Open source creates opportunities for developers worldwide to work together on projects, share knowledge and collectively enhance software solutions. This inclusive approach not speeds up advancements but also ensures that cutting edge tools and technologies are available to everyone. So it always warms my heart when I see any innovations in this space.

Open source software drives innovation by reducing development costs and ensuring transparency and security. To me it embodies the essence of intelligence, by bringing developers together to learn from each other and shape the future of technology as a united community.

The artificial intelligence community has reached a significant milestone with the introduction of Falcon 180B, an open-source large language model (LLM) that boasts an astonishing 180 billion parameters, trained on an unprecedented volume of data. This groundbreaking release, announced by the Hugging Face AI community in a recent blog post, has already profoundly impacted the field. Falcon 180B builds upon the success of its predecessors in the Falcon series, introducing innovations such as multi-query attention to achieve its impressive scale, trained on a staggering 3.5 trillion tokens, representing the longest single-epoch pretraining for any open-source model to date.

Scaling Unleashed

Achieving this goal was no small endeavor. Falcon 180B required the coordinated power of 4,096 GPUs working simultaneously for approximately 7 million GPU hours, with the training and refinement process orchestrated through Amazon SageMaker. Considering this regarding the size of the LLM, the model’s parameters measure 2.5 times larger than Meta’s LLaMA 2, which had previously been considered the most capable open-source LLM with 70 billion parameters trained on 2 trillion tokens. The numbers and data involved are staggering, its like an analyst dream.

Performance Breakthrough

Falcon 180B isn’t just about scale; it excels in benchmark performance across various natural language processing (NLP) tasks. On the leaderboard for open-access models, it impressively scores 68.74 points, coming close to commercial giants like Google’s PaLM-2 on the HellaSwag benchmark. It matches or exceeds PaLM-2 Medium on commonly used benchmarks like HellaSwag, LAMBADA, WebQuestions, Winogrande, and more and performs on par with Google’s PaLM-2 Large. This level of performance is a testament to the capabilities of open-source models, even when compared to industry giants.

Comparing with ChatGPT

When measured against ChatGPT, Falcon 180B sits comfortably between GPT 3.5 and GPT4, depending on the evaluation benchmark. While it may not surpass the capabilities of the paid “plus” version of ChatGPT, it certainly gives the free version a run. I am always happy to see this type of healthy competition in this space.

The Huggingface community is strong so there is potential for further fine-tuning by the community, which is expected to yield even more impressive results. Falcon 180 B’s open release marks a significant step forward in the rapid evolution of large language models, showcasing advanced natural language processing capabilities right from the outset.

A New Chapter in Efficiency

Beyond its sheer scale, Falcon 180B embodies the progress in training large AI models more efficiently. Techniques such as LoRAs, weight randomization, and Nvidia’s Perfusion have played pivotal roles in achieving this efficiency, heralding a new era in AI model development.

With Falcon 180B now freely available on Hugging Face, the AI research community eagerly anticipates further enhancements and refinements. This release marks a huge advancement for open-source AI, setting the stage for exciting developments and breakthroughs. Falcon 180B has already demonstrated its potential to redefine the boundaries of what’s possible in the world of artificial intelligence, and its journey is just beginning. It’s the numbers for me. I am always happy to see this growth in this space. Yes, “the bird” was always about technology. Shared references give you a great headstart in understanding all about Falcon.

References:

huggingface on GitHub

huggingface Falcon documentation

Falcon Models from Technlogy Innovation Institute