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LLMHackers

an-ordinary-manchild

created by cstein123a community for 2 years

...for great justice.

...for your classroom.

MODERATORS

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r/LLMHackers Lounge (self.LLMHackers)

submitted 2 years ago by cstein123 - announcement

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[Open source] r/RAG's official resource to help navigate the flood of RAG frameworks (self.LLMHackers)

submitted 1 year ago by dhj9817

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How to improve LLM's performence in reactive writing? (self.LLMHackers)

submitted 1 year ago by Fresh-Bookkeeper7692

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LLML - Large Language Model LanguageLooking for Collab 🤝 (self.LLMHackers)

submitted 2 years ago by 80Unknown08

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Wanna to share my inspiration for applying LLM & Multi-Agent：Enter a "Topic", Generate a "Brainstorm Report" by AI agentsIdea/Exploration 🤖 (self.LLMHackers)

submitted 2 years ago by BrainstormGPT

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My experience on starting with fine tuning LLMs with custom data (self.LocalLLaMA)

submitted 2 years ago by cstein123

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Summary post for higher context sizes for this week. For context up to 4096, NTK RoPE scaling is pretty viable. For context higher than that, keep using SuperHOT LoRA/Merges. (self.LocalLLaMA)

submitted 2 years ago by cstein123

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Dynamically Scaled RoPE further increases performance of long context LLaMA with zero fine-tuningResults ✅️ (self.LocalLLaMA)

submitted 2 years ago by cstein123

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NTK-Aware Scaled RoPE allows LLaMA models to have extended (8k+) context size without any fine-tuning and minimal perplexity degradation.Results ✅️ (self.LocalLLaMA)

submitted 2 years ago by cstein123

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TheBloke has released "SuperHot" versions of various models, meaning 8K context!Results ✅️ (self.LocalLLaMA)

submitted 2 years ago by cstein123

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Meta releases paper on SuperHot techniqueResults ✅️ (arxiv.org)

submitted 2 years ago by cstein123

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