DEEPCHECKS GLOSSARY

Open Source LLM

What are Open Source LLMs?

Large language models (LLMS) are foundation models that unleash the power of artificial intelligence through deep learning and massive datasets to understand, generate, and interact with human language like never before. Broadly speaking, there are two types of LLMs: proprietary and open-source.

Proprietary LLMs are generally owned by a company, and only its customers are allowed to use them by purchasing a license. The license can also dictate and restrict how the LLM can be used. Open-source LLMs, on the other hand, are free and openly available for anyone to access, use, modify, and distribute. This also ensures that the underlying architecture and the LLM code are accessible, making it easier for developers and researchers to understand and build upon the model accordingly.

Open Source LLM

Benefits of Open Source LLMs

Flexibility and Transparency

Open-source LLMs provide the transparency and flexibility required for organizations that don’t have the native in-house machine-learning talent to build powerful LLM-based applications. Developers are given full transparency into the LLM’s architecture, can inspect the code, and have full visibility into the algorithms. This helps build trust and ensures data security and privacy.

Cost savings

Long-term cost savings are a significant advantage of open-source LLMs, as there are no licensing fees involved compared to proprietary LLMs. However, maintaining the infrastructure required to operate the LLM is costly, along with a high initial rollout cost.

Feature-rich and community-support

Open-source LLMs benefit from massive community support, enabling anyone to contribute, from hobbyists to developers and researchers from a wide variety of industries. This enables companies to experiment and utilize contributions from people with varying perspectives, which allows them to stay ahead of the curve and up-to-date with the latest trends in LLMs.

Furthermore, the flexibility and fine-tuning options provided by open-source LLMs allow companies to further enhance these models according to their business-specific use cases and datasets. They could also build an internal team to handle development, maintenance, updates, and support, but handling such changes and customizations on proprietary LLMs would introduce much overhead as working with the vendor will cost time and money.

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Challenges and Considerations

Resource intensive

Training and fine-tuning open-source LLMs require substantial computational resources and expertise. Companies without in-house talent and access to high-performance computing infrastructure may face challenges in obtaining the full potential of these models. However, the availability of pre-trained models can mitigate some of these barriers, allowing for more accessible fine-tuning and deployment.

Intellectual property

Using open-source models can complicate intellectual property (IP) issues, particularly with publicly shared modifications, but they enable companies to innovate by building proprietary solutions on open technologies, offering a competitive advantage. In contrast, proprietary models have clearly defined IP rights, usually without company ownership of the underlying technology, yet they provide a competitive edge through access to exclusive, cutting-edge technologies.

Ethical concerns

Ethical concerns can arise as the availability of open-source models also enables cyber-criminals to use such LLMs for malicious tasks such as spamming and phishing and the risk of leaking personally identifiable information (PII).

Bias and Fairness

Open-source LLMs are often trained using large volumes of data scraped from the Internet. Such data isn’t often representative and can contain biases regarding race, gender, and ethnicity. The LLMs often reflect this bias and can affect downstream tasks to portray unintentional bias, causing harm. Continuous monitoring and intervention strategies might be required to tackle this problem.

Bias and Fairness

Notable Open Source LLM Projects

  • Meta’s LLaMA2: LLaMa 2 by Meta AI stands out as a high-performing open-source LLM with a license that permits commercial use. This model includes pre-trained and fine-tuned generative text versions, boasting 7 to 70 billion parameters. You can access LLaMa 2 in the Watsonx.ai studio as well as through the HuggingFace ecosystem and transformer library.
  • Bloom by BigScience: Bloom is a groundbreaking multilingual language model crafted by over 1,000 AI researchers. It holds the distinction of being the first multilingual LLM trained with full transparency.
  • StableLM from Stability AI: StableLM, an open-source LLM from Stability AI, the creators of the AI image generator Stable Diffusion, has been trained on an extensive dataset of 1.5 trillion tokens known as “The Pile.” This model is fine-tuned using a diverse combination of open-source datasets, including those from Alpaca, GPT4All (which features models based on GPT-J, MPT, and LLaMa), Dolly, ShareGPT, and HH.
  • Falcon LLM from Technology Innovation Institute (TII): The Falcon LLM is designed to enhance chatbots by generating creative text, solving complex problems, and automating repetitive tasks. Available in two versions, Falcon 6B and Falcon 40B, these models can be used either in their raw form for fine-tuning or as pre-tuned models ready for immediate deployment. Remarkably, Falcon operates with only about 75% of GPT-3’s training compute budget while significantly outperforming it.

Future Directions

The future of open-source LLMs is marked by continued growth and innovation. As computational power becomes more accessible and more efficient algorithms are developed, the potential for even more powerful and versatile models will expand. Collaboration across academia, industry, and open-source communities will remain a driving force in pushing the boundaries of what these models can achieve.