🔴 fengor 🔴 @fengor

Big pricing change to a valuable GCP security feature for least-privilege IAM. Requiring the org-level SCC Premium bundle instead of a pay-as-you-go per-service price will be prohibitively expensive and detrimental to security for many customers in the long run. @philvenables

GCP Weekly @gcpweekly

3 years ago

Policy Intelligence update on September 28, 2023 cloud.google.com/policy-intelli… #googlecloud After January 15, 2024, some Policy Intelligence features will only be available for customers with organization-level activations of Security 1/2

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kat traxler @NightmareJS

3 years ago

As of next year, #google will require premium subscriptions for things like IAM Recommender, Role Insights, etc. This is the OPPOSITE of their mantra calling for shared fate instead of the shared responsibility model. A massive loss in credibility.

GCP Weekly @gcpweekly

3 years ago

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🔴 fengor 🔴 @fengor

3 years ago

@Tetz_Aztlan Hier ^^

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🔴 fengor 🔴 @fengor

3 years ago

@simonholdorf I usually use python when giving introductionary programming lessons

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Jeremy Howard @jeremyphoward

3 years ago

I just uploaded a 90 minute tutorial, which is designed to be the one place I point coders at when they ask "hey, tell me everything I need to know about LLMs!" It starts at the basics: the 3-step pre-training / fine-tuning / classifier ULMFiT approach used in all modern LLMs.

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Itamar Golan 🤓 @ItakGol

3 years ago

This paper blew my mind 🤯 Introducing LongLoRA! An ultra efficient fine-tuning method designed to extend the context sizes of pre-trained large language models (LLMs) without a huge computation cost. Typically, training LLMs with longer context sizes consumes a lot of time and requires strong GPU resources. For example, extending the context length from 2048 to 8192 increases computational costs 16 times, particularly in self-attention layers. 🖥️ What sets LongLoRA apart is its two-pronged approach to speeding up the context extension of LLMs. First, it uses sparse local attention instead of dense global attention during the fine-tuning phase, which is a more efficient way to handle this task. This change, known as shift-short attention, significantly saves computational effort while maintaining similar performance levels compared to traditional attention mechanisms. Plus, it’s simple to implement, requiring just two lines of code during training, and it's optional during inference. 💡 Second, LongLoRA revisits the idea of a more parameter-efficient fine-tuning process for context expansion. The effectiveness of LoRA is enhanced when combined with trainable embedding and normalization, showing solid results. In practical terms, LongLoRA showed strong performance on various tasks using LLaMA2 models ranging from 7B/13B to 70B. Notably, it extended LLaMA2 7B from 4k context to 100k, and LLaMA2 70B to 32k on a single 8x A100 machine, all while keeping the original model architectures intact. 📈 Moreover, LongLoRA plays well and compatible with existing techniques like FlashAttention-2. To make it more user-friendly, a dataset called LongQA was created for supervised fine-tuning, containing over 3k long context question-answer pairs. LongLoRA is an important step toward making model expansion more computationally efficient. Paper - arxiv.org/abs/2309.12307 GitHub - github.com/dvlab-research…

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deararchitects @deararchitects

3 years ago

Issue 154: mailchi.mp/deararchitects… This week: 1. How to bridge silos inside your organization 2. #devops best practices 3. How @stripe is designed for #BlackFriday traffic 4. New #serverless book from @sheenbrisals and @level_out 5. @OReillyMedia #architectures #books #bundle

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🔴 fengor 🔴 @fengor

3 years ago

@mathiasrichel Solange man noch versucht den Kommunen mit Hilfe der Ministerien Blockchain zu verkaufen wird das auch nicht besser...

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🔴 fengor 🔴 @fengor

3 years ago

@simonholdorf Linux > macos > windows

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Gary Marcus @GaryMarcus

3 years ago

Ever wonder what LLMs are for, aside from coding? Here’s one theory:

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Jerry Liu @jerryjliu0

3 years ago

Here are 8 key considerations for building *production-grade* LLM apps over your data (RAG) 💡 (see 🧵): 1️⃣ Chunks used for retrieval shouldn’t necessarily be the same as chunks used for LLM synthesis (@maxrumpf) 2️⃣ Embeddings should live in a different latent space than what you get from raw text, which can contain filler that biases the embeddings. Consider passing a transformed text representation or finetuning. (@maxrumpf) 3️⃣ If retrieval isn’t returning the right context, you may need to dynamically load/update the data itself (@maxrumpf, @bobvanluijt) 4️⃣ Design your pipeline for scalability. Latency times in prototyping do not translate to production workloads. Start with easy-to-use, high latency modules but aggressively try to shrink latency component by component (@bobvanluijt) 5️⃣ Store data in a hierarchical fashion: summaries of documents and chunks for each document (@bobvanluijt) 6️⃣ Robust data pipelines in prod don't matter as much if you only load the data once. But they are especially important when the source data is constantly changing (@tuanacelik, @maxrumpf) 7️⃣ RAG isn’t just question-answering: for summaries, you may need ALL chunks, for question-answering, you may need specific chunks. Your chunk sizes may differ depending on the use case (@tuanacelik) 8️⃣ Embedding-based retrieval does not work well for entity lookup. Hybrid search combines benefits of keyword lookup with additional context (@tuanacelik) All of these insights and more can be found in our fantastic “Make RAG Production-Ready” webinar, now available for viewing on YouTube! 🔥👇 HUGE shoutout to @tuanacelik, @maxrumpf, and @bobvanluijt for a fantastic discussion. It’s clear that we’ve barely scratched the surface - we’ll definitely be doing a part 2! youtu.be/Zj5RCweUHIk

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🔴 fengor 🔴 @fengor

3 years ago

Hey @PeugeotDE falls ihr das liest: wie wär's mit mindestens Mal Ner Fehler ID, so dass man wenigstens weiß was das Auto hat während man mehrere hundert Kilometer von Zuhause mit nem kleinen Kind und der Frau in der Gegend Rum steht

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🔴 fengor 🔴 @fengor

3 years ago

Und der Preis für die größte FUD-Meldung mit nervigen Ton geht an: Peugeot

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🔴 fengor 🔴 @fengor

3 years ago

For anynone with a Canon DSLR, that just won't power on after prolonged storage. This fixed it for me: youtube.com/watch?v=bkjUJY… tl;dr: Remove battery, hold down the shutter button for about 30-60sec, reinsert battery, power on