Exploring the Falcon LLM: The New King of The Jungle

4 min readJul 7, 2023

The Falcon LLM, developed by the Technology Innovation Institute based in Abu Dhabi, is a large language model that has redefined the capabilities of AI language processing. This model is a part of the Falcon family, which includes two primary variations: Falcon-40B and Falcon-7B. Each model brings its unique strengths to the table, making the Falcon LLM an innovative and versatile tool for various applications.

Understanding the Falcon LLM

At its core, the Falcon LLM is a language model with impressive performance and scalability. Its training was conducted on a colossal web dataset known as RefinedWeb, supplemented with some curated sources. The model’s unique feature of multi-query attention enhances scalability, allowing the model to handle large-scale tasks efficiently.

What sets Falcon LLM apart from other large language models is its open-source nature. Released under the Apache 2.0 license, Falcon LLM is freely available for commercial and research usage. This inclusive approach to AI allows researchers, developers, and businesses to harness the power of Falcon LLM without the constraints of closed-source models.

The Falcon LLM’s distinctiveness doesn’t end there. It was crafted using custom tooling and a unique data pipeline, ensuring the quality of the training data. The model also features modifications like rotary positional embeddings and multiquery attention, which enhance its performance. Training was conducted on the AWS SageMaker platform using a custom distributed training codebase, employing 384 A100 40GB GPUs.

Falcon LLM has surpassed LLaMA as the top-ranked model on the Open LLM Leaderboard. The Open LLM Leaderboard is a joint community effort to create a central leaderboard for LLMs (Language Model Models). It includes a list of models and their performance on various benchmarks. Falcon LLM’s ranking on this leaderboard is a testament to its superior performance and efficiency.

Unveiling the Unique Features of Falcon LLM

Falcon LLM’s performance is bolstered by its unique features, the first of which is its use of multi-query attention. This variant of the Transformer neural sequence model reduces memory bandwidth requirements during incremental decoding, allowing for quicker decoding with minimal quality degradation.

Another innovative feature is flash attention, a new attention algorithm that is both fast and memory-efficient for Transformers. FlashAttention minimizes the number of memory reads/writes between GPU high bandwidth memory (HBM) and GPU on-chip SRAM by using tiling. This results in faster training of Transformers and enables longer context, leading to higher quality models and improved performance across various tasks.

The Training Process of Falcon LLM

Training the Falcon LLM required a combination of substantial computational resources and high-quality data. The model was trained on the Falcon RefinedWeb, a massive dataset extracted from CommonCrawl and curated by the Technology Innovation Institute.

The training process utilized bfloat16 precision and the AdamW optimizer to ensure efficient and effective training. The flagship model, Falcon-40B, required 90GB of GPU memory, illustrating the significant computational power required for training such a model. Amazon SageMaker, a cloud machine learning platform, was used for both data preprocessing and model training, providing a scalable and efficient environment for training this large language model.

The Falcon LLM Family: Falcon-40B and Falcon-7B

The Falcon LLM family consists of two main variations: Falcon-40B and Falcon-7B. The Falcon-40B, the flagship model, boasts 40 billion parameters and was trained on one trillion tokens. It outperforms other open-source models, offering an optimized architecture for inference.

On the other hand, Falcon-7B is a more efficient model that requires less GPU memory. Despite its smaller size, Falcon-7B still delivers impressive performance, thanks to the use of multi-query attention.

Both models were trained on the RefinedWeb dataset, which underwent extensive filtering and deduplication processes to ensure high-quality training data. The models can be easily accessed and fine-tuned using the Hugging Face ecosystem. Tools like Text Generation Inference and PEFT (Progressive Early Fertilization Training) are available for running and fine-tuning the models.

The Potential of Falcon LLM

The Falcon LLM models offer a wide range of applications, from powering chatbots and customer service operations to serving as virtual assistants and facilitating language translation. They can also be used for content generation and sentiment analysis.

The open-sourcing of these models aims to foster collaboration and innovation in the AI community. The Technology Innovation Institute is even inviting the research community and SME entrepreneurs to submit use cases for Falcon LLM, offering both investment in training compute power and commercialization opportunities for exceptional proposals.

Summary

Falcon LLM is a powerful and flexible large language model that offers exciting possibilities for commercial applications. Its open-source nature and superior performance make it a valuable tool for researchers, developers, and businesses alike. With Falcon LLM, the future of AI language processing looks promising.

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