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1742918574405288393 | If you've struggled to make the vision model reliability return structured data and even extract tables
Check this out. https://t.co/7RdAyupXBn
This @pydantic model contains both the serialization and turns it into a dead simple data model.
class Table(BaseModel):… | https://twitter.com/i/web/status/1742918574405288393 | [
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1743184370729357323 | Using a Markov chain to generate readable nonsense with 20 lines of Python
https://t.co/JQhR43w4gg | https://twitter.com/i/web/status/1743184370729357323 | [
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] |
1743076579302105338 | RAGatouille (@bclavie) is an awesome way to easily use ColBERT, a more advanced retrieval model compared to dense embedding-based retrieval techniques.
In turn we’ve made it easy to use ColBERT in an e2e @llama_index RAG pipeline in one line of code ⚡️, with our brand-new… https://t.co/6reMeIRxzD https://t.co/LENrAl6A08 | https://twitter.com/i/web/status/1743076579302105338 | [
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1743040090400858288 | With all the excitement around ColBERT today, thanks to the RAGatouille library, this is a good time to understand:
• What makes ColBERT work so well?
• How is it different from standard dense retriever?
• How can it search 100M docs in 0.1 seconds, with no GPU? https://t.co/qMgWdwGuK1 | https://twitter.com/i/web/status/1743040090400858288 | [
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1742988249071747483 | Check out https://t.co/YtfFyPnqx4, a new hosting service for videos. optimized for RAG and AI tools.
Just upload a video via URL/file and:
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.get_shots() to get matching clips… https://t.co/fCsAjEiEp8 | https://twitter.com/i/web/status/1742988249071747483 | [
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1743083207443267759 | So this is pretty wild, training with a ~1TB, ~12M sample image dataset, streaming directly from the HF hub over my fibre internet connection... works! Go go webdatset. Supported in timm via webdataset wrapper with curl pipes or @huggingface datasets streaming wrapper... https://t.co/MiLo77DpdZ | https://twitter.com/i/web/status/1743083207443267759 | [
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1743070051991998842 | 🦜🏗️Building an OpenAI Custom RAG with LangChain: 📹The Ultimate Tutorial to Chat with your Videos!📹
Awesome tutorial by @austinbv on how to use Whisper + OpenAI + LangChain to talk to your videos
Shows off transcription, LCEL, streaming⭐️
https://t.co/UJ6r7QrXqj | https://twitter.com/i/web/status/1743070051991998842 | [
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] |
1743009556521975893 | Love how RAGatouille makes it so easy to train new ColBERTs.
ColBERT's real power is you can train it with as little as a few hundred queries. Other dense retrievers need tens of thousands!
Maybe the test for @bclavie's library is whether we see an uptick in ColBERT downloads😆 https://t.co/TnTPT0smff https://t.co/n4hnHQODqB | https://twitter.com/i/web/status/1743009556521975893 | [
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1742973766483980692 | Big for finance. JPMorgan just announced DocLLM.
Their new LLM can understand documents, invoices, financial reports and contracts.
They key? The model skips costly image encoders and only focuses on bounding box data to understand layout structure.
Trained on 5.5M documents… https://t.co/kJTaZWjCFN | https://twitter.com/i/web/status/1742973766483980692 | [
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1742925603916112076 | TIL: Forcing `pip` to Use `virtualenv` https://t.co/oTmpm5sP1N | https://twitter.com/i/web/status/1742925603916112076 | [
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] |
1742904019427746219 | Here is an open-source library that makes integrating AI into an application extremely easy.
CopilotKit. Star their repository: https://t.co/t1PT8qPriE
The library has two components:
The first component is CopilotTextarea
• It's a drop-in replacement for any textarea field… https://t.co/LxYhfJMQlQ | https://twitter.com/i/web/status/1742904019427746219 | [
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1742930722766397932 | Excited to share a little behind the scenes of the GPT Builder feature available inside the @ChatGPTapp 👀
GPT Builder is actually itself a GPT using instructions and a custom action. This is useful to read if you are building GPTs:
https://t.co/nPt9KXUCgF | https://twitter.com/i/web/status/1742930722766397932 | [
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] |
1742950048252723465 | Advanced RAG with Open-Source LLMs
This article by Akash M. is a fantastic resource for setting up small-to-big retrieval (with child chunks referring to bigger parent chunks) using open-source LLM (zephyr-7b) and embedding models (instructor-large).
A key idea is you can and… https://t.co/NbQz7KbrZd | https://twitter.com/i/web/status/1742950048252723465 | [
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1742950315278672040 | The RAG wave is here to stay, but in practice, it's hard to retrieve the right docs w/ embdings, & better IR models are hard to use!
Let's fix that: Introducing 🪤RAGatouille, a lib to train&use SotA retrieval model, ColBERT, in just a few lines of code!
https://t.co/VRHiGQl0Xv https://t.co/0EpOfV6UWn | https://twitter.com/i/web/status/1742950315278672040 | [
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] |
1742916202186633441 | Releasing NANO: a very small and lightweight detection network for drones that detects a few very simple classes in low-rez.
https://t.co/897dLIVToO https://t.co/moyB3suKqC | https://twitter.com/i/web/status/1742916202186633441 | [
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] |
1742943594242249023 | New short course on advanced retrieval for RAG (retrieval augmented generation)!
RAG fetches relevant documents to give context to an LLM. In Advanced Retrieval for AI with Chroma, taught by @trychroma founder @atroyn, you’ll learn:
(i) Query expansion using an LLM to rewrite… https://t.co/7MHX4HT09V | https://twitter.com/i/web/status/1742943594242249023 | [
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1742659726004101156 | New blog post: Using llamafile for local dev for an Azure OpenAI Python web app:
https://t.co/T8DAvZRdo4 | https://twitter.com/i/web/status/1742659726004101156 | [
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1742851744940384287 | Nieuwe routeplanner voor landelijke gebieden.
#delijn #wateroverlast https://t.co/ECd02UiTMe | https://twitter.com/i/web/status/1742851744940384287 | [
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1742703399081271555 | Hyper-optimize your garden using LlamaIndex!
https://t.co/ICtydhh8L4 is a fascinating side project from Danielle Heymann which uses a genetic algorithm alongside an LLM to optimize where you plant for which plants grow best alongside each other.
It uses local models running on… https://t.co/OmexF5U60r | https://twitter.com/i/web/status/1742703399081271555 | [
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1742706539989741745 | My talk on the past and future of seaborn from #positconf2023 is now available on YouTube https://t.co/5ObIFQwsMT | https://twitter.com/i/web/status/1742706539989741745 | [
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] |
1742640557422268834 | Microsoft breaks the SOTA for embedding models!
Using quick & simple training run synthetic data.
---
Paper: https://t.co/kxGiifICvx
Model: https://t.co/XNBP6DV6H9
Dataset (by @andersonbcdefg): https://t.co/akagzoD4W4
---
Semantic search models are very hard to get "right".… https://t.co/uwmFp5NtkP | https://twitter.com/i/web/status/1742640557422268834 | [
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1742580327057433002 | Naval's Laws for Life: https://t.co/IpxVfqkfxt | https://twitter.com/i/web/status/1742580327057433002 | [
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] |
1734920232173539796 | I am reviewing my favorite papers of the year, and I wanted to highlight "Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling" (https://t.co/JVZFbXhDGm).
Along with the paper, the researchers originally released 8 LLMs ranging from 70M to 12B… https://t.co/qIIOGRjBQk | https://twitter.com/i/web/status/1734920232173539796 | [
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1742584137750344192 | New course in partnership with @trychroma, Advanced Retrieval for AI!
In this course, you'll dive into advanced techniques that use a large language model (LLM) to get the most relevant context for a Retrieval Augmented Generation system.
Join now: https://t.co/uceZGhQ6MQ https://t.co/EHJ8Rm3ZRs | https://twitter.com/i/web/status/1742584137750344192 | [
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] |
1742582340411388265 | ❓The Question Improver Agent
This agent by @sockcymbal is an LLM-driven "tool for thought" designed to elevate the quality and depth of user-posed questions
🥇It won 1st place at the AI for Thought Hackathon at @AGIHouseSF
It has some great prompts!
https://t.co/YGk6RUqClI https://t.co/3xq3UN0Akg | https://twitter.com/i/web/status/1742582340411388265 | [
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] |
1742214328030511527 | We have open sourced the entire code here:
https://t.co/DBZDiylXd2
Do check it out and share your feedback in the comments. | https://twitter.com/i/web/status/1742214328030511527 | [
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1742555240363074000 | E5 mistral-7b: New technique and SOTA model for text embeddings by Microsoft
Paper: https://t.co/XFfJ5GrPKr
Model: https://t.co/HEKzqU33fm
Leaderboard: https://t.co/x0pzUG5V2L
- Only trained on synthetic data (for 93 langs)
- Decoder-only LLM 🤯(Mistral 7B fine-tune)
- Tops… https://t.co/8pD6YWwsop | https://twitter.com/i/web/status/1742555240363074000 | [
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1742550168560382047 | Beam: Run Python functions on the cloud in seconds
https://t.co/DJIWHBCR57 | https://twitter.com/i/web/status/1742550168560382047 | [
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1742459576534843670 | gpt-engineer-org / gpt-engineer
Specify what you want it to build, the AI asks for clarification, and then builds it.
https://t.co/iDEkFst5ZO | https://twitter.com/i/web/status/1742459576534843670 | [
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1742324342371897497 | Colab AI: Soon every moderately used app will include an AI assistant interface. The future is looking ☀️ https://t.co/Wj0xBmRq9q | https://twitter.com/i/web/status/1742324342371897497 | [
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] |
1742448171546997046 | Oxford University mathematician reveals how to avoid spilling your coffee ☕️
[📹 Oxford Sparks]
https://t.co/p2kE3dTdkd | https://twitter.com/i/web/status/1742448171546997046 | [
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1742286947781914936 | Trying to create a language model that understands your own custom data? Here are techniques you can use to create a “specialized” LLM, ordered in terms of the amount of complexity/compute involved…
TL;DR: When trying to solve problems with language models, we should start… https://t.co/ZF4ZPRVdKd | https://twitter.com/i/web/status/1742286947781914936 | [
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1742214168789254646 | 🥳Bruxelles Environnement vous souhaite une bonne année !
🎇Leefmilieu Brussel wenst jullie een gelukkig nieuwjaar! https://t.co/UnB3aaJt5R | https://twitter.com/i/web/status/1742214168789254646 | [
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1742215486174044506 | MIT engineers designed an ingestible capsule that vibrates within the stomach, creating an illusory sense of fullness and reducing appetite. The pill could offer a minimally invasive, cost-effective way to treat obesity. https://t.co/HA17LAXuVe https://t.co/ZOy0DKGJXB | https://twitter.com/i/web/status/1742215486174044506 | [
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] |
1742190352377754053 | IP Adapter FaceID is amazing!
1. Upload a photo of your face
2. Write a prompt
3. Generate new images in seconds!
https://t.co/Lz4CWcct4c
No training (like Dreambooth) involved; everything is done in inference! https://t.co/3s8VFTux7v | https://twitter.com/i/web/status/1742190352377754053 | [
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1742036923668132280 | Better data = better AI. That's why I've spent the last 3 months on:
- Marker - fast, accurate PDF to markdown (5k GH ⭐️s)
- Texify - SOTA math to LaTeX OCR
- Libgen to txt - get 3TB of HQ data
- Textbook quality - HQ synth data
Find them at https://t.co/Mz8ySlTiiJ . | https://twitter.com/i/web/status/1742036923668132280 | [
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1741950691273249082 | @francoisfleuret Is this close to what you want? https://t.co/5JR3uDUMxT | https://twitter.com/i/web/status/1741950691273249082 | [
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] |
1742034428619096327 | Microsoft announces Improving Text Embeddings with Large Language Models
paper page: https://t.co/TWgQ2P9YDk
introduce a novel and simple method for obtaining high-quality text embeddings using only synthetic data and less than 1k training steps. Unlike existing methods that… https://t.co/Uia0Zjk0p8 | https://twitter.com/i/web/status/1742034428619096327 | [
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1741897575836315920 | Principled Instructions for LLMs
Nice set of guiding principles to improve and enhance the quality and reliability of LLM responses.
Shows the effectiveness of the principled instructions and prompt designs across model sizes and scenarios.
Tested on Llama 1/2 (7B, 13B, and… https://t.co/JRiRVa2smB | https://twitter.com/i/web/status/1741897575836315920 | [
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1741926266087870784 | I compiled a prompt engineering "best practices and tricks" doc 😀
Created based on OpenAI @isafulf's prompt engineering talk at @NeurIPSConf and enriched with more details, examples, and tips.
I focused on making the document as comprehensive and concise as possible, and it… https://t.co/nPjux6JSzO | https://twitter.com/i/web/status/1741926266087870784 | [
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] |
1741989284788441528 | One step beyond using RAG for search and retrieval is to use RAG for workflow automation.
Here's a great repo (by lucastonon) that combines RAG with LLM function calling to take the steps necessary to place a food delivery order 🚐🥗.
Let the agent take actions for you, and… https://t.co/C3W0ljJIdS | https://twitter.com/i/web/status/1741989284788441528 | [
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1741843248132329739 | FastUI-Chat is now available as pypi package:
just `pip install fastui-chat` and quickly spin up an interface for your @LangChainAI chatbots! https://t.co/Vb2J0QU2Mq | https://twitter.com/i/web/status/1741843248132329739 | [
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1741802368092561498 | Naval Ravikant on the importance of hiring high-agency people
Naval defines agency as:
“People who just solve problems without even being asked to solve the problem—they identify the problem, they go solve it, they don’t even necessarily have to update you every step of the… https://t.co/imfAhWJSe1 | https://twitter.com/i/web/status/1741802368092561498 | [
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1741834512038826462 | Another Microsoft Research paper introducing a new CodeOcean dataset without actually releasing a dataset. 🤦🏻♂️ https://t.co/BwlGfKpUxY | https://twitter.com/i/web/status/1741834512038826462 | [
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] |
1741868363603955995 | ⭐️Open-Source RAG Chatbot Builder
One of the most popular use cases for GPTs is uploading it some data and creating a chatbot over that data💬
Did you know there's an open-source solution that can do the same thing built entirely in typescript?
⭐️@dialoqbase by @n4ze3m⭐️
Uses… https://t.co/JNFfAOMomQ | https://twitter.com/i/web/status/1741868363603955995 | [
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1741481276677509419 | Abstract writing template by Nature #AcademicTwitter https://t.co/lrfeZSL9Ck | https://twitter.com/i/web/status/1741481276677509419 | [
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1741798196026773711 | learn-generative-ai - Learn Cloud Applied Generative AI Engineering (GenEng) using OpenAI, Gemini, Streamlit, Containers, Serverless, Postgres, LangChain, Pinecone, and Next.js https://t.co/JSZKDSiDOS | https://twitter.com/i/web/status/1741798196026773711 | [
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1741809869957579208 | I'm creating a Twitter/X list of official Python conference accounts: https://t.co/30e6TJfik9
What accounts should I add here? | https://twitter.com/i/web/status/1741809869957579208 | [
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1741729642195869815 | Bridge Truck test https://t.co/a0LYWQjbMV | https://twitter.com/i/web/status/1741729642195869815 | [
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] |
1741612289432998051 | To round off the year, I pulled together some notes on "Stuff we figured out about AI in 2023"
We figured out a lot! https://t.co/KDdDwCAYUD | https://twitter.com/i/web/status/1741612289432998051 | [
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1741808515260338222 | Happy New Year to all! 🎉 Starting 2024 with a bang, we've just posted the final sessions of our DataTopics #RootsConf interviews. Delve into the dynamic world of AI and Data Insights with our latest podcast episode. 🤖💼 🎧 #DataTalks #TechTrends2024
https://t.co/n7H2J3qa81 https://t.co/08XKPayYX7 | https://twitter.com/i/web/status/1741808515260338222 | [
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1741613987912138821 | [AAAI 2024] Official repository for UCMCTrack: Multi-Object Tracking with Uniform Camera Motion Compensation. UCMCTrack ranks first on MOT17 without using any appearance cues.
https://t.co/dauWq30Md2
Tracking Vehicles with Moving Camera https://t.co/rcB9XFNJVC | https://twitter.com/i/web/status/1741613987912138821 | [
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1741775541869359566 | Happy New Year from the Dataroots team! 🎉🌱 We're kicking off 2024 with the release of our latest and final #RootsConf podcast episode. Join us as we explore the dynamic realms of AI and BI 🤖💼🎧 #TechTrends2024 https://t.co/oMN8C0NiAO https://t.co/c52X4HgGIM | https://twitter.com/i/web/status/1741775541869359566 | [
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] |
1741230824916414923 | “The people who wave their hands and calmly & optimistically say, ‘we're solving the alignment problem’, their very statement of that is a sign they don't have an appreciation of how deep the problem is.”
—@DanielDennett https://t.co/AJIIHpkwgp | https://twitter.com/i/web/status/1741230824916414923 | [
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] |
1741493200278175746 | tinyzero
Easily train AlphaZero-like agents on any environment you want!
https://t.co/9MnBQbjHIc | https://twitter.com/i/web/status/1741493200278175746 | [
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] |
1741503733115052360 | 🏗️Building your own RAGs with LangChain
A practical tutorial from Madhav Thacker (Senior Data Scientist @Shopify) on how to build your own RAGs with @LangChainAI⛏️
Thanks to our friends at @arizeai for organizing!
YouTube: https://t.co/Nd65HXYxZw https://t.co/ewJZJRtzHy | https://twitter.com/i/web/status/1741503733115052360 | [
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1741441997066387487 | mixtral-offloading - Run Mixtral-8x7B models in Colab or consumer desktops https://t.co/ptIE8SZWrG | https://twitter.com/i/web/status/1741441997066387487 | [
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1741476430796439720 | Not always easy to let #chatgpt or #bard generate correctly working code. I gave up for both after about 15+ prompts to correct. Both make earlier mistakes over again and fix one thing break another. I am quicker doing it myself rather than tweaking prompts over and over.... | https://twitter.com/i/web/status/1741476430796439720 | [
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1741086686707106235 | I've read a ton of research papers this year. And to conclude this eventful year in AI, I've compiled a selection of 10 noteworthy papers from 2023 that I am discussing in my new article: https://t.co/oK8S6ubDBI
To get a sneak peak, I'm covering:
- insights into LLM training… | https://twitter.com/i/web/status/1741086686707106235 | [
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1741072553173508362 | 🧠 #Microsoft ha lanciato uno strumento definito GPT-RAG, un sistema che permette di usare un #LLM per un modello #RAG (Retrieval Augmented Generation) in un contesto aziendale.
#AI #IntelligenzaArtificiale #GenAI #GenerativeAI
https://t.co/4BvgfhKnzF | https://twitter.com/i/web/status/1741072553173508362 | [
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] |
1741053245756211560 | The NY Times is asking that *ALL* LLMs trained on Times data be destroyed.
That includes GPT 3 and 4, Claude, Mistral, Llama/Llama 2 and pretty much any other model in existence.
I call on @facebook, @cohere, @AnthropicAI, @MistralAI, @huggingface, @google and everyone else… https://t.co/mALscSi81O | https://twitter.com/i/web/status/1741053245756211560 | [
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1741004760197775551 | Understanding Parquet, Iceberg and Data Lakehouses at Broad
https://t.co/q84iCqmdb2 | https://twitter.com/i/web/status/1741004760197775551 | [
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1741252334016188642 | Ok and now.. to AI stuff 🫡 Of course the first thing I'll install is @LMStudioAI and Mixtral 😍
https://t.co/BTKRtK6BpZ
Enabling the metal feature makes Mixtral run super duper fast! | https://twitter.com/i/web/status/1741252334016188642 | [
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] |
1741269248079753637 | ICYMI here's a fantastic guide by @kiennt_ on how you can process/query embedded tables in your RAG pipeline with @llama_index + @UnstructuredIO.
Interestingly, it outperforms a multimodal approach! (GPT-4V + Microsoft Table Transformer)
Check it out: https://t.co/FwLEWqe1lE https://t.co/3PdMjiY65T | https://twitter.com/i/web/status/1741269248079753637 | [
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] |
1741270281203978588 | There's a few ways to process complex PDFs for RAG:
1. Python libraries + OCR
2. Cloud Platform providers
3. Multi-modal (screenshot with a model like GPT-4V or Gemini, extract output).
@kiennt_ found that using @UnstructuredIO (which falls somewhere in between category 1 and… https://t.co/IL2kQFUjTo | https://twitter.com/i/web/status/1741270281203978588 | [
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] |
1741040229983896041 | R² is a widely used measure of fit, but for many analysts, it is just a number.
They believe high R² ➡️ Good predictions.
This is not always true!
Now I will clarify 🔽 https://t.co/jvVDAbaLZX | https://twitter.com/i/web/status/1741040229983896041 | [
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] |
1740834570952720630 | Highly recommend. I was familiar with a lot of the events covered in the book but it put it together cohesively - for a very complex industry - and covers a lot ground on modern geopolitics between the US, China, and Taiwan
Summary:
- history of the semiconductor industry in… https://t.co/gTIWLUejbQ | https://twitter.com/i/web/status/1740834570952720630 | [
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1740871263638159710 | How to use Microsoft's phi-2 as a copilot in VSCode and ask for code explanation to run phi-2 in Python🤯
In this example, I'm using Ollama to download the model, and then I just need to type the downloaded model's name into CodeGPT to use it as Copilot in VSCode.
The most… https://t.co/6jV0Kiz0r1 | https://twitter.com/i/web/status/1740871263638159710 | [
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1740778991064408450 | A holy grail for agents is combining parallel function execution with query planning capabilities.
The recent LLMCompiler paper by Kim et al. (@berkeley_ai ) does exactly that, and I’m excited to introduce an integration with @llama_index 🔌
Here’s how it works 👇
1. Plan:… https://t.co/BDEXWuzcks https://t.co/4DKATrxF74 | https://twitter.com/i/web/status/1740778991064408450 | [
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] |
1740781595328712726 | MoE on Google Colab? 🔥
"Using this strategy, we build can run Mixtral-8x7B with mixed quantization on desktop hardware and free-tier Google Colab instances." 🤯
Paper - https://t.co/02MW4WB04f https://t.co/3pftdQ7fK8 | https://twitter.com/i/web/status/1740781595328712726 | [
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1740724707148853343 | Trained my first sparse-embed model with 25K triplets on my M1 using torch mps support. Game changer for toy projects https://t.co/RHPw5krfoW https://t.co/nEttEW9uH4 | https://twitter.com/i/web/status/1740724707148853343 | [
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] |
1740790264925409578 | Mixtral-8x7B on free Google Colab just dropped!
TL;DR:
- Novel offloading trick & mixed quantization.
- Managed to run Mixtral-8x7B on free-tier Google Colab.
Notebook: https://t.co/2AlJEFf4UP
Code: https://t.co/8HkWIt8Ajh
Paper: https://t.co/d7DG2dUako https://t.co/kz0CYZnzMx | https://twitter.com/i/web/status/1740790264925409578 | [
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1740782873005752687 | The Human Side of Decision Making by Daniel Kahneman.
I highly recommend you read it.
A quick 10-pager. Here's a free PDF:
https://t.co/ccHq7Eo4Bz https://t.co/8udptFOShQ | https://twitter.com/i/web/status/1740782873005752687 | [
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1740704836440445113 | lightllm - LightLLM is a Python-based LLM (Large Language Model) inference and serving framework, notable for its lightweight design, easy scalability, and high-speed performance. https://t.co/5FfjTrkN6K | https://twitter.com/i/web/status/1740704836440445113 | [
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] |
1740532577973449202 | Creating your own Copilot in VSCode is ridiculously easy 🤯
In this video, using the CodeGPT extension in VSCode, I select OpenAI and the gpt-4-1106-preview model to review Python 🐍 code.
You can download the extension for FREE at this link: https://t.co/i8lfNwzZ3m https://t.co/L06cCNoewU | https://twitter.com/i/web/status/1740532577973449202 | [
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1740113292679622807 | Along those lines, updated my normcore LLM reading list https://t.co/4UH6zliUn2 | https://twitter.com/i/web/status/1740113292679622807 | [
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1740406372028531014 | My number 1 secret to exploratory data analysis? Correlation Funnel. In 5 minutes, I'll share 5 years of correlation analysis experience (and why I developed this state-of-the-art strategy). Let's go.
1. The Problem with Correlation: Early on in my data science career, I… https://t.co/rvQqUSFzSw | https://twitter.com/i/web/status/1740406372028531014 | [
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1740571256234057798 | MathPile: A Billion-Token-Scale Pretraining Corpus for Math
paper page: https://t.co/dA5OuHbT1M
High-quality, large-scale corpora are the cornerstone of building foundation models. In this work, we introduce MathPile, a diverse and high-quality math-centric corpus comprising… https://t.co/racvrDVqO9 | https://twitter.com/i/web/status/1740571256234057798 | [
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1740399329683124588 | The magic equation for metabolic health...
2000 / 2 🚶
####
Even more than absolute activity through the day, *frequent* background activity through the day is essential to metabolic health (& metabolic fitness!)
By simply getting up and moving on a regular basis, we give the… https://t.co/RmDe1nkSqs | https://twitter.com/i/web/status/1740399329683124588 | [
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1740375845057728680 | Python, C, Assembly – Faster Cosine Similarity
https://t.co/9biI4SCtlw | https://twitter.com/i/web/status/1740375845057728680 | [
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] |
1697091364633317707 | 画像も扱える言語モデルを使った、画像の異常検知手法 AnomalyGPT。
従来の手法は閾値調整が必要であったが、この手法は不要とのこと。few-shot（正常品が数点しかない）のスコアでSOTA。
論文：https://t.co/K5VwjXpPsQ
github:https://t.co/NaicqKk6Nh https://t.co/FcWgje3KeO | https://twitter.com/i/web/status/1697091364633317707 | [
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1717612387174687150 | Ever wondered which data black-box LLMs like GPT are pretrained on? 🤔
We build a benchmark WikiMIA and develop Min-K% Prob 🕵️, a method for detecting undisclosed pretraining data from LLMs (relying solely on output probs).
Check out our project: https://t.co/XBWfX0SjK5
[1/n] https://t.co/qyOIfQQf8n | https://twitter.com/i/web/status/1717612387174687150 | [
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] |
1738354427759612222 | RAG for LLMs
Great to see an overview of all the retrieval augmented generation (RAG) research that has been happening.
Should be a great read for the end of the year.
https://t.co/criuHgH5E8 https://t.co/E8LJPLGHWy | https://twitter.com/i/web/status/1738354427759612222 | [
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1740089842640531592 | I realized after posting that multi-tweet longform is user-hostile currently so I decided to convert and host it as a stand-alone markdown on my website too:
https://t.co/euU2LzRBcY
The "conversion" was a manual and work-intensive process. I wish that making simple markdown… | https://twitter.com/i/web/status/1740089842640531592 | [
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1740043484269367545 | Large Language Models are Not Stable Recommender Systems
Proposes a two-stage Bayesian framework that interacts between a probing stage to detect positional bias patterns in LMs and a recommendation stage that leverages entropy measurements.
📝https://t.co/NQrKzS3PYS https://t.co/reYGvlyljI | https://twitter.com/i/web/status/1740043484269367545 | [
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1740026904608915580 | pinning this onto my wall https://t.co/8JfAdEYcTQ | https://twitter.com/i/web/status/1740026904608915580 | [
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1740145401100149204 | There are a number of examples in the complaint around weighting showing not all brands and content are equal but I found the overweighting of WebText2 as a pretty good example of how "high-quality" content is given preference. /11 https://t.co/HEy7GmfMkw | https://twitter.com/i/web/status/1740145401100149204 | [
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] |
1739992893186585001 | The cycle of high-performing teams:
- Insatiable curiosity
- Strong bias for action
- Challenge & feedback
- Continual improvement
Great leaders power the cycle by:
- Holding high standards
- Developing the talent
- Removing friction
- Being decisive
The fastest team wins. | https://twitter.com/i/web/status/1739992893186585001 | [
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1739996475902345392 | One of my favorite studies ever was done by Google
They sought to discover the key characteristics of what makes a high-functioning team
The results had nothing to do w/ background, education, intelligence, talent or other similar malarky
Instead, they found these 5 key traits https://t.co/cPHgnqJty8 | https://twitter.com/i/web/status/1739996475902345392 | [
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] |
1740029203683144018 | Pandas too slow for window operations?
Blazing fast 🚀🚀🚀🚀🚀 window_ops Pandas functionality replacement package has got you covered.
#pandas #timeseries #forecasting
https://t.co/9QZp97o0AP https://t.co/FU3LRZ3ojA | https://twitter.com/i/web/status/1740029203683144018 | [
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1740102373371219981 | Hope you enjoyed the thread and, if you have, sharing is very much appreciated!
https://t.co/V2hjLTNvBU | https://twitter.com/i/web/status/1740102373371219981 | [
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1739998176390832400 | Incentives are the most important part for a manager to get right. https://t.co/vYZgCTIsAE | https://twitter.com/i/web/status/1739998176390832400 | [
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] |
1740115768891871434 | We have updated the code for our book "Bayesian Modeling and Computation in Python"! See https://t.co/EMI3DkjB7R
(be careful that the text still shows the original code) https://t.co/LBIOGLaW88 | https://twitter.com/i/web/status/1740115768891871434 | [
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] |
1740088787173011933 | Three teams have been dominating the LLM game for a while:
@MistralAI for sota LLMs 🦙
@LangChainAI for building with LLMs 🦜 @togethercompute for serving LLMs 🚀
If you know how, you can build things really, really fast now.
Brief intro and code walk-through for you 👇 | https://twitter.com/i/web/status/1740088787173011933 | [
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] |
1740097030729683381 | The most unknown most common shortcut I use on my MacBook is:
- Command+Option+Shift+4 to select a small part of the screen and copy it into clipboard as an image
- Command+Shift+4 to do the same, but save it as a file on Desktop as png
Life-changing. | https://twitter.com/i/web/status/1740097030729683381 | [
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1739778820272500781 | https://t.co/HfdsrDH1gq
run all!
I finally managed to prove what I was saying was possible, I managed to put together a logic for creating an instant language model, based on cut fasttext, using Wikipedia's standard vectors as a base, it can read 1 million tokens per second per… | https://twitter.com/i/web/status/1739778820272500781 | [
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Subsets and Splits