Experimental News Clipping Site

Tag: training method

  • Hacker News: How DeepSeek-R1 Was Built, for Dummies

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    Kurt Seifried
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    Source URL: https://www.vellum.ai/blog/the-training-of-deepseek-r1-and-ways-to-use-it Source: Hacker News Title: How DeepSeek-R1 Was Built, for Dummies Feedly Summary: Comments AI Summary and Description: Yes **Summary:** The text discusses DeepSeek’s innovative approach to training reasoning models through pure reinforcement learning (RL) without labeled data. This breakthrough could significantly impact the development of AI, particularly in the realm of large…

  • Hacker News: The impact of competition and DeepSeek on Nvidia

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    Kurt Seifried
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    Source URL: https://youtubetranscriptoptimizer.com/blog/05_the_short_case_for_nvda Source: Hacker News Title: The impact of competition and DeepSeek on Nvidia Feedly Summary: Comments AI Summary and Description: Yes **Summary:** The text presents a comprehensive assessment of the current state and future outlook of Nvidia in the AI hardware market, emphasizing their significant market position and potential vulnerabilities from emerging competition…

  • Hacker News: Qwen2.5-1M: Deploy Your Own Qwen with Context Length Up to 1M Tokens

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    Kurt Seifried
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    Source URL: https://qwenlm.github.io/blog/qwen2.5-1m/ Source: Hacker News Title: Qwen2.5-1M: Deploy Your Own Qwen with Context Length Up to 1M Tokens Feedly Summary: Comments AI Summary and Description: Yes Summary: The text reports on the new release of the open-source Qwen2.5-1M models, capable of processing up to one million tokens, significantly improving inference speed and model performance…

  • Hacker News: DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via RL

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    Kurt Seifried
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    Source URL: https://arxiv.org/abs/2501.12948 Source: Hacker News Title: DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via RL Feedly Summary: Comments AI Summary and Description: Yes Summary: The text discusses the introduction of new language models, DeepSeek-R1 and DeepSeek-R1-Zero, developed to enhance reasoning capabilities in large language models (LLMs) through reinforcement learning. This research represents a significant advancement…

  • Hacker News: DeepSeek and the Effects of GPU Export Controls

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    Kurt Seifried
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    Source URL: https://www.vincentschmalbach.com/deepseek-and-the-effects-of-gpu-export-controls/ Source: Hacker News Title: DeepSeek and the Effects of GPU Export Controls Feedly Summary: Comments AI Summary and Description: Yes Summary: DeepSeek’s unveiling of their V3 model demonstrates that AI advancements do not solely depend on high-end hardware but can be achieved through architectural efficiency. The model, trained on significantly fewer resources…

  • Hacker News: Kimi K1.5: Scaling Reinforcement Learning with LLMs

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    Kurt Seifried
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    Source URL: https://github.com/MoonshotAI/Kimi-k1.5 Source: Hacker News Title: Kimi K1.5: Scaling Reinforcement Learning with LLMs Feedly Summary: Comments AI Summary and Description: Yes Summary: The text introduces Kimi k1.5, a new multi-modal language model that employs reinforcement learning (RL) techniques to significantly enhance AI performance, particularly in reasoning tasks. With advancements in context scaling and policy…

  • Simon Willison’s Weblog: DeepSeek-R1 and exploring DeepSeek-R1-Distill-Llama-8B

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    Kurt Seifried
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    Source URL: https://simonwillison.net/2025/Jan/20/deepseek-r1/ Source: Simon Willison’s Weblog Title: DeepSeek-R1 and exploring DeepSeek-R1-Distill-Llama-8B Feedly Summary: DeepSeek are the Chinese AI lab who dropped the best currently available open weights LLM on Christmas day, DeepSeek v3. That model was trained in part using their unreleased R1 “reasoning" model. Today they’ve released R1 itself, along with a whole…

  • Hacker News: 400x faster embeddings models using static embeddings

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    Kurt Seifried
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    Source URL: https://huggingface.co/blog/static-embeddings Source: Hacker News Title: 400x faster embeddings models using static embeddings Feedly Summary: Comments AI Summary and Description: Yes **Summary:** This blog post discusses a new method to train static embedding models significantly faster than existing state-of-the-art models. These models are suited for various applications, including on-device and in-browser execution, and edge…

  • Hacker News: Transformer^2: Self-Adaptive LLMs

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    Kurt Seifried
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    Source URL: https://sakana.ai/transformer-squared/ Source: Hacker News Title: Transformer^2: Self-Adaptive LLMs Feedly Summary: Comments AI Summary and Description: Yes Summary: The text discusses the innovative Transformer² machine learning system, which introduces self-adaptive capabilities to LLMs, allowing them to adjust dynamically to various tasks. This advancement promises significant improvements in AI efficiency and adaptability, paving the way…