New LLM plugin release: llm-anyscale-endpoints 0.5 adds support for their new hosted Mixtral 8x22B Instruct model, plus a "llm anyscale-endpoints refresh" command for refreshing the list of models without needing to upgrade the plugin in the future https://
github.com/simonw/llm-any
scale-endpoints/releases/tag/0.5
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@simonw
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Anyscale Endpoints Plugin 0.5 Adds Mixtral 8x22B Support
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One Size Doesn’t Fit All in Technology Solutions
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I don't know what my requirements are yet – I build a lot of different things with a lot of different people, so the only thing I'm truly confident about is that one size is not going to fit all of them
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Claude 3 Haiku Image-to-Haiku Demos and Tools
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Here's the full list of demos. My favourite is probably that first one – Haikus from images with Claude 3 Haiku – which you can try out yourself here if you have a Claude 3 API key https://
tools.simonwillison.net/haiku -
AI for Data Journalism: Practical Applications and Demonstrations
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I gave a 50m talk at the Story Discovery at Scale data journalism conference at Stanford a few weeks ago. The video is now out, and I've written an extensive annotated version AI for Data Journalism: demonstrating what we can do with this stuff right now
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OpenAI Chunking Strategy: Assistants vs Custom Implementation
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That's really useful, thanks My problem here is that if I don't know exactly how OpenAI chunking works I can't make a decision as to whether I should use the assistants mechanism or roll my own chunking strategy that's optimized for my particular use-case
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LLM CLI Tool Chunking Strategies and Implementation
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No I don't, and it's something I really need – I want to add chunking features to my LLM CLI tool and library but I can't find reliable information on what strategies are important to support
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A/B Testing Risks on Developer Platforms Raise Reliability Concerns
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I'm suspicious enough of A/B testing as an end-user, the idea that a developer playform I'm trying to build software on might be running A/B tests that affect my applications is horrifying
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OpenAI Chunking Strategy for RAG Implementation Details
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I'm begging you @OpenAIDevs
, please tell us how your chunking works! It's a small detail, but it makes a huge difference in helping me make decisions about how to effectively use your RAG implement -
Finding SSH Terminal Environment for Hands-On Experimentation
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Specifically looking for something I can SSH into and mess around in a terminal for a bit
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Cheapest GPU Server Options for Running Mixtral Models
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What's the cheapest option right now for me to spin up a Linux server somewhere for an hour with enough GPU to run the latest Mixtral model?