Not sure what's happened there. It's a weight-free model so start up is fast. If you DM me the details I'll raise with team.
CODE
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Attention Is All You Need: Seminal Transformer Paper From 2017
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Here's the original Attention Is All You Need paper from June 2017 https://
arxiv.org/abs/1706.03762 -
Sakana AI Hiring Researchers Engineers Nature-Inspired Foundation Models
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Sakana AI is hiring researchers and engineers who are passionate about novel approaches to develop the next generation of nature-inspired foundation AI models! For more details → https://
sakana.ai/careers/ We’re looking for candidates who we believe have the potential to build -
Ludwig v0.9.2 Released with New Features and Improvements
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Announcing #Ludwig v0.9.2! This release introduces several capabilities and fixes: Per-step token utilization to tensorboard and progress tracker Default LoRA target modules for #Mixtral Support for exporting models to Carton https://
pbase.ai/3vI8SCW -
AI’s Role in Software Development: Beyond Code Generation
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AI in software development is not just writing code, it’s hundreds of things. It’s: Voice to code
Text to what the code makes (design, website, whatever)
Diagram to code
User behavior to real-time code updates
AI-assisted debugging and code optimization
Predictive code -
Improving Model Accuracy with Nonlinear MLP Mapping Techniques
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I just checked and my accuracy is more like 70%! with a linear mapping. it goes up with a nonlinear MLP.
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Build AI Agents On All Your Structured And Unstructured Data
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Build AI Agents On All Your Structured And Unstructured Data
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Tips for Reliable, Fast, and User-Friendly Models
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Some tips on making your models reliable, fast, and user-friendly
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Replicate Model Upload Best Practices Guide
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If you upload models to Replicate, I've put together a short best practices guide to make the model the best it can be: https://
replicate.com/guides/model-b
est-practices
… Let me know if there's anything missing. -
Semantic LLM Editing for Compact Knowledge Representation
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I expect that once we can edit LLMs semantically, we can produce compact core LLMs that may not be good at style imitation, but that can quickly and reliably extract knowledge from text and store and exchange it in a kind of universally interpretable mental representation code.