The Technological Innovation Institute (TII) in Abu Dhabi has introduced Falcon, a family of cutting-edge language models available under the Apache 2.0 license. Falcon-40B is the inaugural “truly open” model, boasting capabilities on par with many proprietary alternatives. This development marks a significant advancement and offers many opportunities for professionals, enthusiasts and industries alike.
Falcon2-11B, developed by the TII, it is a causal decoder model that has 11 billion parameters. It has been meticulously trained on a vast corpus exceeding 5 billion tokens, fusing data from RefinedWeb with meticulously curated corpora. This model is accessible under the TII Falcon 2.0 license, a permissive software license inspired by Apache 2.0. In particular, the license includes an acceptable use policy, which encourages the responsible use of ai technologies.
Falcon2-11B, a causal-only decoder model, is trained to predict the next token in a causal language modeling task. It is based on the GPT-3 architecture, but incorporates rotating positional embeddings, multi-query attention, FlashAttention-2, and parallel attention/MLP decoder blocks, distinguishing it from the original GPT-3 model.
The Falcon family includes the Falcon-40B and Falcon-7B models, with the former standing out on the Open LLM leaderboard. Falcon-40B requires ~90 GB of GPU memory, even less than LLaMA-65B. Falcon-7B requires only ~15 GB, allowing for accessible inference and tuning even on commodity hardware. TII offers instruction variants optimized for wizard-type tasks. Both models are trained on vast token datasets, predominantly from RefinedWeb, with extracts publicly available. They employ multi-query attention, which improves the scalability of inference by reducing memory overhead. This design facilitates robust optimizations such as state, making Falcon models formidable competitors in the language model landscape.
The research advocates the use of large language models as a basis for specialized tasks such as summarization and chatbots. However, caution is advised against irresponsible or harmful use without thorough risk assessment. Falcon2-11B, trained in multiple languages, may not generalize much beyond them and may contain biases from web data. Recommendations include adjustments for specific tasks and implementation of safeguards for responsible production use.
In summary, the technology Innovation Institute's introduction of Falcon presents a groundbreaking advance in the field of language modeling. Falcon-40B and Falcon-7B offer notable capabilities, with Falcon-40B leading the Open LLM leaderboard. Falcon2-11B, with its innovative architecture and extensive training, further enriches the Falcon family. Although these models have immense potential for various applications, their responsible use is essential. Vigilance against bias and risk, along with thoughtful tailoring for specific tasks, ensures its ethical and effective implementation across industries. Falcon models therefore represent a promising frontier in ai innovation, poised to responsibly reshape numerous domains.
Asjad is an internal consultant at Marktechpost. He is pursuing B.tech in Mechanical Engineering at Indian Institute of technology, Kharagpur. Asjad is a machine learning and deep learning enthusiast who is always researching applications of machine learning in healthcare.
