM for their needs.
Decision points | Open-Source LLM | Close-Source LLM |
---|---|---|
Accessibility | The code behind the LLM is freely available for anyone to inspect, modify, and use. This fosters collaboration and innovation. | The underlying code is proprietary and not accessible to the public. Users rely on the terms and conditions set by the developer. |
Customization | LLMs can be customized and adapted for specific tasks or applications. Developers can fine-tune the models and experiment with new techniques. | Customization options are typically limited. Users might have some options to adjust parameters, but are restricted to the functionalities provided by the developer. |
Community & Development | Benefit from a thriving community of developers and researchers who contribute to improvements, bug fixes, and feature enhancements. | Development is controlled by the owning company, with limited external contributions. |
Support | Support may come from the community, but users may need to rely on in-house expertise for troubleshooting and maintenance. | Typically comes with dedicated support from the developer, offering professional assistance and guidance. |
Cost | Generally free to use, with minimal costs for running the model on your own infrastructure, & may require investment in technical expertise for customization and maintenance. | May involve licensing fees, pay-per-use models or require cloud-based access with associated costs. |
Transparency & Bias | Greater transparency as the training data and methods are open to scrutiny, potentially reducing bias. | Limited transparency makes it harder to identify and address potential biases within the model. |
IP | Code and potentially training data are publicly accessible, can be used as a foundation for building new models. | Code and training data are considered trade secrets, no external contributions |
Security | Training data might be accessible, raising privacy concerns if it contains sensitive information & Security relies on the community | The codebase is not publicly accessible, control over the training data and stricter privacy measures & Security depends on the vendor's commitment |
Scalability | Users might need to invest in their own infrastructure to train and run very large models & require leveraging community experts resources | Companies often have access to significant resources for training and scaling their models and can be offered as cloud-based services |
Deployment & Integration Complexity | Offers greater flexibility for customization and integration into specific workflows but often requires more technical knowledge | Typically designed for ease of deployment and integration with minimal technical setup. Customization options might be limited to functionalities offered by the vendor. |
The LLM landscape is likely to see a blend of both approaches. Closed-source models will continue to push the boundaries of performance, while open-source models will democratize access and foster innovation. Collaboration between these two sides could lead to even more powerful and responsible AI advancements.
Keep pace with the dynamic advancements in LLM landscape by engaging with Fluid AI. Reach out to us to adopt & deploy the latest LLM model for your organisation with utmost security, privacy & added capabilities to make it Enterprise-ready, user-friendly & support throughout. We work with every LLM model avaliable & allow organisations to also easily switch to any new model launched swiftly to be ahead of the technological curve. We help organisation to deploy the advance model according to your usecases & organisational need. Fluid AI offers the flexibility of Private Deployment option or explore the flexibility of public along with hybrid hosting options.
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