It's been an exciting journey ever since we embarked on the Llama project. Llama 1 was a breakthrough, Llama 2 added more spice, and with the release of Code Llama, the momentum has been nothing short of astonishing.
A Recap of Llama's Journey
Within just a span of seven months since the introduction of Llama 1 and the subsequent unveiling of Llama 2 and Code Llama, the community's response has been overwhelming. To put it into perspective:
Llama-based models have been downloaded over 30 million times through Hugging Face.
A staggering 10 million of these downloads occurred in the last 30 days.
Drawing parallels with PyTorch, Llama is quickly evolving as a robust platform for global AI innovation.
The Llama Community's Exponential Growth
To say Llama has impacted the AI landscape would be an understatement. The growth has been characterized by:
Cloud Adoption: Giants like AWS, Google Cloud, and Microsoft Azure are hosting Llama models. Particularly, AWS's recent collaboration as the managed API partner for Llama 2 has been a game-changer in terms of accessibility.
Innovators' Choice: Startups and innovators like Anyscale, Replicate, and DoorDash are rooting for Llama as their foundational AI tool.
Open-Source Embrace: With over 7,000 derivatives on Hugging Face, the open-source community has enhanced model performance exponentially.
Booming Developer Community: Over 7,000 Llama-related projects are currently hosted on GitHub. From new tools to 'tiny' Llama versions for mobile platforms, the creativity knows no bounds.
Hardware Integration: Top-tier hardware platforms are optimizing for Llama, further enhancing its performance.
The release of Code Llama only solidified its presence, with rapid integration on many platforms, marking a pivotal moment for AI enthusiasts.
From Research to Global Phenomenon
Llama's origin was rooted in the power of large language models (LLMs). Initially developed by a team at FAIR, it sought to harness the prowess of LLMs for various innovative applications. The results? Groundbreaking improvements and diversifications by academic researchers and the wider community.
But Llama 1 was just the beginning. The need for broader accessibility brought Llama 2 to the forefront.
Our Philosophy Behind Releasing Llama Models
At Meta, we firmly believe in open source. The logic is simple:
Research: Harnessing collective wisdom to enhance AI capabilities.
Enterprise and Commercialization: Learning through startups and enterprises to uncover AI's vast potential.
Developer Ecosystem: Utilizing new tools and strategies emerging daily in the AI domain.
Meta has always been at the forefront of advocating for an open approach, and Llama is no exception.
With the AI realm advancing rapidly, here are our core focal points:
Multimodal Experiences: Beyond just text, AI can integrate various modes for richer experiences.
Safety and Responsibility: With AI's potential comes the imperative need for responsible development and application.
Community Emphasis: Like PyTorch, we visualize a developer community with a voice and agency, driving the future of AI innovation.
At AILab, we consistently utilize Llama2 for our daily operations. A significant portion of our projects are predicated on various Llama2 models. We would like to extend our gratitude to Meta for this invaluable opportunity.