The Technology Innovation Institute (TII) has broken new ground by launching Falcon LLM, a robust large language model (LLM) geared towards both research and commercial application. TII has now publicly released Falcon LLM—a model showcasing 40 billion parameters, and trained using one trillion tokens—for unrestricted use.
In a paradigm shift towards democratizing AI technology, TII has issued an open call for project submissions from the international research sphere and SME entrepreneurs. These projects are invited to demonstrate a range of innovative applications for the Falcon LLM.
Falcon LLM sets itself apart by utilizing custom tooling and a bespoke data pipeline, capable of drawing high-grade content from the web for training an autonomous codebase. This leap in technology has allowed Falcon to distance itself from dependencies on tech giants like NVIDIA, Microsoft, or HuggingFace.
In the race for data quality at scale, Falcon stands out. It is well-known that LLMs’ performance is heavily tied to their training data’s quality. The Falcon LLM has been fine-tuned by employing a data pipeline that successfully scales to tens of thousands of CPU cores for rapid processing, while simultaneously ensuring the extraction of superior content from the web via comprehensive filtering and deduplication.
Falcon LLM’s architectural design is a masterstroke in the quest for performance and efficiency. By merging high-quality data with these optimizations, Falcon has achieved a milestone in outclassing GPT-3 while only utilizing 75% of its training compute resources. At the same time, it requires only a fifth of the computation during inference.
Stacked up against other state-of-the-art LLMs from tech leaders such as DeepMind, Google, and Anthropic, Falcon matches stride. This 40 billion parameter autoregressive, decoder-only model was trained over two months on 384 GPUs on AWS. Its pretraining data was sourced from public web crawls, compiling a pretraining dataset of nearly five trillion tokens following extensive filtering and deduplication.
To diversify Falcon’s abilities, the dataset was supplemented with meticulously selected sources, including academic research papers and social media conversations. The performance of Falcon LLM was subsequently verified against well-established open-source benchmarks such as EAI Harness, HELM, and BigBench.
With potential applications across a multitude of fields including chatbots, customer service operations, virtual assistants, language translation, content generation, and sentiment analysis, Falcon LLM is poised to bring about a seismic shift. The team at TII is particularly excited about Falcon’s capacity to automate repetitive work, ultimately increasing productivity for Emirati firms and startups.
On an individual level, chatbots powered by Falcon are anticipated to become crucial personal assistants for users in their day-to-day life.
In an effort to foster collaboration and spur innovation, TII has decided to open-source the model’s weights, making Falcon LLM more accessible to developers and researchers around the world. By doing so under the Apache License Version 2.0, TII is promoting transparency, enabling users to assess and verify the code’s security and reliability.
This move is expected to boost the progress and research in LLMs in a secure and transparent manner, paving the way for more benevolent AI applications. TII’s call for Falcon LLM use cases offers promising proposals not only “Training Compute Power” investment, but also additional opportunities for commercialization.
As part of this initiative, users should keep an eye out for the forthcoming “Falcon Chatbot,” marking yet another step forward in TII’s ongoing quest to push the boundaries of AI technology.
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