Bring Your LLM Workflow In-House With Databricks and Llama-2!
Two AI titans team up to supercharge your LLM workflow.
Created on July 19|Last edited on July 19
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Meta recently announced the open-source availability of its advanced large language model (LLM), Llama 2, for commercial use. This is a significant move as it contributes to the expansion and acceleration of AI research and possible applications.
Poised for Commercial Use
Llama 2, an upgrade from the earlier LLaMA, comes with enhanced features such as improved instruction-following abilities, comparable to OpenAI's ChatGPT. The model's capabilities were preliminarily tested by Databricks, one of Meta's launch partners, who were impressed by its potential applications. In response to these capabilities, Databricks released the 'databricks-dolly-15k' instruction-following dataset for commercial use.
Llama 2 provides a chance for businesses to take full ownership of their generative AI applications. This is a significant advantage when compared to proprietary models like the OpenAI GPT API, offering an array of benefits that stem from the freedom and control that open-source models provide.
Open Source
Primarily, these benefits revolve around flexibility and autonomy. With Llama 2 and similar models, there is no issue of vendor lock-in or mandatory deprecation schedules. Enterprises are free to continue using the model as long as they find it beneficial, without being tied to the policies or lifecycles of a specific provider.
Fine Grained Control
Moreover, open source (OSS) models like Llama 2 allow businesses to fine-tune the models with their own data. Unlike traditional black-box AI models, OSS models provide enterprises complete visibility and control over the fine-tuning process, leading to models that are better aligned with specific business needs.
Predictable Performance
Additionally, OSS models are static in nature, meaning that their behavior does not change over time. In contrast, closed source models can update without warning, potentially impacting performance or compatibility. With Llama 2, businesses can ensure model stability and reliability, providing consistent results.
Trusted Deployment
Enterprises also have the ability to serve a private model instance within a trusted infrastructure. This feature of Llama 2 and similar OSS models offers an extra layer of data security and privacy. It's particularly relevant for organizations operating in sectors with stringent data handling regulations.
Full Alignment
Lastly, with Llama 2, enterprises have tight control over the correctness, bias, and performance of their generative AI applications. This control allows for the creation of more accurate, unbiased, and efficient AI applications. It also means that any potential issues can be identified and resolved more effectively.
The Shift
With these advantages, it's clear that the release of Llama 2 is more than just an upgrade. It represents a shift towards a more open, flexible, and controlled use of AI. As more organizations adopt these models, we could see a substantial evolution in the way generative AI applications are used and managed in a business context.
To facilitate the use of Llama 2, Databricks has made the models available on its platform, providing example notebooks to guide users on usage and fine-tuning processes. Databricks also supports model serving on GPUs to ensure optimal latency and throughput for commercial applications.
In summary, while working with large models like Llama 2 can be complex, tools and resources provided by Databricks aim to make these processes more accessible and manageable. They provide an integrated solution to train, fine-tune, and deploy these models, making it easier for businesses to own their AI applications fully.
The announcement: https://www.databricks.com/blog/building-your-generative-ai-apps-metas-llama-2-and-databricks?utm_source=bambu&utm_medium=social&utm_campaign=advocacy&blaid=4817798
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