How to Create Engaging AI Apps with Meta’s Llama 2 and Databricks for Optimal Search Engine Optimization and User Appeal

How to Create Engaging AI Apps with Meta’s Llama 2 and Databricks for Optimal Search Engine Optimization and User Appeal

Introduction:

Meta’s release of their latest large language model (LLM), Llama 2, to open source for commercial use is a groundbreaking development in the field of AI. As a launch partner, we had the opportunity to test the Llama 2 models in advance and were thoroughly impressed with their capabilities and potential applications. This release follows the success of Meta’s earlier release, LLaMA, which advanced the frontier of Open Source LLMs. Other open source efforts, such as MPT-7B and Falcon-7B, have also contributed to the availability of high-quality LLM models for commercial use. Llama 2 models, ranging from 7B to 70B, not only accelerate LLM research but also empower enterprises to build their own generative AI applications. By leveraging Llama 2 and other open source models, enterprises can maintain complete ownership and control over their generative AI applications, avoiding vendor lock-in and ensuring correctness, bias, and performance. At Databricks, we see an increasing number of customers embracing open source LLMs for various generative AI use cases, comparing them to API-based models in terms of quality, cost, reliability, and security. We offer example notebooks to facilitate development with Llama 2 on Databricks, providing guidance on inference, fine-tuning, and model logging. In addition, our Model Serving offering supports the deployment of fine-tuned LLaMA models on GPUs, ensuring optimal latency and throughput for commercial applications. We also plan to add support for Llama 2 in our optimized LLM Serving offerings, enabling enterprises to achieve best-in-class performance. Sign up for preview access to our GPU-powered Model Serving today!

Full Article: How to Create Engaging AI Apps with Meta’s Llama 2 and Databricks for Optimal Search Engine Optimization and User Appeal

Meta Releases Llama 2 Large Language Model to Open Source for Commercial Use

Meta, the social media giant, has recently made a significant move in the field of open-source artificial intelligence (AI). They have released their latest large language model (LLM) called Llama 2 to open source for commercial use. This development is highly anticipated and has been met with excitement from industry experts. As a launch partner, we had the privilege to test the Llama 2 models in advance and were profoundly impressed with their capabilities and potential applications.

Advancing the Frontier of Open Source LLMs

Meta’s previous release, LLaMA, was a groundbreaking step forward for open-source LLMs. Although the v1 models were not available for commercial use, they significantly accelerated research in generative AI and LLMs. The Alpaca and Vicuna models showcased that, with high-quality instruction-following and chat data, LLaMA models could be fine-tuned to behave like ChatGPT. Building on this research, Databricks created and released the databricks-dolly-15k instruction-following dataset for commercial use. Additionally, LLaMA-Adapter and QLoRA introduced parameter-efficient fine-tuning methods, enabling cost-effective fine-tuning of LLaMA models on consumer GPUs. Moreover, Llama.cpp successfully ported LLaMA models to run efficiently on a MacBook with 4-bit integer quantization.

Growing Number of Open Source Models for Commercial Use

In parallel with Meta’s efforts, several open source projects have emerged aiming to produce models equal to or better than LLaMA for commercial use. MosaicML’s MPT-7B became the first open-source LLM with a permissive license comparable to LLaMA-7B, offering additional features like ALiBi for longer context lengths. Following MPT-7B, numerous models have been released with permissive licenses, including Falcon-7B and 40B, OpenLLaMA-3B, 7B, and 13B, and MPT-30B. These models have expanded the possibilities for enterprises looking to leverage LLMs in their applications.

The Power of Llama 2 for Generative AI Applications

The newly released Llama 2 models will further accelerate LLM research and enable enterprises to develop their own generative AI applications. Llama 2 includes 7B, 13B, and 70B models, all trained on a larger number of tokens compared to LLaMA. Moreover, Llama 2 provides fine-tuned variants for instruction-following and chat purposes, enhancing its versatility in various contexts.

Complete Ownership of Generative AI Applications

Llama 2 and other state-of-the-art open-source models like MPT offer enterprises a unique opportunity to fully own their generative AI models and applications. Using open-source models can bring several advantages compared to proprietary software-as-a-service (SaaS) models:

1. No Vendor Lock-In or Forced Deprecation Schedule
2. Ability to Fine-Tune Models with Enterprise Data and Retain Full Access to Trained Models
3. Consistent Model Behavior over Time
4. Ability to Serve Private Model Instances within Trusted Infrastructure
5. Tight Control over Correctness, Bias, and Performance of Generative AI Applications

Embracing Open Source LLMs at Databricks

At Databricks, we have witnessed numerous customers embracing open-source LLMs for various generative AI use cases. As the quality of these models continues to improve rapidly, more customers are experimenting with them to assess their quality, cost-effectiveness, reliability, and security compared to API-based models.

Developing with Llama 2 on Databricks

Developers can now easily access Llama 2 models on Databricks. We offer example notebooks that demonstrate how to use Llama 2 for inference, wrap it with a Gradio app, efficiently fine-tune it with your data, and log models into MLflow.

Serving Llama 2

To deploy fine-tuned and optimized Llama 2 models across an organization or integrate them into AI-powered applications, Databricks’ Model Serving offering is available. This service supports serving LLMs on GPUs, ensuring the best possible latency and throughput for commercial applications. To deploy a fine-tuned LLaMA model, simply create a Serving Endpoint and include your MLflow model from the Unity Catalog or Model Registry in the endpoint’s configuration. With Databricks’ assistance, you will have a production-ready environment for your model, ready to scale with your traffic.

Preview Access to GPU-powered Model Serving

Databricks also offers optimized LLM serving for enterprises with demanding latency and throughput requirements. They will soon add support for Llama 2 to provide best-in-class performance to those who choose this model.

Llama 2 License and Restrictions

While Llama 2 is now available as open source, certain restrictions may apply. For detailed information, please refer to the Llama 2 license.

With Meta’s release of Llama 2 and the growing ecosystem of open-source LLMs, enterprises now have the opportunity to own their generative AI models fully. By leveraging these models, businesses can benefit from increased control, flexibility, and innovation in their AI applications.

Summary: How to Create Engaging AI Apps with Meta’s Llama 2 and Databricks for Optimal Search Engine Optimization and User Appeal

Meta has released their latest open-source large language model (LLM), Llama 2, which is available for commercial use. This development is significant for open-source AI, and Databricks has been working with Meta as a launch partner and has been impressed with Llama 2’s capabilities and potential applications. Llama 2 includes 7B, 13B, and 70B models, trained on more tokens than the previous LLaMA models. This release not only accelerates LLM research but also enables enterprises to build their own generative AI applications. By using open-source LLMs like Llama 2, businesses can have complete ownership of their AI models and applications without vendor lock-in or forced deprecation schedules.

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