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Grimoire: Most Impressive Custom GPT Discovery
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1/ Grimoire
— Nathan Lands (@NathanLands) 30 novembre 2023
By far the most impressive Custom GPT I've found.https://t.co/8lUZcSrMpx1/ Grimoire By far the most impressive Custom GPT I've found.
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Master AI Fine-Tuning: ZeRO, DeepSpeed and GPTJ-6B
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Master AI Fine-Tuning with our Webinar on Dec 12! Discover ZeRO & DeepSpeed's role in GPU tuning, GPTJ-6b model enhancement, and efficient model loading. #AIWebinar #FineTuning #TechInnovation @ 9AM PT | 12PM ET | 5PM BST. Save your spot! https://
domino.buzz/47RHqjP -
Advanced RAG Techniques with XML Knowledge Graphs Achieve 3x Improvement
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Nearly 3x improvement by using advanced RAG techniques (XML knowledge graph) Excited to highlight this more over the next few days
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Eden’s LoRA Trainer Praised as Best Internet Tool
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Dare I say that Eden has the best Lora trainer on the internet right now.
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Advanced RAG Techniques Course for LLM Answer Generation
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New short course on sophisticated RAG (Retrieval Augmented Generation) techniques is out! Taught by @jerryjliu0 and @datta_cs of @llama_index and @truera_ai , this teaches advanced techniques that help your LLM generate good answers.
— Andrew Ng (@AndrewYNg) 29 novembre 2023
Topics include:
– Sentence-window retrieval,… pic.twitter.com/bExne51PexNew short course on sophisticated RAG (Retrieval Augmented Generation) techniques is out! Taught by @jerryjliu0 and @datta_cs of @llama_index and @truera_ai , this teaches advanced techniques that help your LLM generate good answers. Topics include:
– Sentence-window retrieval, -
Repetition in GPT-3 prompts elicits base-model-like generations, now confirmed
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Fascinating work — I noticed repetition elicits base-model-like generations in GPT-3 last year but assumed they were fully hallucinated. Kicking myself now. As of August you could also do token repeats within a prompt to get base-like completion of grammatically incomplete text:
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Prenatal Environment as Prompt Engineering for AI Systems
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prenatal environment as prompt engineering
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Guest lecture on intuitions for understanding large language models
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It was an honor to give a guest lecture yesterday at Stanford’s CS330 class, "Deep Multi-Task and Meta-Learning"! I discussed a few very simple intuitions for how I personally think about large language models. Slides: https://
docs.google.com/presentation/d
/1hQUd3pF8_2Gr2Obc89LKjmHL0DlH-uof9M0yFVd3FA4/edit?usp=sharing
… Here are the six intuitions: (1) -
Effect not from pre-train mimicry but system message updates
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Effect is similar but my instinct is it’s not from mimicking pre-train data. There are updates to the system message for things like refusing to recite recipes and lyrics found online; I think it’s interpreting these as a more general preference against long quotation.