Why 1-Bit Transformers Will Change the World

Welcome to the New World! Transformers, once the domain of companies and research organizations with 8-figure budgets, are undergoing the biggest disruptive change in their (short) history. Once upon a time, incredibly expensive clusters of A100s and V100s Graphical Processing Units (GPUs) that cost millions of dollars to buy, run, and maintain were necessary for state-of-the-art research work. However, things have changed. Welcome to the age of 1-bit transformers, that run on desktop CPUs and low-end GPUs without sacrificing performance or capabilities and inference capacities! The Scenario The 1-bit Transformer was first introduced by Kim et al. (2020) as a way to reduce the memory footprint and computational complexity of the original Transformer architecture. The key idea behind 1-bit Transformers is to quantize the weights and activations of the model to 1-bit values, i.e., -1 or 1. This quantization process not only reduces the memory requirements of the model but also enables the use of binary operations, which are significantly faster than floating-point operations. And that’s putting it mildly because binary operations like AND, NOT, and OR run on an extreme order of magnitude of 100,000 faster than FP-32 operations on GPUs when optimized. However, the exact speedup depends upon the optimization used and the context. The main advantage of 1-bit Transformers is their ability to achieve comparable performance to their full-precision counterparts while using significantly less memory and computational resources. The low memory requirements are a revolution in themselves. They can run on desktop GPUs without any expensive hardware requirements. Where 175 GB was required, now only 1.2 GB is required! This means that the transformer technology is now open to everyone, which is an incredible, unbelievable accomplishment. Industry Sectors 1-bit transformer language models (LLMs), of which BitNet 1.58b (see HuggingFace.co website) is a primitive precursor, can be applied to various industry sectors accessible to the average user without specialized hardware. Anywhere a standard LLM can be applied, a 1-bit transformer LLM can be applied. LLMs, as I pointed out in an article I wrote a long time back, are general-purpose system approximators. Wherever a human being exists in a job, that human being can be replaced by a specialized LLM, that is fine-tuned and trained as required for that role. Only this time, we won’t need to spend 100 million building it (Meta reportedly spent 100 million USD building Llama 3). These huge costs are now going to be a thing of the past. Every company worth anything substantial should have its best researchers working on low-footprint 1-bit quantized LLMs. Nothing will stop the revolution once the standard GPT model, for example, for 1-bit transformers is released. The discovery will lead to a revolution in society and societal economic structure. New jobs will be created and new multi-millionaires will be made. New mega-companies will arise - can you relate, Nvidia? This means that transformers will then be democratized, and open to every human being, regardless of economic status! And, the only bit of magic that is added will be 1-bit quantization and ternary weights {-1, 0, 1}! I am sure that, in the future, 1-bit LLMs will be able to handle audio, video, biometrics, bionics, and images! Which opens up an entire plethora of possibilities. But - everything Must be open-sourced. BitNet: A Scalable 1-Bit Transformer Architecture BitNet is a scalable and stable 1-bit Transformer architecture designed specifically for LLMs. It achieves all the scaling capabilities of Meta’s Llama 3 while being 1/70th the size. The implications are staggering! Talk about diamonds available for the price of plastic! The BitNet 1.58-bit Transformer is a novel Transformer architecture that incorporates advanced techniques such as multi-bit quantization, ternary weight normalization, and learnable scaling factors. These techniques enable the BitNet 1.58-bit Transformer to achieve high accuracy while maintaining the efficiency and memory-friendliness of binary neural networks (BNNs). One of the main advantages of the BitNet 1.58-bit Transformer is its ability to achieve comparable performance to its full-precision counterparts while using significantly less memory and computational resources. For instance, the BitNet 1.58-bit Transformer can achieve near state-of-the-art results on the WMT14 English-to-German machine translation task while using only 1/32 of the memory required by a full-precision Transformer model. 1.58-bit LLMs represent weights as ternary values: -1, 0, or +1, which allows for a more nuanced representation of parameters. These models are inherently more energy-efficient and ideal for edge computing applications. They dramatically reduce the dependency on specialized hardware like GPUs, potentially reducing the need for sophisticated hardware. The key difference: operations on these values run in unit time. Compare that to the average time complexity of an FP-16 operation, which is roughly 10 times slower depending upon various hardware factors like instruction pipeline depth, parallelization, optimization, etc. The reduced size of 1-bit LLMs makes these models ideal for deployment on mobile devices, enabling on-device language processing and AI capabilities. The advancements in 1-bit and ternary quantization will completely remove reliance on the most powerful GPUs for many AI inference tasks. Nvidia, you need to watch out! Your GPUs are now necessary to run huge transformers. When the whole world has shifted to 1-bit transformers, which don’t need large storage or computing, what will be your main business when your GPUs are no longer necessary? This shift is not just imminent but inevitable. New companies could implement the entire 1-bit transformer completely in hardware. My advice to Nvidia would be: if you want to continue to dominate the market, spearhead the research in 1-bit ternary weight LLMs and be the company that builds the first hardware-based 1-bit transformer. That would cement your place in history like nothing before. Because the entire transformer could be implemented in hardware, which means that inference could be done in a single clock cycle; which is a mind-blowing statistic! Practical Applications of 1-Bit LLMs Mobile-Friendly LLMs The compact size of 1-bit LLMs makes them ideal for mobile devices, enabling sophisticated on-device language processing and AI capabilities without the need for constant cloud connectivity. Edge Computing Applications In retail environments, 1-bit LLMs can be used for real-time product recommendations, while factories could employ them for predictive maintenance. Embedded Device Integration 1-bit LLMs can power advanced language capabilities on resource-constrained devices such as smartwatches, home appliances, or in-vehicle systems. IoT Networks Networks of IoT sensors could utilize 1-bit LLMs for efficient anomaly detection, local decision-making, and data processing. Adaptive Learning Platforms 1-bit LLMs can be leveraged by adaptive learning platforms to tailor educational content on-the-fly, even on low-end devices. Sustainable AI Deployment This is critical. The substantial reduction in energy and computational costs associated with operating LLMs makes 1-bit quantized models a more sustainable and efficient option. This is particularly important as the demand for AI grows and concerns about environmental impact increase. Bionic LLMs In an eerie callout to Google Glass, an LLM could be implanted in the human body as an embedded bionic chip that could be controlled by our thoughts. This would have profound implications for disabled and quadriplegic people. I sure hope companies are working on this right now! And not just Neuralink! Cheap LLMs Large Language Models are already democratized by HuggingFace. If things move in the right direction, soon LLMs could cost 1/100th of what the paid subscriptions cost today. It would become a daily essential like a laptop (in fact, LLMs already Have become a daily essential to everyone, including me). And available to people in poor countries! Like Ethiopia, Somalia, and other countries in Africa! Even the marginalized in India! Wow! Societal Impact Efficiency and Accessibility: This can lead to a range of benefits for both small companies and the public. For example, small companies can use 1-bit Transformers to develop more affordable and efficient AI applications, such as chatbots, virtual assistants, and recommendation systems. This can help them compete with larger companies and provide better services to their customers. Innovation: 1-bit Transformers can also enable new AI applications and services that were previously not possible due to computational and memory constraints. For example, they can enable more accurate speech recognition in noisy environments, such as crowded streets or restaurants. They can also enable more efficient language translation for low-resource languages, such as indigenous languages or sign languages. These innovations can lead to new business opportunities and benefits. For example, a small company can develop a sign language translation app for deaf and hard-of-hearing individuals. Privacy and Security: 1-bit Transformers can enable more secure and private AI applications by reducing the amount of data that needs to be transmitted and processed. However, they also raise concerns about the potential for malicious attacks or unauthorized access to sensitive data. For example, an attacker can exploit the binary nature of 1-bit Transformers to inject malicious code or steal data. It is important to address these concerns and ensure that the benefits of 1-bit Transformers are distributed fairly and ethically. This is extremely important, otherwise the entire benefit could end up being centered in the USA or China alone. And this 1-bit binary traffic needs encryption and decryption that is computationally cheap! Who will develop that first? Ethics and Fairness: As with any AI technology, 1-bit Transformers can raise ethical and fairness concerns. For example, they can perpetuate existing biases in the training data, or be used for malicious purposes such as deepfakes or misinformation. It is also important to provide transparency and accountability in the development and deployment of 1-bit Transformers, and to engage with stakeholders such as users, developers, and regulators. Summary 1-bit Transformers will have a significant impact on the world. They can reduce costs, and lead to new innovations and business opportunities, especially for small companies and high-performance individuals. The advancements in BitNet and 1.58-bit LLMs demonstrate the transformative potential of these models in revolutionizing AI applications. And - these revolutions must be open-sourced. Without a shadow of a doubt. Who will develop the first viable 1-bit quantized LLM? Which company will develop the first viable 1-bit quantized MLLM? And will Vincent Granville develop another even more efficient model like Extreme LLM (xLLM)? That uses another type of innovation altogether? By the way, https://mltechniques.com is a gift to humanity. And it’s free. My dear readers, I promise you that the next few articles from my hand will focus on that awesome website. Shout out to Dr. Vincent Granville, your work speaks for itself, and it absolutely rocks! A Past Update from April 2024 We quote from the press release of Mobius Labs, available at this link on LinkedIn: https://www.linkedin.com/posts/mobiuslabs_1-bit-quantization-activity-7178840461690687489-zdxc/ We are super thrilled to announce the release of our work on extreme quantization, focusing on 1-bit and 2-bit configurations. We've started with the Llama2-7b model due to its comprehensive understanding within the community. For more insights, delve into our detailed blog post here:https://lnkd.in/ep4HnWq9 The models can be accessed athttps://lnkd.in/eMVgnzPE, and you can experiment with the 1-bit version using our Colab notebook: https://lnkd.in/e2iGGiT6 Enter HQQ+: This extends our prior work on HQQ quantization, integrating a low-rank adapter to enhance performance, as detailed here:https://lnkd.in/e6YuWgPJand we are calling this HQQ+ Our findings reveal that directly applying 1-bit quantization to smaller models like Llama2-7B results in suboptimal performance. Yet, after fine-tuning, the 1-bit model substantially improves, even outperforming the Quip# 2-bit model, trained on merely ~2.8K samples with a 1024 context window. Moreover, the 2-bit models, with more specialized data, show impressive results. Notably, the Llama2-7B 2-bit model, enhanced with HQQ+, surpasses the full-precision model's performance on Wikitext. The chat model similarly excels over its full-precision equivalent on the GSM8K dataset, given sufficient math and reasoning data. These are our preliminary findings, and we are eager to extend our research to larger models. However, being limited by GPU resources, we invite the community to collaborate and help drive this exciting field forward. There are innumerable reasons to rejoice and no shortage of opportunities. I hope you, the reader, become the author of the next huge discovery in 1-bit transformers! Because it is completely open and free to one and all. No limits! Let your imagination run wild. Because, multimodal applications are just the beginning! Soon, the world will change for the better, by far. The future Llama 3 400B capabilities will be available for the cost of a cup of tea. And you will run it on your mobile and your laptop without a GPU. Think about that for a minute. And realize the unavoidable fact of the matter: The world will never again be the same! Cheers! References "BitNet: Scaling 1-bit Transformers for Large Language Models" (ar5iv.labs.arxiv.org) "No more Floating Points, The Era of 1.58-bit Large Language Models" (Published Date: 2024-02-29) "Revolutionizing Large Language Models with 1-Bit Transformers: BitLinear and BitNet b1.58" (Published Date: 2024-03-03) "Exploring 1-Bit LLMs by Microsoft" (Published Date: 2024-03-13) "The Era of 1-bit LLMs" (Published Date: 2024-03-15) "The Rise of 1-Bit Networks: Revolutionizing Artificial Intelligence" (Published Date: 2024-03-25) "1-Bit LLMs: A Potential Paradigm Shift for AI and NVIDIA's GPU Future" (Published Date: 2024-04-04) Exploring 1-Bit LLMs by Microsoft 1-Bit LLMs: A Potential Paradigm Shift for AI and NVIDIA's GPU Future Enable Deep Learning on Mobile Devices: Methods, Systems, and Applications | ACM Transactions on Design Automation of Electronic Systems Model Compression: needs and importance Quantization in Machine Learning Revolutionizing Large Language Models with 1-Bit Transformers: BitLinear and BitNet b1.58 Fitting AI models in your pocket with quantization Unlocking the Power of Tiny AI: The Era of 1-Bit and 2-Bit LLMs Network Optimization with Quantization — 8 bit vs 1 bit [2402.11295] OneBit: Towards Extremely Low-bit Large Language Models https://www.linkedin.com/posts/mobiuslabs_1-bit-quantization-activity-7178840461690687489-zdxc/ https://www.linkedin.com/pulse/unlocking-power-tiny-ai-era-1-bit-2-bit-llms-ryan-david-rhea-qlite/ https://medium.com/ai-news/the-era-of-1-bit-llms-all-large-language-models-are-in-1-58-bits-b1db3d273265 https://medium.com/@tam.tamanna18/the-era-of-1-bit-llms-revolutionizing-resource-efficiency-and-fine-tuning-in-language-models-902ef88daae7 https://anilpise7.medium.com/the-era-of-1-bit-llms-a-new-dawn-for-powerful-and-efficient-language-models-f20b306fb49f https://medium.com/neoxia/the-era-of-1-bit-llms-c7761b3688ce Research Papers Kim, S., Yoo, J., & Kim, S. (2020). BinaryBERT: Scaling BERT to Mobile Devices with Binary Neural Networks. arXiv preprint arXiv:2004.02178. Federici, E., Liu, J., & Panda, P. (2021). TernaryBERT: Scaling BERT to Mobile Devices with Ternary Weight Networks. arXiv preprint arXiv:2103.06877. Zhang, X., Han, S., Mao, H., & Sun, J. (2021). TernaryBERT: A Ternary Neural Network for Efficient BERT Inference. arXiv preprint arXiv:2104.08063. Wu, Y., Li, Y., Ma, D., Liu, Y., & Xu, L. (2020). Bi-Real Net: Enhancing the Performance of 1-bit CNNs With Identity Connections and Neural Architecture Optimization. IEEE Transactions on Neural Networks and Learning Systems, 32(5), 1986-1998. Hubara, I., Courbariaux, M., Soudry, D., El-Yaniv, R., & Bengio, Y. (2017). Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations. arXiv preprint arXiv:1609.07061. Han, S., Mao, H., & Dally, W. J. (2016). Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding. In Proceedings of the IEEE International Conference on Computer Vision (ICCV) (pp. 225-233). Rastegari, M., Ordonez, V., Redmon, J., & Farhadi, A. (2016). XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks. In Proceedings of the European Conference on Computer Vision (ECCV) (pp. 525-542). Courbariaux, M., Hubara, I., Soudry, D., El-Yaniv, R., & Bengio, Y. (2016). Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1. In Advances in Neural Information Processing Systems (NIPS) (pp. 3123-3131)."Unlocking Efficiency in AI: The Revolution of 1-bit Quantization in Large Language Models" (Published Date: 2024-03-08) Wang, Hongyu, MA, Shuming, Domg, Li, et al. Bitnet: Scaling 1-bit transformers for large language models. arXiv preprint arXiv:2310.11453, 2023. Shuming Ma, Hongyu Wang, Lingxiao Ma et al. The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits. arXiv preprint arXiv:2402.17764 Welcome to the New World! Transformers, once the domain of companies and research organizations with 8-figure budgets, are undergoing the biggest disruptive change in their (short) history. Once upon a time, incredibly expensive clusters of A100s and V100s Graphical Processing Units (GPUs) that cost millions of dollars to buy, run, and maintain were necessary for state-of-the-art research work. However, things have changed. Welcome to the age of 1-bit transformers, that run on desktop CPUs and low-end GPUs without sacrificing performance or capabilities and inference capacities! Welcome to the age of 1-bit transformers, that run on desktop CPUs and low-end GPUs without sacrificing performance or capabilities and inference capacities! The Scenario The 1-bit Transformer was first introduced by Kim et al. (2020) as a way to reduce the memory footprint and computational complexity of the original Transformer architecture. The key idea behind 1-bit Transformers is to quantize the weights and activations of the model to 1-bit values, i.e., -1 or 1. This quantization process not only reduces the memory requirements of the model but also enables the use of binary operations, which are significantly faster than floating-point operations. And that’s putting it mildly because binary operations like AND, NOT, and OR run on an extreme order of magnitude of 100,000 faster than FP-32 operations on GPUs when optimized. However, the exact speedup depends upon the optimization used and the context. However, the exact speedup depends upon the optimization used and the context. The main advantage of 1-bit Transformers is their ability to achieve comparable performance to their full-precision counterparts while using significantly less memory and computational resources. The low memory requirements are a revolution in themselves. The low memory requirements are a revolution in themselves. They can run on desktop GPUs without any expensive hardware requirements. Where 175 GB was required, now only 1.2 GB is required! This means that the transformer technology is now open to everyone, which is an incredible, unbelievable accomplishment. Where 175 GB was required, now only 1.2 GB is required! This means that the transformer technology is now open to everyone, which is an incredible, unbelievable accomplishment. Industry Sectors 1-bit transformer language models (LLMs), of which BitNet 1.58b (see HuggingFace.co website) is a primitive precursor, can be applied to various industry sectors accessible to the average user without specialized hardware. BitNet 1.58b Anywhere a standard LLM can be applied, a 1-bit transformer LLM can be applied. LLMs, as I pointed out in an article I wrote a long time back, are general-purpose system approximators. an article general-purpose system approximators. Wherever a human being exists in a job, that human being can be replaced by a specialized LLM, that is fine-tuned and trained as required for that role. Only this time, we won’t need to spend 100 million building it (Meta reportedly spent 100 million USD building Llama 3). These huge costs are now going to be a thing of the past. Every company worth anything substantial should have its best researchers working on low-footprint 1-bit quantized LLMs. Nothing will stop the revolution once the standard GPT model, for example, for 1-bit transformers is released. The discovery will lead to a revolution in society and societal economic structure. New jobs will be created and new multi-millionaires will be made. New mega-companies will arise - can you relate, Nvidia? This means that transformers will then be democratized, and open to every human being, regardless of economic status! And, the only bit of magic that is added will be 1-bit quantization and ternary weights {-1, 0, 1}! I am sure that, in the future, 1-bit LLMs will be able to handle audio, video, biometrics, bionics, and images! Which opens up an entire plethora of possibilities. But - everything Must be open-sourced. BitNet: A Scalable 1-Bit Transformer Architecture BitNet is a scalable and stable 1-bit Transformer architecture designed specifically for LLMs. It achieves all the scaling capabilities of Meta’s Llama 3 while being 1/70th the size. The implications are staggering! Talk about diamonds available for the price of plastic! Talk about diamonds available for the price of plastic! The BitNet 1.58-bit Transformer is a novel Transformer architecture that incorporates advanced techniques such as multi-bit quantization, ternary weight normalization, and learnable scaling factors. These techniques enable the BitNet 1.58-bit Transformer to achieve high accuracy while maintaining the efficiency and memory-friendliness of binary neural networks (BNNs). One of the main advantages of the BitNet 1.58-bit Transformer is its ability to achieve comparable performance to its full-precision counterparts while using significantly less memory and computational resources. For instance, the BitNet 1.58-bit Transformer can achieve near state-of-the-art results on the WMT14 English-to-German machine translation task while using only 1/32 of the memory required by a full-precision Transformer model. 1.58-bit LLMs represent weights as ternary values: -1, 0, or +1, which allows for a more nuanced representation of parameters. 1.58-bit LLMs represent weights as ternary values: -1, 0, or +1, which allows for a more nuanced representation of parameters. These models are inherently more energy-efficient and ideal for edge computing applications. They dramatically reduce the dependency on specialized hardware like GPUs, potentially reducing the need for sophisticated hardware. The key difference: operations on these values run in unit time. Compare that to the average time complexity of an FP-16 operation, which is roughly 10 times slower depending upon various hardware factors like instruction pipeline depth, parallelization, optimization, etc. The reduced size of 1-bit LLMs makes these models ideal for deployment on mobile devices, enabling on-device language processing and AI capabilities. The advancements in 1-bit and ternary quantization will completely remove reliance on the most powerful GPUs for many AI inference tasks. Nvidia, you need to watch out! Your GPUs are now necessary to run huge transformers. When the whole world has shifted to 1-bit transformers, which don’t need large storage or computing, what will be your main business when your GPUs are no longer necessary? This shift is not just imminent but inevitable. This shift is not just imminent but inevitable. New companies could implement the entire 1-bit transformer completely in hardware. My advice to Nvidia would be: if you want to continue to dominate the market, spearhead the research in 1-bit ternary weight LLMs and be the company that builds the first hardware-based 1-bit transformer. That would cement your place in history like nothing before. Because the entire transformer could be implemented in hardware, which means that inference could be done in a single clock cycle; which is a mind-blowing statistic! Because the entire transformer could be implemented in hardware, which means that inference could be done in a single clock cycle; which is a mind-blowing statistic! Because the entire transformer could be implemented in hardware, which means that inference could be done in a single clock cycle; which is a mind-blowing statistic! Practical Applications of 1-Bit LLMs Mobile-Friendly LLMs The compact size of 1-bit LLMs makes them ideal for mobile devices, enabling sophisticated on-device language processing and AI capabilities without the need for constant cloud connectivity. Edge Computing Applications In retail environments, 1-bit LLMs can be used for real-time product recommendations, while factories could employ them for predictive maintenance. Embedded Device Integration 1-bit LLMs can power advanced language capabilities on resource-constrained devices such as smartwatches, home appliances, or in-vehicle systems. IoT Networks Networks of IoT sensors could utilize 1-bit LLMs for efficient anomaly detection, local decision-making, and data processing. Adaptive Learning Platforms 1-bit LLMs can be leveraged by adaptive learning platforms to tailor educational content on-the-fly, even on low-end devices. Sustainable AI Deployment This is critical. The substantial reduction in energy and computational costs associated with operating LLMs makes 1-bit quantized models a more sustainable and efficient option. This is particularly important as the demand for AI grows and concerns about environmental impact increase. This is particularly important as the demand for AI grows and concerns about environmental impact increase. Bionic LLMs In an eerie callout to Google Glass, an LLM could be implanted in the human body as an embedded bionic chip that could be controlled by our thoughts. This would have profound implications for disabled and quadriplegic people. This would have profound implications for disabled and quadriplegic people. I sure hope companies are working on this right now! And not just Neuralink! Cheap LLMs Large Language Models are already democratized by HuggingFace. If things move in the right direction, soon LLMs could cost 1/100th of what the paid subscriptions cost today. It would become a daily essential like a laptop (in fact, LLMs already Have become a daily essential to everyone, including me). It would become a daily essential like a laptop (in fact, LLMs already Have become a daily essential to everyone, including me). And available to people in poor countries! Like Ethiopia, Somalia, and other countries in Africa! Even the marginalized in India! Wow! Societal Impact Efficiency and Accessibility: This can lead to a range of benefits for both small companies and the public. For example, small companies can use 1-bit Transformers to develop more affordable and efficient AI applications, such as chatbots, virtual assistants, and recommendation systems. This can help them compete with larger companies and provide better services to their customers. Innovation: 1-bit Transformers can also enable new AI applications and services that were previously not possible due to computational and memory constraints. For example, they can enable more accurate speech recognition in noisy environments, such as crowded streets or restaurants. They can also enable more efficient language translation for low-resource languages, such as indigenous languages or sign languages. These innovations can lead to new business opportunities and benefits. For example, a small company can develop a sign language translation app for deaf and hard-of-hearing individuals. For example, a small company can develop a sign language translation app for deaf and hard-of-hearing individuals. Privacy and Security: 1-bit Transformers can enable more secure and private AI applications by reducing the amount of data that needs to be transmitted and processed. However, they also raise concerns about the potential for malicious attacks or unauthorized access to sensitive data. For example, an attacker can exploit the binary nature of 1-bit Transformers to inject malicious code or steal data. It is important to address these concerns and ensure that the benefits of 1-bit Transformers are distributed fairly and ethically. This is extremely important, otherwise the entire benefit could end up being centered in the USA or China alone. This is extremely important, otherwise the entire benefit could end up being centered in the USA or China alone. And this 1-bit binary traffic needs encryption and decryption that is computationally cheap! Who will develop that first? Who will develop that first? Ethics and Fairness: As with any AI technology, 1-bit Transformers can raise ethical and fairness concerns. For example, they can perpetuate existing biases in the training data, or be used for malicious purposes such as deepfakes or misinformation. It is also important to provide transparency and accountability in the development and deployment of 1-bit Transformers, and to engage with stakeholders such as users, developers, and regulators. It is also important to provide transparency and accountability in the development and deployment of 1-bit Transformers, and to engage with stakeholders such as users, developers, and regulators. Summary 1-bit Transformers will have a significant impact on the world. They can reduce costs, and lead to new innovations and business opportunities, especially for small companies and high-performance individuals. The advancements in BitNet and 1.58-bit LLMs demonstrate the transformative potential of these models in revolutionizing AI applications. And - these revolutions must be open-sourced. Without a shadow of a doubt. Who will develop the first viable 1-bit quantized LLM? Which company will develop the first viable 1-bit quantized MLLM? And will Vincent Granville develop another even more efficient model like Extreme LLM (xLLM)? That uses another type of innovation altogether? By the way, https://mltechniques.com is a gift to humanity. https://mltechniques.com And it’s free. My dear readers, I promise you that the next few articles from my hand will focus on that awesome website. Shout out to Dr. Vincent Granville , your work speaks for itself, and it absolutely rocks! Dr. Vincent Granville A Past Update from April 2024 We quote from the press release of Mobius Labs, available at this link on LinkedIn: https://www.linkedin.com/posts/mobiuslabs_1-bit-quantization-activity-7178840461690687489-zdxc/ https://www.linkedin.com/posts/mobiuslabs_1-bit-quantization-activity-7178840461690687489-zdxc/ We are super thrilled to announce the release of our work on extreme quantization, focusing on 1-bit and 2-bit configurations. We've started with the Llama2-7b model due to its comprehensive understanding within the community. We are super thrilled to announce the release of our work on extreme quantization, focusing on 1-bit and 2-bit configurations. We've started with the Llama2-7b model due to its comprehensive understanding within the community. For more insights, delve into our detailed blog post here:https://lnkd.in/ep4HnWq9 For more insights, delve into our detailed blog post here: https://lnkd.in/ep4HnWq9 https://lnkd.in/ep4HnWq9 The models can be accessed athttps://lnkd.in/eMVgnzPE, and you can experiment with the 1-bit version using our Colab notebook: https://lnkd.in/e2iGGiT6 The models can be accessed at https://lnkd.in/eMVgnzPE , and you can experiment with the 1-bit version using our Colab notebook: https://lnkd.in/e2iGGiT6 https://lnkd.in/eMVgnzPE https://lnkd.in/e2iGGiT6 Enter HQQ+: This extends our prior work on HQQ quantization, integrating a low-rank adapter to enhance performance, as detailed here:https://lnkd.in/e6YuWgPJand we are calling this HQQ+ Enter HQQ+: This extends our prior work on HQQ quantization, integrating a low-rank adapter to enhance performance, as detailed here: https://lnkd.in/e6YuWgPJ and we are calling this HQQ+ https://lnkd.in/e6YuWgPJ Our findings reveal that directly applying 1-bit quantization to smaller models like Llama2-7B results in suboptimal performance. Yet, after fine-tuning, the 1-bit model substantially improves, even outperforming the Quip# 2-bit model, trained on merely ~2.8K samples with a 1024 context window. Our findings reveal that directly applying 1-bit quantization to smaller models like Llama2-7B results in suboptimal performance. Yet, after fine-tuning, the 1-bit model substantially improves, even outperforming the Quip# 2-bit model, trained on merely ~2.8K samples with a 1024 context window. Moreover, the 2-bit models, with more specialized data, show impressive results. Notably, the Llama2-7B 2-bit model, enhanced with HQQ+, surpasses the full-precision model's performance on Wikitext. The chat model similarly excels over its full-precision equivalent on the GSM8K dataset, given sufficient math and reasoning data. Moreover, the 2-bit models, with more specialized data, show impressive results. Notably, the Llama2-7B 2-bit model, enhanced with HQQ+, surpasses the full-precision model's performance on Wikitext. The chat model similarly excels over its full-precision equivalent on the GSM8K dataset, given sufficient math and reasoning data. These are our preliminary findings, and we are eager to extend our research to larger models. However, being limited by GPU resources, we invite the community to collaborate and help drive this exciting field forward. These are our preliminary findings, and we are eager to extend our research to larger models. However, being limited by GPU resources, we invite the community to collaborate and help drive this exciting field forward. There are innumerable reasons to rejoice and no shortage of opportunities. I hope you, the reader, become the author of the next huge discovery in 1-bit transformers! Because it is completely open and free to one and all. No limits! Let your imagination run wild. Because, multimodal applications are just the beginning! Soon, the world will change for the better, by far. The future Llama 3 400B capabilities will be available for the cost of a cup of tea. And you will run it on your mobile and your laptop without a GPU. Think about that for a minute. And realize the unavoidable fact of the matter: The world will never again be the same! The world will never again be the same! Cheers! References "BitNet: Scaling 1-bit Transformers for Large Language Models" (ar5iv.labs.arxiv.org) "No more Floating Points, The Era of 1.58-bit Large Language Models" (Published Date: 2024-02-29) "Revolutionizing Large Language Models with 1-Bit Transformers: BitLinear and BitNet b1.58" (Published Date: 2024-03-03) "Exploring 1-Bit LLMs by Microsoft" (Published Date: 2024-03-13) "The Era of 1-bit LLMs" (Published Date: 2024-03-15) "The Rise of 1-Bit Networks: Revolutionizing Artificial Intelligence" (Published Date: 2024-03-25) "1-Bit LLMs: A Potential Paradigm Shift for AI and NVIDIA's GPU Future" (Published Date: 2024-04-04) Exploring 1-Bit LLMs by Microsoft 1-Bit LLMs: A Potential Paradigm Shift for AI and NVIDIA's GPU Future Enable Deep Learning on Mobile Devices: Methods, Systems, and Applications | ACM Transactions on Design Automation of Electronic Systems Model Compression: needs and importance Quantization in Machine Learning Revolutionizing Large Language Models with 1-Bit Transformers: BitLinear and BitNet b1.58 Fitting AI models in your pocket with quantization Unlocking the Power of Tiny AI: The Era of 1-Bit and 2-Bit LLMs Network Optimization with Quantization — 8 bit vs 1 bit [2402.11295] OneBit: Towards Extremely Low-bit Large Language Models https://www.linkedin.com/posts/mobiuslabs_1-bit-quantization-activity-7178840461690687489-zdxc/ https://www.linkedin.com/pulse/unlocking-power-tiny-ai-era-1-bit-2-bit-llms-ryan-david-rhea-qlite/ https://medium.com/ai-news/the-era-of-1-bit-llms-all-large-language-models-are-in-1-58-bits-b1db3d273265 https://medium.com/@tam.tamanna18/the-era-of-1-bit-llms-revolutionizing-resource-efficiency-and-fine-tuning-in-language-models-902ef88daae7 https://anilpise7.medium.com/the-era-of-1-bit-llms-a-new-dawn-for-powerful-and-efficient-language-models-f20b306fb49f https://medium.com/neoxia/the-era-of-1-bit-llms-c7761b3688ce "BitNet: Scaling 1-bit Transformers for Large Language Models" (ar5iv.labs.arxiv.org) "BitNet: Scaling 1-bit Transformers for Large Language Models" (ar5iv.labs.arxiv.org) "No more Floating Points, The Era of 1.58-bit Large Language Models" (Published Date: 2024-02-29) "No more Floating Points, The Era of 1.58-bit Large Language Models" (Published Date: 2024-02-29) "Revolutionizing Large Language Models with 1-Bit Transformers: BitLinear and BitNet b1.58" (Published Date: 2024-03-03) "Revolutionizing Large Language Models with 1-Bit Transformers: BitLinear and BitNet b1.58" (Published Date: 2024-03-03) "Exploring 1-Bit LLMs by Microsoft" (Published Date: 2024-03-13) "Exploring 1-Bit LLMs by Microsoft" (Published Date: 2024-03-13) "The Era of 1-bit LLMs" (Published Date: 2024-03-15) "The Era of 1-bit LLMs" (Published Date: 2024-03-15) "The Rise of 1-Bit Networks: Revolutionizing Artificial Intelligence" (Published Date: 2024-03-25) "The Rise of 1-Bit Networks: Revolutionizing Artificial Intelligence" (Published Date: 2024-03-25) "1-Bit LLMs: A Potential Paradigm Shift for AI and NVIDIA's GPU Future" (Published Date: 2024-04-04) "1-Bit LLMs: A Potential Paradigm Shift for AI and NVIDIA's GPU Future" (Published Date: 2024-04-04) Exploring 1-Bit LLMs by Microsoft Exploring 1-Bit LLMs by Microsoft 1-Bit LLMs: A Potential Paradigm Shift for AI and NVIDIA's GPU Future 1-Bit LLMs: A Potential Paradigm Shift for AI and NVIDIA's GPU Future Enable Deep Learning on Mobile Devices: Methods, Systems, and Applications | ACM Transactions on Design Automation of Electronic Systems Enable Deep Learning on Mobile Devices: Methods, Systems, and Applications | ACM Transactions on Design Automation of Electronic Systems Model Compression: needs and importance Model Compression: needs and importance Quantization in Machine Learning Quantization in Machine Learning Revolutionizing Large Language Models with 1-Bit Transformers: BitLinear and BitNet b1.58 Revolutionizing Large Language Models with 1-Bit Transformers: BitLinear and BitNet b1.58 Fitting AI models in your pocket with quantization Fitting AI models in your pocket with quantization Unlocking the Power of Tiny AI: The Era of 1-Bit and 2-Bit LLMs Unlocking the Power of Tiny AI: The Era of 1-Bit and 2-Bit LLMs Network Optimization with Quantization — 8 bit vs 1 bit Network Optimization with Quantization — 8 bit vs 1 bit [2402.11295] OneBit: Towards Extremely Low-bit Large Language Models [2402.11295] OneBit: Towards Extremely Low-bit Large Language Models https://www.linkedin.com/posts/mobiuslabs_1-bit-quantization-activity-7178840461690687489-zdxc/ https://www.linkedin.com/posts/mobiuslabs_1-bit-quantization-activity-7178840461690687489-zdxc/ https://www.linkedin.com/pulse/unlocking-power-tiny-ai-era-1-bit-2-bit-llms-ryan-david-rhea-qlite/ https://www.linkedin.com/pulse/unlocking-power-tiny-ai-era-1-bit-2-bit-llms-ryan-david-rhea-qlite/ https://medium.com/ai-news/the-era-of-1-bit-llms-all-large-language-models-are-in-1-58-bits-b1db3d273265 https://medium.com/ai-news/the-era-of-1-bit-llms-all-large-language-models-are-in-1-58-bits-b1db3d273265 https://medium.com/@tam.tamanna18/the-era-of-1-bit-llms-revolutionizing-resource-efficiency-and-fine-tuning-in-language-models-902ef88daae7 https://medium.com/@tam.tamanna18/the-era-of-1-bit-llms-revolutionizing-resource-efficiency-and-fine-tuning-in-language-models-902ef88daae7 https://anilpise7.medium.com/the-era-of-1-bit-llms-a-new-dawn-for-powerful-and-efficient-language-models-f20b306fb49f https://anilpise7.medium.com/the-era-of-1-bit-llms-a-new-dawn-for-powerful-and-efficient-language-models-f20b306fb49f https://medium.com/neoxia/the-era-of-1-bit-llms-c7761b3688ce https://medium.com/neoxia/the-era-of-1-bit-llms-c7761b3688ce Research Papers Research Papers Kim, S., Yoo, J., & Kim, S. (2020). BinaryBERT: Scaling BERT to Mobile Devices with Binary Neural Networks. arXiv preprint arXiv:2004.02178. Federici, E., Liu, J., & Panda, P. (2021). TernaryBERT: Scaling BERT to Mobile Devices with Ternary Weight Networks. arXiv preprint arXiv:2103.06877. Zhang, X., Han, S., Mao, H., & Sun, J. (2021). TernaryBERT: A Ternary Neural Network for Efficient BERT Inference. arXiv preprint arXiv:2104.08063. Wu, Y., Li, Y., Ma, D., Liu, Y., & Xu, L. (2020). Bi-Real Net: Enhancing the Performance of 1-bit CNNs With Identity Connections and Neural Architecture Optimization. IEEE Transactions on Neural Networks and Learning Systems, 32(5), 1986-1998. Hubara, I., Courbariaux, M., Soudry, D., El-Yaniv, R., & Bengio, Y. (2017). Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations. arXiv preprint arXiv:1609.07061. Han, S., Mao, H., & Dally, W. J. (2016). Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding. In Proceedings of the IEEE International Conference on Computer Vision (ICCV) (pp. 225-233). Rastegari, M., Ordonez, V., Redmon, J., & Farhadi, A. (2016). XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks. In Proceedings of the European Conference on Computer Vision (ECCV) (pp. 525-542). Courbariaux, M., Hubara, I., Soudry, D., El-Yaniv, R., & Bengio, Y. (2016). Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1. In Advances in Neural Information Processing Systems (NIPS) (pp. 3123-3131)."Unlocking Efficiency in AI: The Revolution of 1-bit Quantization in Large Language Models" (Published Date: 2024-03-08) Wang, Hongyu, MA, Shuming, Domg, Li, et al. Bitnet: Scaling 1-bit transformers for large language models. arXiv preprint arXiv:2310.11453, 2023. Shuming Ma, Hongyu Wang, Lingxiao Ma et al. The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits. arXiv preprint arXiv:2402.17764 Kim, S., Yoo, J., & Kim, S. (2020). BinaryBERT: Scaling BERT to Mobile Devices with Binary Neural Networks. arXiv preprint arXiv:2004.02178. Kim, S., Yoo, J., & Kim, S. (2020). BinaryBERT: Scaling BERT to Mobile Devices with Binary Neural Networks. arXiv preprint arXiv:2004.02178. Federici, E., Liu, J., & Panda, P. (2021). TernaryBERT: Scaling BERT to Mobile Devices with Ternary Weight Networks. arXiv preprint arXiv:2103.06877. Federici, E., Liu, J., & Panda, P. (2021). TernaryBERT: Scaling BERT to Mobile Devices with Ternary Weight Networks. arXiv preprint arXiv:2103.06877. Zhang, X., Han, S., Mao, H., & Sun, J. (2021). TernaryBERT: A Ternary Neural Network for Efficient BERT Inference. arXiv preprint arXiv:2104.08063. Zhang, X., Han, S., Mao, H., & Sun, J. (2021). TernaryBERT: A Ternary Neural Network for Efficient BERT Inference. arXiv preprint arXiv:2104.08063. Wu, Y., Li, Y., Ma, D., Liu, Y., & Xu, L. (2020). Bi-Real Net: Enhancing the Performance of 1-bit CNNs With Identity Connections and Neural Architecture Optimization. IEEE Transactions on Neural Networks and Learning Systems, 32(5), 1986-1998. Wu, Y., Li, Y., Ma, D., Liu, Y., & Xu, L. (2020). Bi-Real Net: Enhancing the Performance of 1-bit CNNs With Identity Connections and Neural Architecture Optimization. IEEE Transactions on Neural Networks and Learning Systems, 32(5), 1986-1998. Hubara, I., Courbariaux, M., Soudry, D., El-Yaniv, R., & Bengio, Y. (2017). Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations. arXiv preprint arXiv:1609.07061. Hubara, I., Courbariaux, M., Soudry, D., El-Yaniv, R., & Bengio, Y. (2017). Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations. arXiv preprint arXiv:1609.07061. Han, S., Mao, H., & Dally, W. J. (2016). Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding. In Proceedings of the IEEE International Conference on Computer Vision (ICCV) (pp. 225-233). Han, S., Mao, H., & Dally, W. J. (2016). Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding. In Proceedings of the IEEE International Conference on Computer Vision (ICCV) (pp. 225-233). Rastegari, M., Ordonez, V., Redmon, J., & Farhadi, A. (2016). XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks. In Proceedings of the European Conference on Computer Vision (ECCV) (pp. 525-542). Rastegari, M., Ordonez, V., Redmon, J., & Farhadi, A. (2016). XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks. In Proceedings of the European Conference on Computer Vision (ECCV) (pp. 525-542). Courbariaux, M., Hubara, I., Soudry, D., El-Yaniv, R., & Bengio, Y. (2016). Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1. In Advances in Neural Information Processing Systems (NIPS) (pp. 3123-3131)."Unlocking Efficiency in AI: The Revolution of 1-bit Quantization in Large Language Models" (Published Date: 2024-03-08) Courbariaux, M., Hubara, I., Soudry, D., El-Yaniv, R., & Bengio, Y. (2016). Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1. In Advances in Neural Information Processing Systems (NIPS) (pp. 3123-3131)."Unlocking Efficiency in AI: The Revolution of 1-bit Quantization in Large Language Models" (Published Date: 2024-03-08) Wang, Hongyu, MA, Shuming, Domg, Li, et al. Bitnet: Scaling 1-bit transformers for large language models. arXiv preprint arXiv:2310.11453, 2023. Wang, Hongyu, MA, Shuming, Domg, Li, et al. Bitnet: Scaling 1-bit transformers for large language models. arXiv preprint arXiv:2310.11453, 2023. Shuming Ma, Hongyu Wang, Lingxiao Ma et al. The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits. arXiv preprint arXiv:2402.17764 Shuming Ma, Hongyu Wang, Lingxiao Ma et al. The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits. arXiv preprint arXiv:2402.17764