Tag: context window
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Hacker News: New LLM optimization technique slashes memory costs up to 75%
Source URL: https://venturebeat.com/ai/new-llm-optimization-technique-slashes-memory-costs-up-to-75/ Source: Hacker News Title: New LLM optimization technique slashes memory costs up to 75% Feedly Summary: Comments AI Summary and Description: Yes Summary: Researchers at Sakana AI have developed a novel technique called “universal transformer memory” that enhances the efficiency of large language models (LLMs) by optimizing their memory usage. This innovation…
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Hacker News: Comparison of Claude Sonnet 3.5, GPT-4o, o1, and Gemini 1.5 Pro for coding
Source URL: https://www.qodo.ai/blog/comparison-of-claude-sonnet-3-5-gpt-4o-o1-and-gemini-1-5-pro-for-coding/ Source: Hacker News Title: Comparison of Claude Sonnet 3.5, GPT-4o, o1, and Gemini 1.5 Pro for coding Feedly Summary: Comments AI Summary and Description: Yes **Summary:** This text provides a comprehensive analysis of various AI models, particularly focusing on recent advancements in LLMs (Large Language Models) for coding tasks. It assesses the…
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Cloud Blog: Don’t let resource exhaustion leave your users hanging: A guide to handling 429 errors
Source URL: https://cloud.google.com/blog/products/ai-machine-learning/learn-how-to-handle-429-resource-exhaustion-errors-in-your-llms/ Source: Cloud Blog Title: Don’t let resource exhaustion leave your users hanging: A guide to handling 429 errors Feedly Summary: Large language models (LLMs) give developers immense power and scalability, but managing resource consumption is key to delivering a smooth user experience. LLMs demand significant computational resources, which means it’s essential to…
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Simon Willison’s Weblog: Quoting Steven Johnson
Source URL: https://simonwillison.net/2024/Nov/21/steven-johnson/#atom-everything Source: Simon Willison’s Weblog Title: Quoting Steven Johnson Feedly Summary: When we started working on what became NotebookLM in the summer of 2022, we could fit about 1,500 words in the context window. Now we can fit up to 1.5 million words. (And using various other tricks, effectively fit 25 million words.)…
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Simon Willison’s Weblog: Qwen: Extending the Context Length to 1M Tokens
Source URL: https://simonwillison.net/2024/Nov/18/qwen-turbo/#atom-everything Source: Simon Willison’s Weblog Title: Qwen: Extending the Context Length to 1M Tokens Feedly Summary: Qwen: Extending the Context Length to 1M Tokens The new Qwen2.5-Turbo boasts a million token context window (up from 128,000 for Qwen 2.5) and faster performance: Using sparse attention mechanisms, we successfully reduced the time to first…