outputs/task_1738560819_What is distillation in LLM and how did deepseek r1 was trained on the same and why the current panic in AI industry led by deepseek r1 training cost is flawed.json

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        "output": "\ud83d\udcc3 Source: https://abhishek-maheshwarappa.medium.com/deepseek-r1-revolutionizing-reasoning-with-reinforcement-learning-and-distillation-24f9e1877627\nTitle: DeepSeek-R1: Revolutionizing Reasoning with Reinforcement Learning and Distillation | by Abhishek Maheshwarappa | Jan, 2025 | Medium\nContent: requiring RL training. This approach democratizes access to advanced reasoning capabilities for research and industry applications.DeepSeek-R1 EvaluationDeepSeek-R1\u2019s performance is benchmarked against industry-leading models, showcasing:Reasoning Tasks: Achieving superior accuracy in benchmarks like AIME 2024 and MATH-500.General QA: Outperforming competitors like GPT-4o and Claude in creative writing and instruction-following tasks.Long-Context Understanding: Excelling in tasks requiring extended reasoning, such as AlpacaEval and ArenaHard.These results highlight the effectiveness of RL in improving reasoning and generalization across diverse tasks.Distillation vs. Reinforcement LearningDistillation vs. RLAdvantages of Distillation:Distillation achieves better performance for smaller models with less computational effort compared to RL.DeepSeek-R1 distilled models outperform traditional RL-trained compact architectures, such as QwQ-32B.Challenges with RL:RL for smaller models is\n\nSource: https://abhishek-maheshwarappa.medium.com/deepseek-r1-revolutionizing-reasoning-with-reinforcement-learning-and-distillation-24f9e1877627\nTitle: DeepSeek-R1: Revolutionizing Reasoning with Reinforcement Learning and Distillation | by Abhishek Maheshwarappa | Jan, 2025 | Medium\nContent: requiring RL training. This approach democratizes access to advanced reasoning capabilities for research and industry applications.DeepSeek-R1 EvaluationDeepSeek-R1\u2019s performance is benchmarked against industry-leading models, showcasing:Reasoning Tasks: Achieving superior accuracy in benchmarks like AIME 2024 and MATH-500.General QA: Outperforming competitors like GPT-4o and Claude in creative writing and instruction-following tasks.Long-Context Understanding: Excelling in tasks requiring extended reasoning, such as AlpacaEval and ArenaHard.These results highlight the effectiveness of RL in improving reasoning and generalization across diverse tasks.Distillation vs. Reinforcement LearningDistillation vs. RLAdvantages of Distillation:Distillation achieves better performance for smaller models with less computational effort compared to RL.DeepSeek-R1 distilled models outperform traditional RL-trained compact architectures, such as QwQ-32B.Challenges with RL:RL for smaller models is\n\nSource: https://abhishek-maheshwarappa.medium.com/deepseek-r1-revolutionizing-reasoning-with-reinforcement-learning-and-distillation-24f9e1877627\nTitle: DeepSeek-R1: Revolutionizing Reasoning with Reinforcement Learning and Distillation | by Abhishek Maheshwarappa | Jan, 2025 | Medium\nContent: requiring RL training. This approach democratizes access to advanced reasoning capabilities for research and industry applications.DeepSeek-R1 EvaluationDeepSeek-R1\u2019s performance is benchmarked against industry-leading models, showcasing:Reasoning Tasks: Achieving superior accuracy in benchmarks like AIME 2024 and MATH-500.General QA: Outperforming competitors like GPT-4o and Claude in creative writing and instruction-following tasks.Long-Context Understanding: Excelling in tasks requiring extended reasoning, such as AlpacaEval and ArenaHard.These results highlight the effectiveness of RL in improving reasoning and generalization across diverse tasks.Distillation vs. Reinforcement LearningDistillation vs. RLAdvantages of Distillation:Distillation achieves better performance for smaller models with less computational effort compared to RL.DeepSeek-R1 distilled models outperform traditional RL-trained compact architectures, such as QwQ-32B.Challenges with RL:RL for smaller models is\n\nSource: https://medium.com/@deepankar080892/deepseek-r1-redefining-open-source-reasoning-in-llms-89f09250afed\nTitle: DeepSeek-R1: Transforming AI Reasoning with Reinforcement Learning and Efficient Distillation | by Deepankar Singh | Feb, 2025 | Medium\nContent: DeepSeek-R1: Transforming AI Reasoning with Reinforcement Learning and Efficient DistillationDeepankar Singh\u00b7Follow29 min read\u00b71 day ago--Share\nDeepSeek-R1: Transforming AI Reasoning with Reinforcement Learning and Efficient Distillation\nDeepankar Singh\u00b7Follow29 min read\u00b71 day ago--Share\nDeepankar Singh\u00b7Follow29 min read\u00b71 day ago--Share\nDeepankar Singh\u00b7Follow29 min read\u00b71 day ago--Share\nDeepankar Singh\u00b7Follow29 min read\u00b71 day ago\nDeepankar Singh\u00b7Follow29 min read\u00b71 day ago\n29 min read\u00b71 day ago\n29 min read\u00b71 day ago\n29 min read\u00b71 day ago\n29 min read\u00b71 day ago\n29 min read\u00b71 day ago\n29 min read\nDeepSeek-R1: Redefining Open-Source Reasoning in LLMs\n\nSource: https://abhishek-maheshwarappa.medium.com/deepseek-r1-revolutionizing-reasoning-with-reinforcement-learning-and-distillation-24f9e1877627\nTitle: DeepSeek-R1: Revolutionizing Reasoning with Reinforcement Learning and Distillation | by Abhishek Maheshwarappa | Jan, 2025 | Medium\nContent: This approach democratizes access to advanced reasoning capabilities for research and industry applications.DeepSeek-R1 EvaluationDeepSeek-R1\u2019s performance is benchmarked against industry-leading models, showcasing:Reasoning Tasks: Achieving superior accuracy in benchmarks like AIME 2024 and MATH-500.General QA: Outperforming competitors like GPT-4o and Claude in creative writing and instruction-following tasks.Long-Context Understanding: Excelling in tasks requiring extended reasoning, such as AlpacaEval and ArenaHard.These results highlight the effectiveness of RL in improving reasoning and generalization across diverse tasks.Distillation vs. Reinforcement LearningDistillation vs. RLAdvantages of Distillation:Distillation achieves better performance for smaller models with less computational effort compared to RL.DeepSeek-R1 distilled models outperform traditional RL-trained compact architectures, such as QwQ-32B.Challenges with RL:RL for smaller models is computationally intensive\n\nSource: https://abhishek-maheshwarappa.medium.com/deepseek-r1-revolutionizing-reasoning-with-reinforcement-learning-and-distillation-24f9e1877627\nTitle: DeepSeek-R1: Revolutionizing Reasoning with Reinforcement Learning and Distillation | by Abhishek Maheshwarappa | Jan, 2025 | Medium\nContent: This approach democratizes access to advanced reasoning capabilities for research and industry applications.DeepSeek-R1 EvaluationDeepSeek-R1\u2019s performance is benchmarked against industry-leading models, showcasing:Reasoning Tasks: Achieving superior accuracy in benchmarks like AIME 2024 and MATH-500.General QA: Outperforming competitors like GPT-4o and Claude in creative writing and instruction-following tasks.Long-Context Understanding: Excelling in tasks requiring extended reasoning, such as AlpacaEval and ArenaHard.These results highlight the effectiveness of RL in improving reasoning and generalization across diverse tasks.Distillation vs. Reinforcement LearningDistillation vs. RLAdvantages of Distillation:Distillation achieves better performance for smaller models with less computational effort compared to RL.DeepSeek-R1 distilled models outperform traditional RL-trained compact architectures, such as QwQ-32B.Challenges with RL:RL for smaller models is computationally intensive\n\nSource: https://abhishek-maheshwarappa.medium.com/deepseek-r1-revolutionizing-reasoning-with-reinforcement-learning-and-distillation-24f9e1877627\nTitle: DeepSeek-R1: Revolutionizing Reasoning with Reinforcement Learning and Distillation | by Abhishek Maheshwarappa | Jan, 2025 | Medium\nContent: This approach democratizes access to advanced reasoning capabilities for research and industry applications.DeepSeek-R1 EvaluationDeepSeek-R1\u2019s performance is benchmarked against industry-leading models, showcasing:Reasoning Tasks: Achieving superior accuracy in benchmarks like AIME 2024 and MATH-500.General QA: Outperforming competitors like GPT-4o and Claude in creative writing and instruction-following tasks.Long-Context Understanding: Excelling in tasks requiring extended reasoning, such as AlpacaEval and ArenaHard.These results highlight the effectiveness of RL in improving reasoning and generalization across diverse tasks.Distillation vs. Reinforcement LearningDistillation vs. RLAdvantages of Distillation:Distillation achieves better performance for smaller models with less computational effort compared to RL.DeepSeek-R1 distilled models outperform traditional RL-trained compact architectures, such as QwQ-32B.Challenges with RL:RL for smaller models is computationally intensive\n\nSource: https://abhishek-maheshwarappa.medium.com/deepseek-r1-revolutionizing-reasoning-with-reinforcement-learning-and-distillation-24f9e1877627\nTitle: DeepSeek-R1: Revolutionizing Reasoning with Reinforcement Learning and Distillation | by Abhishek Maheshwarappa | Jan, 2025 | Medium\nContent: This approach democratizes access to advanced reasoning capabilities for research and industry applications.DeepSeek-R1 EvaluationDeepSeek-R1\u2019s performance is benchmarked against industry-leading models, showcasing:Reasoning Tasks: Achieving superior accuracy in benchmarks like AIME 2024 and MATH-500.General QA: Outperforming competitors like GPT-4o and Claude in creative writing and instruction-following tasks.Long-Context Understanding: Excelling in tasks requiring extended reasoning, such as AlpacaEval and ArenaHard.These results highlight the effectiveness of RL in improving reasoning and generalization across diverse tasks.Distillation vs. Reinforcement LearningDistillation vs. RLAdvantages of Distillation:Distillation achieves better performance for smaller models with less computational effort compared to RL.DeepSeek-R1 distilled models outperform traditional RL-trained compact architectures, such as QwQ-32B.Challenges with RL:RL for smaller models is computationally intensive\n\nSource: https://abhishek-maheshwarappa.medium.com/deepseek-r1-revolutionizing-reasoning-with-reinforcement-learning-and-distillation-24f9e1877627\nTitle: DeepSeek-R1: Revolutionizing Reasoning with Reinforcement Learning and Distillation | by Abhishek Maheshwarappa | Jan, 2025 | Medium\nContent: This approach democratizes access to advanced reasoning capabilities for research and industry applications.DeepSeek-R1 EvaluationDeepSeek-R1\u2019s performance is benchmarked against industry-leading models, showcasing:Reasoning Tasks: Achieving superior accuracy in benchmarks like AIME 2024 and MATH-500.General QA: Outperforming competitors like GPT-4o and Claude in creative writing and instruction-following tasks.Long-Context Understanding: Excelling in tasks requiring extended reasoning, such as AlpacaEval and ArenaHard.These results highlight the effectiveness of RL in improving reasoning and generalization across diverse tasks.Distillation vs. Reinforcement LearningDistillation vs. RLAdvantages of Distillation:Distillation achieves better performance for smaller models with less computational effort compared to RL.DeepSeek-R1 distilled models outperform traditional RL-trained compact architectures, such as QwQ-32B.Challenges with RL:RL for smaller models is computationally intensive\n\nSource: https://abhishek-maheshwarappa.medium.com/deepseek-r1-revolutionizing-reasoning-with-reinforcement-learning-and-distillation-24f9e1877627\nTitle: DeepSeek-R1: Revolutionizing Reasoning with Reinforcement Learning and Distillation | by Abhishek Maheshwarappa | Jan, 2025 | Medium\nContent: This approach democratizes access to advanced reasoning capabilities for research and industry applications.DeepSeek-R1 EvaluationDeepSeek-R1\u2019s performance is benchmarked against industry-leading models, showcasing:Reasoning Tasks: Achieving superior accuracy in benchmarks like AIME 2024 and MATH-500.General QA: Outperforming competitors like GPT-4o and Claude in creative writing and instruction-following tasks.Long-Context Understanding: Excelling in tasks requiring extended reasoning, such as AlpacaEval and ArenaHard.These results highlight the effectiveness of RL in improving reasoning and generalization across diverse tasks.Distillation vs. Reinforcement LearningDistillation vs. RLAdvantages of Distillation:Distillation achieves better performance for smaller models with less computational effort compared to RL.DeepSeek-R1 distilled models outperform traditional RL-trained compact architectures, such as QwQ-32B.Challenges with RL:RL for smaller models is computationally intensive\n",
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        "output": "\ud83d\udcc3 Source: https://medium.com/@nrgore1/deepseek-vs-openai-a-comparative-analysis-of-llm-development-and-cost-efficiency-a8534f32c9a8\nTitle: DeepSeek Vs OpenAI: A comparative analysis of LLM development and cost efficiency | by Narendra Gore | Jan, 2025 | Medium\nContent: Data Optimization: Instead of training on an exhaustive range of general-purpose data, DeepSeek-R1 focuses on carefully curated domain-specific datasets, achieving faster convergence and fewer training epochs.\nSmaller Teams, Bigger Impact: DeepSeek maintains a smaller team of AI engineers but employs automation tools for model testing and refinement, reducing human labor costs.\nFor instance, while OpenAI reportedly spent upwards of $100 million on training GPT-4, DeepSeek achieved similar outcomes for DeepSeek-R1 with a budget closer to $15 million. This stark difference is a testament to their efficiency-driven philosophy.\nComparative Cost Analysis\n\nSource: https://medium.com/@nrgore1/deepseek-vs-openai-a-comparative-analysis-of-llm-development-and-cost-efficiency-a8534f32c9a8\nTitle: DeepSeek Vs OpenAI: A comparative analysis of LLM development and cost efficiency | by Narendra Gore | Jan, 2025 | Medium\nContent: tasks.Cost Efficiency: How Does DeepSeek Keep Costs Low?DeepSeek has revolutionized cost management by addressing key areas where traditional development incurs significant expenditure:Energy Efficiency: Their proprietary accelerators consume up to 50% less power per training iteration compared to OpenAI\u2019s reliance on generic GPU clusters.Data Optimization: Instead of training on an exhaustive range of general-purpose data, DeepSeek-R1 focuses on carefully curated domain-specific datasets, achieving faster convergence and fewer training epochs.Smaller Teams, Bigger Impact: DeepSeek maintains a smaller team of AI engineers but employs automation tools for model testing and refinement, reducing human labor costs.For instance, while OpenAI reportedly spent upwards of $100 million on training GPT-4, DeepSeek achieved similar outcomes for DeepSeek-R1 with a budget closer to $15 million. This stark difference is a testament to their efficiency-driven philosophy.Comparative Cost\n\nSource: https://medium.com/@nrgore1/deepseek-vs-openai-a-comparative-analysis-of-llm-development-and-cost-efficiency-a8534f32c9a8\nTitle: DeepSeek Vs OpenAI: A comparative analysis of LLM development and cost efficiency | by Narendra Gore | Jan, 2025 | Medium\nContent: tasks.Cost Efficiency: How Does DeepSeek Keep Costs Low?DeepSeek has revolutionized cost management by addressing key areas where traditional development incurs significant expenditure:Energy Efficiency: Their proprietary accelerators consume up to 50% less power per training iteration compared to OpenAI\u2019s reliance on generic GPU clusters.Data Optimization: Instead of training on an exhaustive range of general-purpose data, DeepSeek-R1 focuses on carefully curated domain-specific datasets, achieving faster convergence and fewer training epochs.Smaller Teams, Bigger Impact: DeepSeek maintains a smaller team of AI engineers but employs automation tools for model testing and refinement, reducing human labor costs.For instance, while OpenAI reportedly spent upwards of $100 million on training GPT-4, DeepSeek achieved similar outcomes for DeepSeek-R1 with a budget closer to $15 million. This stark difference is a testament to their efficiency-driven philosophy.Comparative Cost\n\nSource: https://medium.com/@nrgore1/deepseek-vs-openai-a-comparative-analysis-of-llm-development-and-cost-efficiency-a8534f32c9a8\nTitle: DeepSeek Vs OpenAI: A comparative analysis of LLM development and cost efficiency | by Narendra Gore | Jan, 2025 | Medium\nContent: tasks.Cost Efficiency: How Does DeepSeek Keep Costs Low?DeepSeek has revolutionized cost management by addressing key areas where traditional development incurs significant expenditure:Energy Efficiency: Their proprietary accelerators consume up to 50% less power per training iteration compared to OpenAI\u2019s reliance on generic GPU clusters.Data Optimization: Instead of training on an exhaustive range of general-purpose data, DeepSeek-R1 focuses on carefully curated domain-specific datasets, achieving faster convergence and fewer training epochs.Smaller Teams, Bigger Impact: DeepSeek maintains a smaller team of AI engineers but employs automation tools for model testing and refinement, reducing human labor costs.For instance, while OpenAI reportedly spent upwards of $100 million on training GPT-4, DeepSeek achieved similar outcomes for DeepSeek-R1 with a budget closer to $15 million. This stark difference is a testament to their efficiency-driven philosophy.Comparative Cost\n\nSource: https://medium.com/@nrgore1/deepseek-vs-openai-a-comparative-analysis-of-llm-development-and-cost-efficiency-a8534f32c9a8\nTitle: DeepSeek Vs OpenAI: A comparative analysis of LLM development and cost efficiency | by Narendra Gore | Jan, 2025 | Medium\nContent: tasks.Cost Efficiency: How Does DeepSeek Keep Costs Low?DeepSeek has revolutionized cost management by addressing key areas where traditional development incurs significant expenditure:Energy Efficiency: Their proprietary accelerators consume up to 50% less power per training iteration compared to OpenAI\u2019s reliance on generic GPU clusters.Data Optimization: Instead of training on an exhaustive range of general-purpose data, DeepSeek-R1 focuses on carefully curated domain-specific datasets, achieving faster convergence and fewer training epochs.Smaller Teams, Bigger Impact: DeepSeek maintains a smaller team of AI engineers but employs automation tools for model testing and refinement, reducing human labor costs.For instance, while OpenAI reportedly spent upwards of $100 million on training GPT-4, DeepSeek achieved similar outcomes for DeepSeek-R1 with a budget closer to $15 million. This stark difference is a testament to their efficiency-driven philosophy.Comparative Cost\n\nSource: https://medium.com/@nrgore1/deepseek-vs-openai-a-comparative-analysis-of-llm-development-and-cost-efficiency-a8534f32c9a8\nTitle: DeepSeek Vs OpenAI: A comparative analysis of LLM development and cost efficiency | by Narendra Gore | Jan, 2025 | Medium\nContent: tasks.Cost Efficiency: How Does DeepSeek Keep Costs Low?DeepSeek has revolutionized cost management by addressing key areas where traditional development incurs significant expenditure:Energy Efficiency: Their proprietary accelerators consume up to 50% less power per training iteration compared to OpenAI\u2019s reliance on generic GPU clusters.Data Optimization: Instead of training on an exhaustive range of general-purpose data, DeepSeek-R1 focuses on carefully curated domain-specific datasets, achieving faster convergence and fewer training epochs.Smaller Teams, Bigger Impact: DeepSeek maintains a smaller team of AI engineers but employs automation tools for model testing and refinement, reducing human labor costs.For instance, while OpenAI reportedly spent upwards of $100 million on training GPT-4, DeepSeek achieved similar outcomes for DeepSeek-R1 with a budget closer to $15 million. This stark difference is a testament to their efficiency-driven philosophy.Comparative Cost\n\nSource: https://medium.com/@nrgore1/deepseek-vs-openai-a-comparative-analysis-of-llm-development-and-cost-efficiency-a8534f32c9a8\nTitle: DeepSeek Vs OpenAI: A comparative analysis of LLM development and cost efficiency | by Narendra Gore | Jan, 2025 | Medium\nContent: tasks.Cost Efficiency: How Does DeepSeek Keep Costs Low?DeepSeek has revolutionized cost management by addressing key areas where traditional development incurs significant expenditure:Energy Efficiency: Their proprietary accelerators consume up to 50% less power per training iteration compared to OpenAI\u2019s reliance on generic GPU clusters.Data Optimization: Instead of training on an exhaustive range of general-purpose data, DeepSeek-R1 focuses on carefully curated domain-specific datasets, achieving faster convergence and fewer training epochs.Smaller Teams, Bigger Impact: DeepSeek maintains a smaller team of AI engineers but employs automation tools for model testing and refinement, reducing human labor costs.For instance, while OpenAI reportedly spent upwards of $100 million on training GPT-4, DeepSeek achieved similar outcomes for DeepSeek-R1 with a budget closer to $15 million. This stark difference is a testament to their efficiency-driven philosophy.Comparative Cost\n\nSource: https://medium.com/@nrgore1/deepseek-vs-openai-a-comparative-analysis-of-llm-development-and-cost-efficiency-a8534f32c9a8\nTitle: DeepSeek Vs OpenAI: A comparative analysis of LLM development and cost efficiency | by Narendra Gore | Jan, 2025 | Medium\nContent: tasks.Cost Efficiency: How Does DeepSeek Keep Costs Low?DeepSeek has revolutionized cost management by addressing key areas where traditional development incurs significant expenditure:Energy Efficiency: Their proprietary accelerators consume up to 50% less power per training iteration compared to OpenAI\u2019s reliance on generic GPU clusters.Data Optimization: Instead of training on an exhaustive range of general-purpose data, DeepSeek-R1 focuses on carefully curated domain-specific datasets, achieving faster convergence and fewer training epochs.Smaller Teams, Bigger Impact: DeepSeek maintains a smaller team of AI engineers but employs automation tools for model testing and refinement, reducing human labor costs.For instance, while OpenAI reportedly spent upwards of $100 million on training GPT-4, DeepSeek achieved similar outcomes for DeepSeek-R1 with a budget closer to $15 million. This stark difference is a testament to their efficiency-driven philosophy.Comparative Cost\n\nSource: https://medium.com/@nrgore1/deepseek-vs-openai-a-comparative-analysis-of-llm-development-and-cost-efficiency-a8534f32c9a8\nTitle: DeepSeek Vs OpenAI: A comparative analysis of LLM development and cost efficiency | by Narendra Gore | Jan, 2025 | Medium\nContent: tasks.Cost Efficiency: How Does DeepSeek Keep Costs Low?DeepSeek has revolutionized cost management by addressing key areas where traditional development incurs significant expenditure:Energy Efficiency: Their proprietary accelerators consume up to 50% less power per training iteration compared to OpenAI\u2019s reliance on generic GPU clusters.Data Optimization: Instead of training on an exhaustive range of general-purpose data, DeepSeek-R1 focuses on carefully curated domain-specific datasets, achieving faster convergence and fewer training epochs.Smaller Teams, Bigger Impact: DeepSeek maintains a smaller team of AI engineers but employs automation tools for model testing and refinement, reducing human labor costs.For instance, while OpenAI reportedly spent upwards of $100 million on training GPT-4, DeepSeek achieved similar outcomes for DeepSeek-R1 with a budget closer to $15 million. This stark difference is a testament to their efficiency-driven philosophy.Comparative Cost\n\nSource: https://medium.com/@nrgore1/deepseek-vs-openai-a-comparative-analysis-of-llm-development-and-cost-efficiency-a8534f32c9a8\nTitle: DeepSeek Vs OpenAI: A comparative analysis of LLM development and cost efficiency | by Narendra Gore | Jan, 2025 | Medium\nContent: DeepSeek-R1 shines in domain-specific and instruction-following tasks.Cost Efficiency: How Does DeepSeek Keep Costs Low?DeepSeek has revolutionized cost management by addressing key areas where traditional development incurs significant expenditure:Energy Efficiency: Their proprietary accelerators consume up to 50% less power per training iteration compared to OpenAI\u2019s reliance on generic GPU clusters.Data Optimization: Instead of training on an exhaustive range of general-purpose data, DeepSeek-R1 focuses on carefully curated domain-specific datasets, achieving faster convergence and fewer training epochs.Smaller Teams, Bigger Impact: DeepSeek maintains a smaller team of AI engineers but employs automation tools for model testing and refinement, reducing human labor costs.For instance, while OpenAI reportedly spent upwards of $100 million on training GPT-4, DeepSeek achieved similar outcomes for DeepSeek-R1 with a budget closer to $15 million. This stark difference is a testament to\n",
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        "output": "\ud83d\udcc3 Source: https://time.com/7211646/is-deepseek-panic-overblown/\nTitle: Is the DeepSeek Panic Overblown?  | TIME\nContent: of claims about the low cost of training its models, tech CEOs cited reports that DeepSeek actually had a stash of 50,000 Nvidia chips, which it could not talk about due to U.S. export controls. Those chips would cost somewhere in the region of $1 billion. It is, however, true that DeepSeek\u2019s new R1 model is far cheaper for users to access than its competitor model OpenAI o1, with its model access fees around 30 times lower ($2.19 per million \u201ctokens,\u201d or segments of words outputted, versus $60). That sparked worries among some investors of a looming price war in the American AI industry, which could reduce expected returns on investment and make it more difficult for U.S. companies to raise funds required to build new data centers to fuel their AI models.Oliver Stephenson, associate director of AI and emerging tech policy at the Federation of American Scientists, says that people shouldn\u2019t draw conclusions from this price point. \u201cWhile DeepSeek has made genuine efficiency gains,\n\nSource: https://time.com/7211646/is-deepseek-panic-overblown/\nTitle: Is the DeepSeek Panic Overblown?  | TIME\nContent: To further obscure the picture, DeepSeek may also not be being entirely honest about its expenses. In the wake of claims about the low cost of training its models, tech CEOs cited reports that DeepSeek actually had a stash of 50,000 Nvidia chips, which it could not talk about due to U.S. export controls. Those chips would cost somewhere in the region of $1 billion.\nIt is, however, true that DeepSeek\u2019s new R1 model is far cheaper for users to access than its competitor model OpenAI o1, with its model access fees around 30 times lower ($2.19 per million \u201ctokens,\u201d or segments of words outputted, versus $60). That sparked worries among some investors of a looming price war in the American AI industry, which could reduce expected returns on investment and make it more difficult for U.S. companies to raise funds required to build new data centers to fuel their AI models.\n\nSource: https://time.com/7211646/is-deepseek-panic-overblown/\nTitle: Is the DeepSeek Panic Overblown?  | TIME\nContent: actually had a stash of 50,000 Nvidia chips, which it could not talk about due to U.S. export controls. Those chips would cost somewhere in the region of $1 billion. It is, however, true that DeepSeek\u2019s new R1 model is far cheaper for users to access than its competitor model OpenAI o1, with its model access fees around 30 times lower ($2.19 per million \u201ctokens,\u201d or segments of words outputted, versus $60). That sparked worries among some investors of a looming price war in the American AI industry, which could reduce expected returns on investment and make it more difficult for U.S. companies to raise funds required to build new data centers to fuel their AI models.Oliver Stephenson, associate director of AI and emerging tech policy at the Federation of American Scientists, says that people shouldn\u2019t draw conclusions from this price point. \u201cWhile DeepSeek has made genuine efficiency gains, their pricing could be an attention-grabbing strategy,\u201d he says. \u201cThey could be making a loss\n\nSource: https://time.com/7211646/is-deepseek-panic-overblown/\nTitle: Is the DeepSeek Panic Overblown?  | TIME\nContent: actually had a stash of 50,000 Nvidia chips, which it could not talk about due to U.S. export controls. Those chips would cost somewhere in the region of $1 billion. It is, however, true that DeepSeek\u2019s new R1 model is far cheaper for users to access than its competitor model OpenAI o1, with its model access fees around 30 times lower ($2.19 per million \u201ctokens,\u201d or segments of words outputted, versus $60). That sparked worries among some investors of a looming price war in the American AI industry, which could reduce expected returns on investment and make it more difficult for U.S. companies to raise funds required to build new data centers to fuel their AI models.Oliver Stephenson, associate director of AI and emerging tech policy at the Federation of American Scientists, says that people shouldn\u2019t draw conclusions from this price point. \u201cWhile DeepSeek has made genuine efficiency gains, their pricing could be an attention-grabbing strategy,\u201d he says. \u201cThey could be making a loss\n\nSource: https://time.com/7211646/is-deepseek-panic-overblown/\nTitle: Is the DeepSeek Panic Overblown?  | TIME\nContent: not talk about due to U.S. export controls. Those chips would cost somewhere in the region of $1 billion. It is, however, true that DeepSeek\u2019s new R1 model is far cheaper for users to access than its competitor model OpenAI o1, with its model access fees around 30 times lower ($2.19 per million \u201ctokens,\u201d or segments of words outputted, versus $60). That sparked worries among some investors of a looming price war in the American AI industry, which could reduce expected returns on investment and make it more difficult for U.S. companies to raise funds required to build new data centers to fuel their AI models.Oliver Stephenson, associate director of AI and emerging tech policy at the Federation of American Scientists, says that people shouldn\u2019t draw conclusions from this price point. \u201cWhile DeepSeek has made genuine efficiency gains, their pricing could be an attention-grabbing strategy,\u201d he says. \u201cThey could be making a loss on inference.\u201d (Inference is the running of an already-formed\n\nSource: https://time.com/7211646/is-deepseek-panic-overblown/\nTitle: Is the DeepSeek Panic Overblown?  | TIME\nContent: not talk about due to U.S. export controls. Those chips would cost somewhere in the region of $1 billion. It is, however, true that DeepSeek\u2019s new R1 model is far cheaper for users to access than its competitor model OpenAI o1, with its model access fees around 30 times lower ($2.19 per million \u201ctokens,\u201d or segments of words outputted, versus $60). That sparked worries among some investors of a looming price war in the American AI industry, which could reduce expected returns on investment and make it more difficult for U.S. companies to raise funds required to build new data centers to fuel their AI models.Oliver Stephenson, associate director of AI and emerging tech policy at the Federation of American Scientists, says that people shouldn\u2019t draw conclusions from this price point. \u201cWhile DeepSeek has made genuine efficiency gains, their pricing could be an attention-grabbing strategy,\u201d he says. \u201cThey could be making a loss on inference.\u201d (Inference is the running of an already-formed\n\nSource: https://time.com/7211646/is-deepseek-panic-overblown/\nTitle: Is the DeepSeek Panic Overblown?  | TIME\nContent: \u201cIt\u2019s not a leap forward on AI frontier capabilities,\u201d says Lennart Heim, an AI researcher at RAND. \u201cI think the market just got it wrong.\u201d\nRead More: What to Know About DeepSeek, the Chinese AI Company Causing Stock Market Chaos\nHere are several claims being widely circulated about DeepSeek\u2019s implications, and why scientists say they\u2019re incomplete or outright wrong.\nClaim: DeepSeek is much cheaper than other models.\nIn December, DeepSeek reported that its V3 model cost just $6 million to train. This figure seemed startlingly low compared to the more than $100 million that OpenAI said it spent training GPT-4, or the \u201cfew tens of millions\u201d that Anthropic spent training a recent version of its Claude model.\n\nSource: https://time.com/7211646/is-deepseek-panic-overblown/\nTitle: Is the DeepSeek Panic Overblown?  | TIME\nContent: it is not a massive technological breakthrough\u2014and that the American AI industry still has key advantages over China\u2019s.\u201cIt\u2019s not a leap forward on AI frontier capabilities,\u201d says Lennart Heim, an AI researcher at RAND. \u201cI think the market just got it wrong.\u201dRead More: What to Know About DeepSeek, the Chinese AI Company Causing Stock Market ChaosHere are several claims being widely circulated about DeepSeek\u2019s implications, and why scientists say they\u2019re incomplete or outright wrong. Claim: DeepSeek is much cheaper than other models. In December, DeepSeek reported that its V3 model cost just $6 million to train. This figure seemed startlingly low compared to the more than $100 million that OpenAI said it spent training GPT-4, or the \u201cfew tens of millions\u201d that Anthropic spent training a recent version of its Claude model.DeepSeek\u2019s lower price tag was thanks to some big efficiency gains that the company\u2019s researchers described in a paper accompanying their model\u2019s release. But were\n\nSource: https://time.com/7211646/is-deepseek-panic-overblown/\nTitle: Is the DeepSeek Panic Overblown?  | TIME\nContent: it is not a massive technological breakthrough\u2014and that the American AI industry still has key advantages over China\u2019s.\u201cIt\u2019s not a leap forward on AI frontier capabilities,\u201d says Lennart Heim, an AI researcher at RAND. \u201cI think the market just got it wrong.\u201dRead More: What to Know About DeepSeek, the Chinese AI Company Causing Stock Market ChaosHere are several claims being widely circulated about DeepSeek\u2019s implications, and why scientists say they\u2019re incomplete or outright wrong. Claim: DeepSeek is much cheaper than other models. In December, DeepSeek reported that its V3 model cost just $6 million to train. This figure seemed startlingly low compared to the more than $100 million that OpenAI said it spent training GPT-4, or the \u201cfew tens of millions\u201d that Anthropic spent training a recent version of its Claude model.DeepSeek\u2019s lower price tag was thanks to some big efficiency gains that the company\u2019s researchers described in a paper accompanying their model\u2019s release. But were\n\nSource: https://time.com/7211646/is-deepseek-panic-overblown/\nTitle: Is the DeepSeek Panic Overblown?  | TIME\nContent: They say that while DeepSeek does represent a genuine advancement in AI efficiency, it is not a massive technological breakthrough\u2014and that the American AI industry still has key advantages over China\u2019s.\u201cIt\u2019s not a leap forward on AI frontier capabilities,\u201d says Lennart Heim, an AI researcher at RAND. \u201cI think the market just got it wrong.\u201dRead More: What to Know About DeepSeek, the Chinese AI Company Causing Stock Market ChaosHere are several claims being widely circulated about DeepSeek\u2019s implications, and why scientists say they\u2019re incomplete or outright wrong. Claim: DeepSeek is much cheaper than other models. In December, DeepSeek reported that its V3 model cost just $6 million to train. This figure seemed startlingly low compared to the more than $100 million that OpenAI said it spent training GPT-4, or the \u201cfew tens of millions\u201d that Anthropic spent training a recent version of its Claude model.DeepSeek\u2019s lower price tag was thanks to some big efficiency gains that the\n",
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        "output": "\ud83d\udcc3 Source: https://www.philschmid.de/deepseek-r1\nTitle: Bite: How Deepseek R1 was trained\nContent: reaching performance levels comparable to OpenAI-o1-0912 alongside output token length per problem increasing, indicating the model naturally learns to solve tasks with more thinking time/token generation. This has the drawback of leading to poor readability and language mixing but it was solved for R1 using a multi-stage approach with alternating SFT \u2192 RL steps. The Multi-Stage Training of DeepSeek R1 To prevent the early unstable cold start phase of reinforcement training (RL) training from the base model, the team started with supervised fine-tuning. Stage 1/4 Base to Supervised Fine-Tuning (SFT) Collected up to 10k token-long chain-of-thought (CoT) using the fine-tuned models, R1-zero and human annotator. The data is used to fine-tune Deepseek V3 base to improve readbility and coherence. Stage 2/4 RL for Reasoning Used the same RL pipeline as R1-Zero, focusing on reasoning-intensive tasks such as coding and math using the same Rule-Based Reward Models. This time, an additional\n\nSource: https://www.philschmid.de/deepseek-r1\nTitle: Bite: How Deepseek R1 was trained\nContent: from 15.6% to 71.0%, reaching performance levels comparable to OpenAI-o1-0912 alongside output token length per problem increasing, indicating the model naturally learns to solve tasks with more thinking time/token generation. This has the drawback of leading to poor readability and language mixing but it was solved for R1 using a multi-stage approach with alternating SFT \u2192 RL steps. The Multi-Stage Training of DeepSeek R1 To prevent the early unstable cold start phase of reinforcement training (RL) training from the base model, the team started with supervised fine-tuning. Stage 1/4 Base to Supervised Fine-Tuning (SFT) Collected up to 10k token-long chain-of-thought (CoT) using the fine-tuned models, R1-zero and human annotator. The data is used to fine-tune Deepseek V3 base to improve readbility and coherence. Stage 2/4 RL for Reasoning Used the same RL pipeline as R1-Zero, focusing on reasoning-intensive tasks such as coding and math using the same Rule-Based Reward Models. This\n\nSource: https://www.philschmid.de/deepseek-r1\nTitle: Bite: How Deepseek R1 was trained\nContent: to OpenAI-o1-0912 alongside output token length per problem increasing, indicating the model naturally learns to solve tasks with more thinking time/token generation. This has the drawback of leading to poor readability and language mixing but it was solved for R1 using a multi-stage approach with alternating SFT \u2192 RL steps. The Multi-Stage Training of DeepSeek R1 To prevent the early unstable cold start phase of reinforcement training (RL) training from the base model, the team started with supervised fine-tuning. Stage 1/4 Base to Supervised Fine-Tuning (SFT) Collected up to 10k token-long chain-of-thought (CoT) using the fine-tuned models, R1-zero and human annotator. The data is used to fine-tune Deepseek V3 base to improve readbility and coherence. Stage 2/4 RL for Reasoning Used the same RL pipeline as R1-Zero, focusing on reasoning-intensive tasks such as coding and math using the same Rule-Based Reward Models. This time, an additional reward for \"language consistency\" is used\n\nSource: https://teqnoverse.medium.com/deepseek-r1-its-all-about-architecture-and-training-approach-50af74c223b8\nTitle: DeepSeek R1: It\u2019s All About Architecture and Training Approach | by TeqnoVerse | Jan, 2025 | Medium\nContent: behavior by leveraging a diverse set of rewards. This includes reasoning-specific rewards for tasks with clear rules, like math or coding, and human preference rewards to align the model with values such as helpfulness and harmlessness. Additionally, the model is trained on a wide variety of prompts to enhance its generalizabilityThis multi-stage process allows DeepSeek R1 to leverage the power of both supervised learning and reinforcement learning, resulting in a model with strong reasoning capabilities, improved readability, and better alignment with human preferences.\n\nSource: https://teqnoverse.medium.com/deepseek-r1-its-all-about-architecture-and-training-approach-50af74c223b8\nTitle: DeepSeek R1: It\u2019s All About Architecture and Training Approach | by TeqnoVerse | Jan, 2025 | Medium\nContent: behavior by leveraging a diverse set of rewards. This includes reasoning-specific rewards for tasks with clear rules, like math or coding, and human preference rewards to align the model with values such as helpfulness and harmlessness. Additionally, the model is trained on a wide variety of prompts to enhance its generalizabilityThis multi-stage process allows DeepSeek R1 to leverage the power of both supervised learning and reinforcement learning, resulting in a model with strong reasoning capabilities, improved readability, and better alignment with human preferences.\n\nSource: https://teqnoverse.medium.com/deepseek-r1-its-all-about-architecture-and-training-approach-50af74c223b8\nTitle: DeepSeek R1: It\u2019s All About Architecture and Training Approach | by TeqnoVerse | Jan, 2025 | Medium\nContent: behavior by leveraging a diverse set of rewards. This includes reasoning-specific rewards for tasks with clear rules, like math or coding, and human preference rewards to align the model with values such as helpfulness and harmlessness. Additionally, the model is trained on a wide variety of prompts to enhance its generalizabilityThis multi-stage process allows DeepSeek R1 to leverage the power of both supervised learning and reinforcement learning, resulting in a model with strong reasoning capabilities, improved readability, and better alignment with human preferences.\n\nSource: https://teqnoverse.medium.com/deepseek-r1-its-all-about-architecture-and-training-approach-50af74c223b8\nTitle: DeepSeek R1: It\u2019s All About Architecture and Training Approach | by TeqnoVerse | Jan, 2025 | Medium\nContent: behavior by leveraging a diverse set of rewards. This includes reasoning-specific rewards for tasks with clear rules, like math or coding, and human preference rewards to align the model with values such as helpfulness and harmlessness. Additionally, the model is trained on a wide variety of prompts to enhance its generalizabilityThis multi-stage process allows DeepSeek R1 to leverage the power of both supervised learning and reinforcement learning, resulting in a model with strong reasoning capabilities, improved readability, and better alignment with human preferences.\n\nSource: https://teqnoverse.medium.com/deepseek-r1-its-all-about-architecture-and-training-approach-50af74c223b8\nTitle: DeepSeek R1: It\u2019s All About Architecture and Training Approach | by TeqnoVerse | Jan, 2025 | Medium\nContent: behavior by leveraging a diverse set of rewards. This includes reasoning-specific rewards for tasks with clear rules, like math or coding, and human preference rewards to align the model with values such as helpfulness and harmlessness. Additionally, the model is trained on a wide variety of prompts to enhance its generalizabilityThis multi-stage process allows DeepSeek R1 to leverage the power of both supervised learning and reinforcement learning, resulting in a model with strong reasoning capabilities, improved readability, and better alignment with human preferences.\n\nSource: https://teqnoverse.medium.com/deepseek-r1-its-all-about-architecture-and-training-approach-50af74c223b8\nTitle: DeepSeek R1: It\u2019s All About Architecture and Training Approach | by TeqnoVerse | Jan, 2025 | Medium\nContent: behavior by leveraging a diverse set of rewards. This includes reasoning-specific rewards for tasks with clear rules, like math or coding, and human preference rewards to align the model with values such as helpfulness and harmlessness. Additionally, the model is trained on a wide variety of prompts to enhance its generalizabilityThis multi-stage process allows DeepSeek R1 to leverage the power of both supervised learning and reinforcement learning, resulting in a model with strong reasoning capabilities, improved readability, and better alignment with human preferences.\n\nSource: https://teqnoverse.medium.com/deepseek-r1-its-all-about-architecture-and-training-approach-50af74c223b8\nTitle: DeepSeek R1: It\u2019s All About Architecture and Training Approach | by TeqnoVerse | Jan, 2025 | Medium\nContent: behavior by leveraging a diverse set of rewards. This includes reasoning-specific rewards for tasks with clear rules, like math or coding, and human preference rewards to align the model with values such as helpfulness and harmlessness. Additionally, the model is trained on a wide variety of prompts to enhance its generalizabilityThis multi-stage process allows DeepSeek R1 to leverage the power of both supervised learning and reinforcement learning, resulting in a model with strong reasoning capabilities, improved readability, and better alignment with human preferences.\n",
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        "output": "\ud83d\udcc3 Source: https://toloka.ai/blog/knowledge-distillation/\nTitle: Knowledge distillation: a way to make a large model more efficient and accessible\nContent: large language models (LLMs), knowledge distillation has facilitated the transfer of more abstract characteristics, such as the model's style, reasoning capabilities, and alignment with human preferences and values. Knowledge distillation techniques go beyond simply copying the outputs of teacher models; they strive to mimic the underlying \"thought processes\" of these models.Knowledge distillation represents one of the most effective ways to reduce the size of a model as well as its processing speed. As a result of model compression, the intricate and large deep neural network is condensed into a smaller and more simplified one, while preserving the accuracy and performance of the initial model.A scaled-down model, trained to replicate the behavior of a heavy and accurate teacher model, achieves similar results to the teacher model, significantly benefiting in terms of size and speed due to its simplified architecture. The student model may even outperform the teacher in some cases,\n\nSource: https://medium.com/@jenrola_odun/exploring-knowledge-distillation-in-large-language-models-9d9be2bff669\nTitle: Exploring Knowledge Distillation in Large Language Models | by Odunola Jenrola | Medium\nContent: Open in appSign upSign inWriteSign upSign inExploring Knowledge Distillation in Large Language ModelsOdunola Jenrola\u00b7Follow10 min read\u00b7Nov 15, 2023--1ListenSharePhoto by Growtika on UnsplashAI companies continue to scale up language models , yet practical deployment of these large models remains a formidable challenge. In this article, we delve into an invaluable technique to address this issue: Knowledge distillation.Knowledge distillation enables the transfer of expertise from a complex, large model, known as the \u201cteacher,\u201d to a more compact, lightweight model, the \u201cstudent.\u201d This is now a cornerstone in AI as it not only eases the computational burden of deploying complex models but also facilitates their application in real-world scenarios especially in resource constrained environment like edge devices and CPU-only environments.In this article, Our goal is to distill a large BERT transformer model I already finetuned for text classification. Our student model is a more compact\n\nSource: https://medium.com/@jenrola_odun/exploring-knowledge-distillation-in-large-language-models-9d9be2bff669\nTitle: Exploring Knowledge Distillation in Large Language Models | by Odunola Jenrola | Medium\nContent: Open in appSign upSign inWriteSign upSign inExploring Knowledge Distillation in Large Language ModelsOdunola Jenrola\u00b7Follow10 min read\u00b7Nov 15, 2023--1ListenSharePhoto by Growtika on UnsplashAI companies continue to scale up language models , yet practical deployment of these large models remains a formidable challenge. In this article, we delve into an invaluable technique to address this issue: Knowledge distillation.Knowledge distillation enables the transfer of expertise from a complex, large model, known as the \u201cteacher,\u201d to a more compact, lightweight model, the \u201cstudent.\u201d This is now a cornerstone in AI as it not only eases the computational burden of deploying complex models but also facilitates their application in real-world scenarios especially in resource constrained environment like edge devices and CPU-only environments.In this article, Our goal is to distill a large BERT transformer model I already finetuned for text classification. Our student model is a more compact\n\nSource: https://medium.com/@jenrola_odun/exploring-knowledge-distillation-in-large-language-models-9d9be2bff669\nTitle: Exploring Knowledge Distillation in Large Language Models | by Odunola Jenrola | Medium\nContent: Open in appSign upSign inWriteSign upSign inExploring Knowledge Distillation in Large Language ModelsOdunola Jenrola\u00b7Follow10 min read\u00b7Nov 15, 2023--1ListenSharePhoto by Growtika on UnsplashAI companies continue to scale up language models , yet practical deployment of these large models remains a formidable challenge. In this article, we delve into an invaluable technique to address this issue: Knowledge distillation.Knowledge distillation enables the transfer of expertise from a complex, large model, known as the \u201cteacher,\u201d to a more compact, lightweight model, the \u201cstudent.\u201d This is now a cornerstone in AI as it not only eases the computational burden of deploying complex models but also facilitates their application in real-world scenarios especially in resource constrained environment like edge devices and CPU-only environments.In this article, Our goal is to distill a large BERT transformer model I already finetuned for text classification. Our student model is a more compact\n\nSource: https://medium.com/@jenrola_odun/exploring-knowledge-distillation-in-large-language-models-9d9be2bff669\nTitle: Exploring Knowledge Distillation in Large Language Models | by Odunola Jenrola | Medium\nContent: Exploring Knowledge Distillation in Large Language ModelsOdunola Jenrola\u00b7Follow10 min read\u00b7Nov 15, 2023--1ListenSharePhoto by Growtika on UnsplashAI companies continue to scale up language models , yet practical deployment of these large models remains a formidable challenge. In this article, we delve into an invaluable technique to address this issue: Knowledge distillation.Knowledge distillation enables the transfer of expertise from a complex, large model, known as the \u201cteacher,\u201d to a more compact, lightweight model, the \u201cstudent.\u201d This is now a cornerstone in AI as it not only eases the computational burden of deploying complex models but also facilitates their application in real-world scenarios especially in resource constrained environment like edge devices and CPU-only environments.In this article, Our goal is to distill a large BERT transformer model I already finetuned for text classification. Our student model is a more compact model also based on BERT but having fewer\n\nSource: https://medium.com/@jenrola_odun/exploring-knowledge-distillation-in-large-language-models-9d9be2bff669\nTitle: Exploring Knowledge Distillation in Large Language Models | by Odunola Jenrola | Medium\nContent: Exploring Knowledge Distillation in Large Language ModelsOdunola Jenrola\u00b7Follow10 min read\u00b7Nov 15, 2023--1ListenSharePhoto by Growtika on UnsplashAI companies continue to scale up language models , yet practical deployment of these large models remains a formidable challenge. In this article, we delve into an invaluable technique to address this issue: Knowledge distillation.Knowledge distillation enables the transfer of expertise from a complex, large model, known as the \u201cteacher,\u201d to a more compact, lightweight model, the \u201cstudent.\u201d This is now a cornerstone in AI as it not only eases the computational burden of deploying complex models but also facilitates their application in real-world scenarios especially in resource constrained environment like edge devices and CPU-only environments.In this article, Our goal is to distill a large BERT transformer model I already finetuned for text classification. Our student model is a more compact model also based on BERT but having fewer\n\nSource: https://medium.com/@jenrola_odun/exploring-knowledge-distillation-in-large-language-models-9d9be2bff669\nTitle: Exploring Knowledge Distillation in Large Language Models | by Odunola Jenrola | Medium\nContent: Exploring Knowledge Distillation in Large Language ModelsOdunola Jenrola\u00b7Follow10 min read\u00b7Nov 15, 2023--1ListenSharePhoto by Growtika on UnsplashAI companies continue to scale up language models , yet practical deployment of these large models remains a formidable challenge. In this article, we delve into an invaluable technique to address this issue: Knowledge distillation.Knowledge distillation enables the transfer of expertise from a complex, large model, known as the \u201cteacher,\u201d to a more compact, lightweight model, the \u201cstudent.\u201d This is now a cornerstone in AI as it not only eases the computational burden of deploying complex models but also facilitates their application in real-world scenarios especially in resource constrained environment like edge devices and CPU-only environments.In this article, Our goal is to distill a large BERT transformer model I already finetuned for text classification. Our student model is a more compact model also based on BERT but having fewer\n\nSource: https://medium.com/@jenrola_odun/exploring-knowledge-distillation-in-large-language-models-9d9be2bff669\nTitle: Exploring Knowledge Distillation in Large Language Models | by Odunola Jenrola | Medium\nContent: Exploring Knowledge Distillation in Large Language ModelsOdunola Jenrola\u00b7Follow10 min read\u00b7Nov 15, 2023--1ListenSharePhoto by Growtika on UnsplashAI companies continue to scale up language models , yet practical deployment of these large models remains a formidable challenge. In this article, we delve into an invaluable technique to address this issue: Knowledge distillation.Knowledge distillation enables the transfer of expertise from a complex, large model, known as the \u201cteacher,\u201d to a more compact, lightweight model, the \u201cstudent.\u201d This is now a cornerstone in AI as it not only eases the computational burden of deploying complex models but also facilitates their application in real-world scenarios especially in resource constrained environment like edge devices and CPU-only environments.In this article, Our goal is to distill a large BERT transformer model I already finetuned for text classification. Our student model is a more compact model also based on BERT but having fewer\n\nSource: https://medium.com/@jenrola_odun/exploring-knowledge-distillation-in-large-language-models-9d9be2bff669\nTitle: Exploring Knowledge Distillation in Large Language Models | by Odunola Jenrola | Medium\nContent: Exploring Knowledge Distillation in Large Language ModelsOdunola Jenrola\u00b7Follow10 min read\u00b7Nov 15, 2023--1ListenSharePhoto by Growtika on UnsplashAI companies continue to scale up language models , yet practical deployment of these large models remains a formidable challenge. In this article, we delve into an invaluable technique to address this issue: Knowledge distillation.Knowledge distillation enables the transfer of expertise from a complex, large model, known as the \u201cteacher,\u201d to a more compact, lightweight model, the \u201cstudent.\u201d This is now a cornerstone in AI as it not only eases the computational burden of deploying complex models but also facilitates their application in real-world scenarios especially in resource constrained environment like edge devices and CPU-only environments.In this article, Our goal is to distill a large BERT transformer model I already finetuned for text classification. Our student model is a more compact model also based on BERT but having fewer\n\nSource: https://medium.com/@jenrola_odun/exploring-knowledge-distillation-in-large-language-models-9d9be2bff669\nTitle: Exploring Knowledge Distillation in Large Language Models | by Odunola Jenrola | Medium\nContent: Exploring Knowledge Distillation in Large Language ModelsOdunola Jenrola\u00b7Follow10 min read\u00b7Nov 15, 2023--1ListenSharePhoto by Growtika on UnsplashAI companies continue to scale up language models , yet practical deployment of these large models remains a formidable challenge. In this article, we delve into an invaluable technique to address this issue: Knowledge distillation.Knowledge distillation enables the transfer of expertise from a complex, large model, known as the \u201cteacher,\u201d to a more compact, lightweight model, the \u201cstudent.\u201d This is now a cornerstone in AI as it not only eases the computational burden of deploying complex models but also facilitates their application in real-world scenarios especially in resource constrained environment like edge devices and CPU-only environments.In this article, Our goal is to distill a large BERT transformer model I already finetuned for text classification. Our student model is a more compact model also based on BERT but having fewer\n",
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      "https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fe48af6fa-8956-44b0-84cf-915e607f3b5e_1546x884.png"
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}