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Tag: smaller models

  • Simon Willison’s Weblog: Everything I’ve learned so far about running local LLMs

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    Source URL: https://simonwillison.net/2024/Nov/10/running-llms/#atom-everything Source: Simon Willison’s Weblog Title: Everything I’ve learned so far about running local LLMs Feedly Summary: Everything I’ve learned so far about running local LLMs Chris Wellons shares detailed notes on his experience running local LLMs on Windows – though most of these tips apply to other operating systems as well. This…

  • Cloud Blog: How to deploy and serve multi-host gen AI large open models over GKE

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    Source URL: https://cloud.google.com/blog/products/ai-machine-learning/deploy-and-serve-open-models-over-google-kubernetes-engine/ Source: Cloud Blog Title: How to deploy and serve multi-host gen AI large open models over GKE Feedly Summary: Context As generative AI experiences explosive growth fueled by advancements in LLMs (Large Language Models), access to open models is more critical than ever for developers. Open models are publicly available pre-trained foundational…

  • Hacker News: Notes on Anthropic’s Computer Use Ability

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    Source URL: https://composio.dev/blog/claude-computer-use/ Source: Hacker News Title: Notes on Anthropic’s Computer Use Ability Feedly Summary: Comments AI Summary and Description: Yes **Summary:** The text discusses Anthropic’s latest AI models, Haiku 3.5 and Sonnet 3.5, highlighting the new “Computer Use” feature that enhances LLM capabilities by enabling interactions like a human user. It presents practical examples…

  • Hacker News: Cerebras Inference now 3x faster: Llama3.1-70B breaks 2,100 tokens/s

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    Source URL: https://cerebras.ai/blog/cerebras-inference-3x-faster/ Source: Hacker News Title: Cerebras Inference now 3x faster: Llama3.1-70B breaks 2,100 tokens/s Feedly Summary: Comments AI Summary and Description: Yes Summary: The text announces a significant performance upgrade to Cerebras Inference, showcasing its ability to run the Llama 3.1-70B AI model at an impressive speed of 2,100 tokens per second. This…

  • Hacker News: Throw more AI at your problems

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    Source URL: https://frontierai.substack.com/p/throw-more-ai-at-your-problems Source: Hacker News Title: Throw more AI at your problems Feedly Summary: Comments AI Summary and Description: Yes Summary: The text provides insights into the evolution of AI application development, particularly around the use of multiple LLM (Large Language Model) calls as a means to effectively address problems. It emphasizes a shift…

  • Simon Willison’s Weblog: Un Ministral, des Ministraux

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    Source URL: https://simonwillison.net/2024/Oct/16/un-ministral-des-ministraux/ Source: Simon Willison’s Weblog Title: Un Ministral, des Ministraux Feedly Summary: Un Ministral, des Ministraux Two new models from Mistral: Ministral 3B and Ministral 8B (joining Mixtral, Pixtral, Codestral and Mathstral as weird naming variants on the Mistral theme. These models set a new frontier in knowledge, commonsense, reasoning, function-calling, and efficiency…

  • Hacker News: Lm.rs Minimal CPU LLM inference in Rust with no dependency

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    Source URL: https://github.com/samuel-vitorino/lm.rs Source: Hacker News Title: Lm.rs Minimal CPU LLM inference in Rust with no dependency Feedly Summary: Comments AI Summary and Description: Yes Summary: The provided text pertains to the development and utilization of a Rust-based application for running inference on Large Language Models (LLMs), particularly the LLama 3.2 models. It discusses technical…