A new titan large language model (LLM) has been made available this week, setting a groundbreaking standard for open models. The Falcon 180 Billion (Falcon 180B), developed by TII, has been released on HuggingFace, marking a significant milestone in the field of artificial intelligence. With an impressive 180 billion parameters, Falcon 180B stands as the largest openly available language model to date, surpassing its predecessors and competitors in scale and complexity.
“Falcon 180B is the best openly released LLM today, outperforming Llama 2 70B and OpenAI’s GPT-3.5 on MMLU, and is on par with Google’s PaLM 2-Large on HellaSwag, LAMBADA, WebQuestions, Winogrande, PIQA, ARC, BoolQ, CB, COPA, RTE, WiC, WSC, ReCoRD. Falcon 180B typically sits somewhere between GPT 3.5 and GPT4 depending on the evaluation benchmark and further finetuning from the community will be very interesting to follow now that it’s openly released.”
The Falcon 180B’s training process was a monumental task, involving a staggering 3.5 trillion tokens. This was accomplished using TII’s RefinedWeb dataset, marking the longest single-epoch pretraining for an open model. The model’s training was conducted on up to 4096 GPUs simultaneously, utilizing Amazon SageMaker for a total of approximately 7,000,000 GPU hours. This extensive training process underscores the immense computational power and resources required to develop such a sophisticated model.
Falcon-40B LLM
The dataset for Falcon 180B predominantly consists of web data from RefinedWeb, accounting for approximately 85% of the total data. The remaining data is a mix of curated content such as conversations, technical papers, and a small fraction of code, accounting for about 3% of the total data. This diverse dataset has equipped Falcon 180B with a broad knowledge base, enabling it to handle a wide range of natural language tasks.
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RefinedWeb
“RefinedWeb is built through stringent filtering and large-scale deduplication of CommonCrawl; we found models trained on RefinedWeb to achieve performance in-line or better than models trained on curated datasets, while only relying on web data.”
Falcon 180B is not just a scaled-up version of its predecessor, Falcon 40B. It builds on the innovations of Falcon 40B, such as multiquery attention, which has significantly improved the model’s scalability. The released chat model has been fine-tuned on chat and instruction datasets, incorporating a mix of several large-scale conversational datasets. This fine-tuning has further enhanced the model’s performance in conversational tasks.
The model’s performance is nothing short of remarkable. Falcon 180B achieves state-of-the-art results across natural language tasks, topping the leaderboard for open-access models. It even rivals proprietary models like PaLM-2, demonstrating its prowess in the field. In fact, Falcon 180B outperforms Llama 2 70B and OpenAI’s GPT-3.5 on MMLU, and is on par with Google’s PaLM 2-Large on various benchmarks. These achievements highlight the model’s exceptional capabilities and its potential to revolutionize the field of natural language processing.
Falcon-40B demonstration space
Falcon 180B can be found on the Hugging Face Hub and can be interacted with on the Falcon Chat Demo Space. It is available in the Hugging Face ecosystem, starting with Transformers version 4.33. While Falcon 180B can be commercially used, it is subject to very restrictive conditions, excluding any “hosting use”. This ensures that the model’s use aligns with ethical guidelines and prevents misuse.
Falcon 180B represents a significant leap forward in the field of language models. Its impressive scale, extensive training, and state-of-the-art performance set a new standard for open models. As the largest openly available language model, Falcon 180B has the potential to significantly impact various fields, from natural language processing to artificial intelligence research.
However, its use is subject to strict conditions to ensure ethical and responsible application. Despite these restrictions, Falcon 180B’s release marks a significant milestone in the field, paving the way for future advancements in language models.
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