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Faster AI Code Generation with Bifurcated Attention

Table of Links Abstract and 1 Introduction 2 Background and 2.1 Transformer-Based Large Language Models 2.2 LLM Service & Autoregressive Generation 2.3 Batching Techniques for LLMs 3 Memory Challenges in LLM Serving 3.1 Memory Management in Existing Systems 4 Method and 4.1 PagedAttention 4.2 KV Cache Manager 4.3 Decoding with PagedAttention and vLLM 4.4 Application to Other Decoding Scenarios 4.5 Scheduling and Preemption 4.6 Distributed Execution 5 Implementation 6 Evaluation and 6.1 Experimental Setup 6.2 Basic Sampling 6.3 Parallel Sampling and Beam Search 6.4 Shared prefix 6.5 Chatbot 7 Ablation Studies 8 Discussion 9 Related Work 10 Conclusion, Acknowledgement and References 6.4 Shared prefix We explore the effectiveness of vLLM for the case a prefix is shared among different input prompts, as illustrated in Fig. 10. For the model, we use LLaMA-13B [52], which is multilingual. For the workload, we use the WMT16 [4] Englishto-German translation dataset and synthesize two prefixes that include an instruction and a few translation examples. The first prefix includes a single example (i.e., one-shot) while the other prefix includes 5 examples (i.e., few-shot). As shown in Fig. 16 (a), vLLM achieves 1.67× higher throughput than Orca (Oracle) when the one-shot prefix is shared. Furthermore, when more examples are shared (Fig. 16 (b)), vLLM achieves 3.58× higher throughput than Orca (Oracle). This paper is available on arxiv under CC BY 4.0 DEED license. Authors:
(1) Woosuk Kwon, UC Berkeley with Equal contribution;
(2) Zhuohan Li, UC Berkeley with Equal contribution;
(3) Siyuan Zhuang, UC Berkeley;
(4) Ying Sheng, UC Berkeley and Stanford University;
(5) Lianmin Zheng, UC Berkeley;
(6) Cody Hao Yu, Independent Researcher;
(7) Cody Hao Yu, Independent Researcher;
(8) Joseph E. Gonzalez, UC Berkeley;
(9) Hao Zhang, UC San Diego;
(10) Ion Stoica, UC Berkeley. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Background and 2.1 Transformer-Based Large Language Models 2 Background and 2.1 Transformer-Based Large Language Models 2.2 LLM Service & Autoregressive Generation 2.2 LLM Service & Autoregressive Generation 2.3 Batching Techniques for LLMs 2.3 Batching Techniques for LLMs 3 Memory Challenges in LLM Serving 3 Memory Challenges in LLM Serving 3.1 Memory Management in Existing Systems 3.1 Memory Management in Existing Systems 4 Method and 4.1 PagedAttention 4 Method and 4.1 PagedAttention 4.2 KV Cache Manager 4.2 KV Cache Manager 4.3 Decoding with PagedAttention and vLLM 4.3 Decoding with PagedAttention and vLLM 4.4 Application to Other Decoding Scenarios 4.4 Application to Other Decoding Scenarios 4.5 Scheduling and Preemption 4.5 Scheduling and Preemption 4.6 Distributed Execution 4.6 Distributed Execution 5 Implementation 5 Implementation 6 Evaluation and 6.1 Experimental Setup 6 Evaluation and 6.1 Experimental Setup 6.2 Basic Sampling 6.2 Basic Sampling 6.3 Parallel Sampling and Beam Search 6.3 Parallel Sampling and Beam Search 6.4 Shared prefix 6.4 Shared prefix 6.5 Chatbot 6.5 Chatbot 7 Ablation Studies 7 Ablation Studies 8 Discussion 8 Discussion 9 Related Work 9 Related Work 10 Conclusion, Acknowledgement and References 10 Conclusion, Acknowledgement and References 6.4 Shared prefix We explore the effectiveness of vLLM for the case a prefix is shared among different input prompts, as illustrated in Fig. 10. For the model, we use LLaMA-13B [52], which is multilingual. For the workload, we use the WMT16 [4] Englishto-German translation dataset and synthesize two prefixes that include an instruction and a few translation examples. The first prefix includes a single example (i.e., one-shot) while the other prefix includes 5 examples (i.e., few-shot). As shown in Fig. 16 (a), vLLM achieves 1.67× higher throughput than Orca (Oracle) when the one-shot prefix is shared. Furthermore, when more examples are shared (Fig. 16 (b)), vLLM achieves 3.58× higher throughput than Orca (Oracle). This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv Authors: (1) Woosuk Kwon, UC Berkeley with Equal contribution; (2) Zhuohan Li, UC Berkeley with Equal contribution; (3) Siyuan Zhuang, UC Berkeley; (4) Ying Sheng, UC Berkeley and Stanford University; (5) Lianmin Zheng, UC Berkeley; (6) Cody Hao Yu, Independent Researcher; (7) Cody Hao Yu, Independent Researcher; (8) Joseph E. Gonzalez, UC Berkeley; (9) Hao Zhang, UC San Diego; (10) Ion Stoica, UC Berkeley. Authors: Authors: (1) Woosuk Kwon, UC Berkeley with Equal contribution; (2) Zhuohan Li, UC Berkeley with Equal contribution; (3) Siyuan Zhuang, UC Berkeley; (4) Ying Sheng, UC Berkeley and Stanford University; (5) Lianmin Zheng, UC Berkeley; (6) Cody Hao Yu, Independent Researcher; (7) Cody Hao Yu, Independent Researcher; (8) Joseph E. Gonzalez, UC Berkeley; (9) Hao Zhang, UC San Diego; (10) Ion Stoica, UC Berkeley.

How Effective is vLLM When a Prefix Is Thrown Into the Mix?

Practical LLMs for Real-World Applications

Authors:
(1) Suzanna Sia, Johns Hopkins University;
(2) David Mueller;
(3) Kevin Duh. Table of Links Abstract and 1. Background
2. Data and Settings
3. Where does In-context MT happen?
4. Characterising Redundancy in Layers
5. Inference Efficiency
6. Further Analysis
7. Conclusion, Acknowledgments, and References
A. Appendix A. Appendix A.1. Graphical View of Context Masking Experiments A.2. Prompt Format A.3. Additional Results on English & Spanish In addition to the language pairs en → fr and fr → en, we also run experiments on English and Spanish language pairs, both en → es and es → en. Due to space limitations, we plot the results of those experiments here. Overall, we see largely identical trends on both directions of English and Spanish to what we observe on English and French translation tasks, leading us to conclude that our conclusions generalize across different translation tasks. A.4. Autoregressive Decoder only Transformer The transformer consists of stacked blocks of self-attention, which itself consists of smaller units of self-attention heads that are concatenated before being fed through a fully connected layer. In autoregressive decoder-only transformers, training and inference adopts a causal mask, where current positions are only able to attend to previous timesteps, instead of being able to attend to the entire input sequence. Unlike encoder-decoder NMT models where source and target sentence have separate processing transformer blocks, decoder-only means that the same model weights are both used to “encode" the source sentence and “decode" the target sentence in a single continuous sequence. A.5. Training with Autoregressive Translation The original language modeling objective in GPT training involves predicting the entire input token sequence which consists of both the source and target sentence (shifted by 1 position). We found this to produce slightly worse results than only minimising the negative log likelihood of predicting the target sentence to be translated, and not the entire sequence. We consider this autoregressive translation training. A.6. L0 Attention Gate Training Training Details For Section 6.5, We train using Adam Optimizer (β1 = 0.9, β2 = 0.999) with a batch size of 32, and learning rate of 0.001, early stopping patience of 10 and threshold of 0.01. We initialise attention head gates to be 1 instead of random or 0.5 as this leads to faster convergence. We experiment with two different training settings, the 0-prompts Train setting and the 5-prompts Train setting. As described in Section A.5, we train the model by predicting only the target sentence, conditioned on the context. In the 0-prompt setting, the context consists of the instructions and the source sentence to be translated. In the 5-prompt setting, the context consists of the instructions, 5 prompt examples, and the source sentence to be translated. In the 0-prompt setting, the conditional prefix consists of the instructions and the source sentence to be translated. In the 5-prompt setting, the conditional prefix consists of the instruction, 5 source target sentence pairs, and the source sentence to be translated. Data We used the first 10,000 lines of en → fr from WMT06 Europarl (Koehn, 2005) for training.[7] To test the generalisability of trained attention head gates, we use a different test domain, FLORES (Goyal et al., 2021) to reflect the scarcity of in-domain data. We also test an additional language direction en→pt in FLORES to see if training can generalise across languages. Training Details We train using Adam Optimizer (β1 = 0.9, β2 = 0.999) with a batch size of 32, and learning rate of 0.001. We use a large early stopping patience of 10 and threshold of 0.01, and train for up to 100 epochs. This is due to the nature of L0 training; we do not expect performance to improve over many iterations and would like the attention gates to keep training as long as there is no large loss in performance. We initialise attention head gates to be 1 instead of random or 0.5 as this leads to much faster convergence and better performance. For the regularisation weight λ, we search over a hyperparameter set of {0.1, 0.01, 0.001, 0.0001} and found 0.01 performs best on the validation set. A.7. L0 head masking experiments. Additional experiments on L0 head masking in the fr→ en and es→ en direction. A.8. Generalisability of L0 gate training We experiment with 0-prompts and 5-prompts in training and using λ = 0 (no regularisation) and λ = 0.01. L0 training for the 0-prompts shows some gains for the 0-prompts test case, and with no loss on the 5-prompts test case (Table 2). Notably, this persists in en→pt, a different language direction from training. The robustness of translation performance under multiple testing conditions (number of prompts, datasets, language directions) gives some confidence that the trained discrete attention head gates from L0 support a general ability to translate (Table 2). In contrast, the soft attention head gates without regularisation (λ = 0) appear to overfit as they perform well on some conditions but deteriorate in others. We observe that 0-prompt training for L0(λ = 0.01) also A.9. Qualitative Analysis of Layer-wise Masking BLOOM is observed to be more robust to masking of layers; suggesting that task location is more distributed. For the 5-prompt setting, the performance only decreases very slightly. For the 0-prompt setting, we observe that similar to GPTNEO, performance drops when masking out the middle layers. At the aggregate level, BLOOM appears to still be translating (> 0 BLEU) even when layers are masked. However we observe that the drop in performance is due to around 40 to 50% of the test sentences scoring < 5 BLEU points. There is a clear failure to translate, not simply producing poorer translations. This paper is available on arxiv under CC BY 4.0 DEED license. [7] Data available from https://www.statmt.org/europarl/ Authors: (1) Suzanna Sia, Johns Hopkins University; (2) David Mueller; (3) Kevin Duh. Authors: Authors: (1) Suzanna Sia, Johns Hopkins University; (2) David Mueller; (3) Kevin Duh. Table of Links Abstract and 1. Background 2. Data and Settings 3. Where does In-context MT happen? 4. Characterising Redundancy in Layers 5. Inference Efficiency 6. Further Analysis 7. Conclusion, Acknowledgments, and References A. Appendix Abstract and 1. Background Abstract and 1. Background 2. Data and Settings 2. Data and Settings 3. Where does In-context MT happen? 3. Where does In-context MT happen? 4. Characterising Redundancy in Layers 4. Characterising Redundancy in Layers 5. Inference Efficiency 5. Inference Efficiency 6. Further Analysis 6. Further Analysis 7. Conclusion, Acknowledgments, and References 7. Conclusion, Acknowledgments, and References A. Appendix A. Appendix A. Appendix A.1. Graphical View of Context Masking Experiments A.2. Prompt Format A.3. Additional Results on English & Spanish In addition to the language pairs en → fr and fr → en, we also run experiments on English and Spanish language pairs, both en → es and es → en. Due to space limitations, we plot the results of those experiments here. Overall, we see largely identical trends on both directions of English and Spanish to what we observe on English and French translation tasks, leading us to conclude that our conclusions generalize across different translation tasks. A.4. Autoregressive Decoder only Transformer The transformer consists of stacked blocks of self-attention, which itself consists of smaller units of self-attention heads that are concatenated before being fed through a fully connected layer. In autoregressive decoder-only transformers, training and inference adopts a causal mask, where current positions are only able to attend to previous timesteps, instead of being able to attend to the entire input sequence. Unlike encoder-decoder NMT models where source and target sentence have separate processing transformer blocks, decoder-only means that the same model weights are both used to “encode" the source sentence and “decode" the target sentence in a single continuous sequence. A.5. Training with Autoregressive Translation The original language modeling objective in GPT training involves predicting the entire input token sequence which consists of both the source and target sentence (shifted by 1 position). We found this to produce slightly worse results than only minimising the negative log likelihood of predicting the target sentence to be translated, and not the entire sequence. We consider this autoregressive translation training. A.6. L0 Attention Gate Training Training Details For Section 6.5, We train using Adam Optimizer (β1 = 0.9, β2 = 0.999) with a batch size of 32, and learning rate of 0.001, early stopping patience of 10 and threshold of 0.01. We initialise attention head gates to be 1 instead of random or 0.5 as this leads to faster convergence. We experiment with two different training settings, the 0-prompts Train setting and the 5-prompts Train setting. As described in Section A.5, we train the model by predicting only the target sentence, conditioned on the context. In the 0-prompt setting, the context consists of the instructions and the source sentence to be translated. In the 5-prompt setting, the context consists of the instructions, 5 prompt examples, and the source sentence to be translated. In the 0-prompt setting, the conditional prefix consists of the instructions and the source sentence to be translated. In the 5-prompt setting, the conditional prefix consists of the instruction, 5 source target sentence pairs, and the source sentence to be translated. Data We used the first 10,000 lines of en → fr from WMT06 Europarl (Koehn, 2005) for training.[7] To test the generalisability of trained attention head gates, we use a different test domain, FLORES (Goyal et al., 2021) to reflect the scarcity of in-domain data. We also test an additional language direction en→pt in FLORES to see if training can generalise across languages. Data Training Details We train using Adam Optimizer (β1 = 0.9, β2 = 0.999) with a batch size of 32, and learning rate of 0.001. We use a large early stopping patience of 10 and threshold of 0.01, and train for up to 100 epochs. This is due to the nature of L0 training; we do not expect performance to improve over many iterations and would like the attention gates to keep training as long as there is no large loss in performance. We initialise attention head gates to be 1 instead of random or 0.5 as this leads to much faster convergence and better performance. For the regularisation weight λ, we search over a hyperparameter set of {0.1, 0.01, 0.001, 0.0001} and found 0.01 performs best on the validation set. Training Details A.7. L0 head masking experiments. Additional experiments on L0 head masking in the fr→ en and es→ en direction. A.8. Generalisability of L0 gate training We experiment with 0-prompts and 5-prompts in training and using λ = 0 (no regularisation) and λ = 0.01. L0 training for the 0-prompts shows some gains for the 0-prompts test case, and with no loss on the 5-prompts test case (Table 2). Notably, this persists in en→pt, a different language direction from training. The robustness of translation performance under multiple testing conditions (number of prompts, datasets, language directions) gives some confidence that the trained discrete attention head gates from L0 support a general ability to translate (Table 2). In contrast, the soft attention head gates without regularisation (λ = 0) appear to overfit as they perform well on some conditions but deteriorate in others. We observe that 0-prompt training for L0(λ = 0.01) also A.9. Qualitative Analysis of Layer-wise Masking BLOOM is observed to be more robust to masking of layers; suggesting that task location is more distributed. BLOOM For the 5-prompt setting, the performance only decreases very slightly. For the 0-prompt setting, we observe that similar to GPTNEO, performance drops when masking out the middle layers. At the aggregate level, BLOOM appears to still be translating (> 0 BLEU) even when layers are masked. However we observe that the drop in performance is due to around 40 to 50% of the test sentences scoring < 5 BLEU points. There is a clear failure to translate, not simply producing poorer translations. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv [7] Data available from https://www.statmt.org/europarl/

Where does In-context Translation Happen in Large Language Models: Appendix

Why 1-Bit Transformers Will Change the World

This paper is available on arxiv under CC 4.0 license. . Authors: Gemini Team, Google Table of Links Abstract and Introduction Model Architecture Training Infrastructure Training Dataset Evaluation Responsible Deployment Discussion and Conclusion, References Contributions and Acknowledgments Appendix 9. Appendix 9.1. Chain-of-Thought Comparisons on MMLU benchmark We contrast several chain-of-thought approaches on MMLU and discuss their results in this section. We proposed a new approach where model produces k chain-of-thought samples, selects the majority vote if the model is confident above a threshold, and otherwise defers to the greedy sample choice. The thresholds are optimized for each model based on their validation split performance. The proposed approach is referred to as uncertainty-routed chain-of-thought. The intuition behind this approach is that chain-of-thought samples might degrade performance compared to the maximum-likelihood decision when the model is demonstrably inconsistent. We compare the gains from the proposed approach on both Gemini Ultra and GPT-4 in Figure 7. We find that Gemini Ultra benefits more from this approach compared to using only chain-of-thought samples. GPT-4’s performance improves from 84.2% with greedy sampling to 87.3% with uncertainty-routed chain-of-thought approach with 32 samples, but it already achieves these gains from using 32 chain-of-thought samples. In contrast, Gemini Ultra improves its performance significantly from 84.0% with greedy sampling to 90.0% with uncertainty-routed chain-of-thought approach with 32 samples while it marginally improves to 85.0% with the use of 32 chain-of-thought samples only. 9.2. Capabilities and Benchmarking Tasks We use more than 50 benchmarks as a holistic harness to evaluate the Gemini models across text, image, audio and video. We provide a detailed list of benchmarking tasks for six different capabilities in text understanding and generation: factuality, long context, math/science, reasoning, summarization, and multilinguality. We also enumerate the benchmarks used for image understanding, video understanding, and audio understanding tasks. • : We use 5 benchmarks: BoolQ (Clark et al., 2019), NaturalQuestions-Closed (Kwiatkowski et al., 2019), NaturalQuestions-Retrieved (Kwiatkowski et al., 2019), RealtimeQA (Kasai et al., 2022), TydiQA-noContext and TydiQA-goldP (Clark et al., 2020). Factuality We use 6 benchmarks: NarrativeQA (Kočiský et al., 2018), Scrolls-Qasper, Scrolls-Quality (Shaham et al., 2022), XLsum (En), XLSum (non-English languages) (Hasan et al., 2021), and one other internal benchmark. • Long Context: We use 8 benchmarks: GSM8k (with CoT) (Cobbe et al., 2021), Hendryck’s MATH pass@1 (Hendrycks et al., 2021b), MMLU (Hendrycks et al., 2021a), Math-StackExchange, Math-AMC 2022-2023 problems, and three other internal benchmarks. • Math/Science: • : We use 7 benchmarks: BigBench Hard (with CoT) (Srivastava et al., 2022; Suzgun et al., 2022), CLRS (Veličković et al., 2022), Proof Writer (Tafjord et al., 2020), Reasoning-Fermi problems (Kalyan et al., 2021), Lambada (Paperno et al., 2016), HellaSwag (Zellers et al., 2019), DROP (Dua et al., 2019). Reasoning • : We use 5 benchmarks: XL Sum (English), XL Sum (non-English languages) (Hasan et al., 2021), WikiLingua (non-English languages), WikiLingua (English) (Ladhak et al., 2020), XSum (Narayan et al., 2018). Summarization • : We use 10 benchmarks: XLSum (Non-English languages) (Hasan et al., 2021), WMT22 (Kocmi et al., 2022), WMT23 (Tom et al., 2023), FRMT (Riley et al., 2023), WikiLingua (Non-English languages) (Ladhak et al., 2020), TydiQA (no context), TydiQA (GoldP) (Clark et al., 2020), MGSM (Shi et al., 2023), translated MMLU (Hendrycks et al., 2021a), NTREX (Federmann et al., 2022), FLORES-200 (Team et al., 2022). Multilinguality We use 9 benchmarks for image understanding: MMMU (Yue et al., 2023), TextVQA (Singh et al., 2019), DocVQA (Mathew et al., 2021), ChartQA (Masry et al., 2022), InfographicVQA (Mathew et al., 2022), MathVista (Lu et al., 2023), AI2D (Kembhavi et al., 2016), VQAv2 (Goyal et al., 2017), XM3600 (Thapliyal et al., 2022) for multi-lingual image understanding, and 6 benchmarks for video understanding: VATEX (Wang et al., 2019) for captioning in two different languages, YouCook2 (Zhou et al., 2018), NextQA (Xiao et al., 2021), ActivityNet-QA (Yu et al., 2019), and Perception Test MCQA (Pătrăucean et al., 2023). • Image and Video: • : We use 5 benchmarks including automatic speech recognition (ASR) tasks such as FLEURS (Conneau et al., 2023), VoxPopuli (Wang et al., 2021), Multi-lingual Librispeech (Pratap et al., 2020), and automatic speech translation task such as CoVoST 2 (Wang et al., 2020). Audio 9.3. Qualitative Examples This section shows sample qualitative examples from prompting the Gemini Ultra model. Some illustrative examples of multimodal reasoning for image understanding tasks over charts, natural images and memes are shown in Figures 8, 9, 11, 13, 14, and 15. Figure 10 shows an example of image generation capabilities of Gemini Ultra where the user generates an interleaved sequence of image and text to design a blog post. Beyond English, Figure 16 shows model’s capability to understand images in a multilingual setting. Gemini models also show strong performance on multimodal image understanding and reasoning in mathematics, as shown in Figures 12, 18 and 19. Figure 20 is an example of complex multimodal reasoning demonstrating how the model composes complex image understanding, code generation, and instruction following capabilities for a given user task. In Figure 17, we see another example of the model being able to generate working code and follow complex user instructions. Finally, Figure 21 shows an example of Gemini Ultra’s capability of understanding video by reasoning over temporally connected set of frames. 9.3.1. Chart understanding and reasoning over data 9.3.2. Multimodal question answering 9.3.3. Interleaved image and text generation 9.3.4. Image understanding and reasoning 9.3.5. Geometrical reasoning 9.3.6. Information seeking about objects 9.3.7. Multimodal reasoning based on visual cues 9.3.8. Multimodal humor understanding 9.4. Commonsense reasoning in a multilingual setting 9.4.1. Reasoning and code generation 9.4.2. Mathematics: Calculus 9.5. Multi-step reasoning and mathematics 9.5.1. Complex image understanding, code generation, and instruction following 9.5.2. Video understanding and reasoning

Gemini - A Family of Highly Capable Multimodal Models: Appendix

Gemini - A Family of Highly Capable Multimodal Models: Discussion and Conclusion, References

Gemini - A Family of Highly Capable
Multimodal Models: Evaluation

Operation Weather.

Breaking Barriers: Decentralized Mobile Networks As A Solution To Global Inequality In a world where mobile connectivity is as essential as electricity, it's baffling to realize that not all regions enjoy equal access to this vital infrastructure. Unveiling the crux of this issue, let's explore how decentralization can revolutionize the global telecommunication industry and bring about economic equity. Bridging the Gap: Decentralized Mobile Networks and the Promise of Global Connectivity Despite the leaps and bounds in mobile technology, there's an undeniable gap that separates the digitally connected world from regions that are still grappling with limited or nonexistent mobile network access. This disparity is not just a case of technological haves and have-nots; it's a severe impediment to the affected regions' full participation in the global economy. On the other hand, existing networks are primarily centralized systems where a few major players hold sway over infrastructure, pricing, and data management. Users and small-scale operators have limited economic incentives or power to influence these network environments, leading to a system that often fails to meet their unique needs. A World Without Digital Divide: How Decentralization is Making it Happen The root of the problem lies in the centralization of the mobile networks. The traditional telecom model, dominated by a small number of companies, restricts competition and innovation while often overlooking underserved regions due to lower profitability. Moreover, centralization gives network providers control over user data, leading to privacy concerns. They determine the costs, data caps, and network rules, making the consumer a passive participant rather than an active stakeholder. Decentralization: The Game Changer Enter decentralized mobile networks. By transferring control and ownership from corporations to individual users and operators, decentralized systems could tackle many of the problems that plague our current mobile network infrastructure. Firstly, these systems democratize network access by enabling individuals and businesses to set up and operate nodes, effectively bringing connectivity to underserved areas. Instead of relying on a centralized company's profitability analysis, networks grow organically based on community needs, bridging the digital divide. Secondly, users are financially incentivized to contribute to the network, such as running a node or sharing their resources. This economic motivation could drive network expansion and encourage active participation in the mobile ecosystem, fostering competition and innovation. Moreover, a decentralized mobile network is fundamentally designed to respect user data privacy, returning control of personal data to the user. The Great Digital Divide: A Cool Solution for an Uncool Problem Imagine a world where every person, no matter where they live, has a superfast internet connection right on their smartphone. Sounds cool, right? But here's a bummer - we're not there yet. But don't worry, because World Mobile has a solution that's just as cool. : Vested Interest Disclosure This author is an independent contributor publishing via our . Be it through direct compensation, media partnerships, or networking, the author has a vested interest in the company/ies mentioned in this story. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR brand-as-author program Here's the thing, even though we have crazy fast 5G networks and smartphones that seem straight out of a sci-fi movie, not everyone around the globe gets to enjoy this. Some regions, especially remote or poorer areas, don't have a good mobile network. This means they can't enjoy all the benefits that come with being connected to the world, like online education, e-commerce, or just chilling with Netflix. Plus, the big companies that provide these mobile services usually control everything, from how fast your internet is to how much you need to pay for it. It's like being in a race where the track, the rules, and even the rewards are controlled by a few people. Not exactly fair, huh? But here's the cool part. is trying to change this game by creating a mobile ecosystem where everyone can participate, and the best part? They can even earn money from it! This is what we call a decentralized system - where power doesn't stay with a few big companies but is shared among the users. World Mobile Imagine having your own tiny piece of the internet that you can control, and even better, get paid for helping it run smoothly. This is done using something called World Mobile Token (WMT), which is like digital money or points that you can earn. To make this even more exciting and to make sure that everyone who contributes is rewarded, has come up with a great idea. They're buying back these WMT tokens from people. It's like a thank-you gift to those who help in making this cool new mobile world possible, and it also makes the WMT more valuable. World Mobile So, while it's a bummer that not everyone gets to enjoy the digital wonders of the 21st century, there's hope. With the power of decentralization, World Mobile is paving the way for a future where everyone, everywhere, can enjoy the benefits of mobile connectivity - and even get rewarded for it! Now, how cool is that? Don’t forget to like and share the story!

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