In this video, I look at olmOCR, the OpenOCR system from Allen AI.
Colab: https://dripl.ink/HpaK4
Blog: https://olmocr.allenai.org/blog
macOS ver: https://jonathansoma.com/words/olmocr-on-macos-with-lm-studio.html
For more tutorials on using LLMs and building agents, check out my Patreon
Patreon: https://www.patreon.com/SamWitteveen
Twitter: https://x.com/Sam_Witteveen
🕵️ Interested in building LLM Agents? Fill out the form below
Building LLM Agents Form: https://drp.li/dIMes
👨💻Github:
https://github.com/samwit/llm-tutorials
⏱️Time Stamps:
00:00 Intro
00:31 Allen AI Blog
01:20 olmOCR Blog
02:08 olmOCR Hugging Face
04:52 olmOCR GitHub
05:41 Demo
05:59 Running olmOCR on macOS with LM Studio
Structured data extraction from unstructured content using LLM schemas
LLM 0.23 is out today, and the signature feature is support for schemas—a new way of providing structured output from a model that matches a specification provided by the user. …
For some time, I’ve argued that a common conception of AI is misguided. This is the idea that AI systems like large language and vision models are individual intelligent agents, analogous to human agents. Instead, I’ve argued that these models are “cultural technologies” like writing, print, pictures, libraries, internet search engines, and Wikipedia.
I tested NotebookLM against my small, private LLM research assistant. The difference is amazing.
I tested my local, small LLM connected to my Zotero database in Open WebUI against NotebookLM. The results were really, truly surprising.
Please Like and Subscribe to support the channel! @LearnMetaAnalysis
I tested Granite 3.1-2b and Granite 3.1-8b vs NotebookLM. I am now more impressed than ever!
If you find Granite hallucinating with the default temperature setting, I've had good results setting it at about .20.
Results from the testing: https://docs.google.com/spreadsheets/d/1nW7B_A7fzayaMancYwU89ApOOfLG5WOefzstdL_fg-Y/edit?usp=sharing
Learn how to set up your own local, private research assistant like Dwayne with no coding: https://youtu.be/b2BSZfOtD_w
The original RAG template I adapted (my modified version is in the google sheet): https://medium.com/@kelvincampelo/how-ive-optimized-document-interactions-with-open-webui-and-rag-a-comprehensive-guide-65d1221729eb
Tutorials and how-to guides:
Connect a LLM to your Zotero (or any other local folder): https://youtu.be/b2BSZfOtD_w
Conventional meta-analysis: https://www.youtube.com/playlist?list=PLXa5cTEormkEbYpBIgikgE0y9QR7QIgzs
Three-level meta-analysis: https://www.youtube.com/playlist?list=PLXa5cTEormkHwRmu_TJXa7fSb6-WBXXoJ
Three-level meta-analysis with correlated and hierarchical effects and robust variance estimation: https://www.youtube.com/playlist?list=PLXa5cTEormkEGenfcnp9X5dQUhmm7f9Jp
Want free point and click (no coding required) meta-analysis software? Check out Simple Meta-Analysis: https://learnmeta-analysis.com/pages/simple-meta-analysis-software
Tired of manually extracting data for systematic review and meta-analysis? Check out AI-Assisted Data Extraction, a free package for R! https://youtu.be/HuWXbe7hgFc
Free ebook on meta-analysis in R (no download required): https://noah-schroeder.github.io/reviewbook/
Visit our website at https://learnmeta-analysis.com/
0:40 Setup
4:05 Results
Described as GenAIs greatest flaw, indirect prompt injection is a big problem, Mike Pound from University of Nottingham explains how it is like SQL Injection...
Comparison and ranking the performance of over 30 AI models (LLMs) across key metrics including quality, price, performance and speed (output speed - tokens per second & latency - TTFT), context window & others.
How to improve the local LLM connected to Zotero for stunning results. So easy even I can do it.
Learn how to make simple changes that help your LLM chat with Zotero like a pro! I’m getting well written, well-cited results from a 2b parameter LLM.
Please Like and Subscribe to support the channel! @LearnMetaAnalysis
Embedding result testing: https://docs.google.com/spreadsheets/d/1P3rOLEO_NtCUYxaFIVaVZfMv4BOkQb3w/edit?usp=sharing&ouid=111617079417577058774&rtpof=true&sd=true
Granite 3.1 Dense is my favorite LLM for this setup right now, it's available in 2b and 8b versions for ollama - https://ollama.com/library/granite3.1-dense:2b
Snowflake Arctic Embed 2 has performed well for me so far as an embedding model: https://ollama.com/library/snowflake-arctic-embed2
MTEB leaderboard to see what embedding models perform well at different tasks: https://huggingface.co/spaces/mteb/leaderboard
How to connect a LLM to Zotero - https://youtu.be/b2BSZfOtD_w
I generally prefer local, private LLMs, but if you need large SOTA models like ChatGPT, Claude, Deepseek, Gemini, or Grok, check out ChatLLM - My 3 month review of ChatLLM: https://youtu.be/_Z3nLKvTbGc
Tutorials and how-to guides:
Conventional meta-analysis: https://www.youtube.com/playlist?list=PLXa5cTEormkEbYpBIgikgE0y9QR7QIgzs
Three-level meta-analysis: https://www.youtube.com/playlist?list=PLXa5cTEormkHwRmu_TJXa7fSb6-WBXXoJ
Three-level meta-analysis with correlated and hierarchical effects and robust variance estimation: https://www.youtube.com/playlist?list=PLXa5cTEormkEGenfcnp9X5dQUhmm7f9Jp
Want free point and click (no coding required) meta-analysis software? Check out Simple Meta-Analysis: https://learnmeta-analysis.com/pages/simple-meta-analysis-software
Tired of manually extracting data for systematic review and meta-analysis? Check out AI-Assisted Data Extraction, a free package for R! https://youtu.be/HuWXbe7hgFc
Free ebook on meta-analysis in R (no download required): https://noah-schroeder.github.io/reviewbook/
Visit our website at https://learnmeta-analysis.com/
0:15 Knowledge
0:59 Help make this better
1:32 Modify ‘knowledge’ settings
5:46 Demo of results
7:22 Top K
11:25 Testing Different embeddings
13:25 Use # not models
14:45 Impatient people (like me!) start here
21:38 Example Results
OpenAI are rolling out their Deep research "agentic" research tool to their $20/month ChatGPT Plus users today, who get 10 queries a month. $200/month ChatGPT Pro gets 120 uses. Deep …
There were a number of announcements at GitHub Universe that impacted VS Code and Copilot users. This is a roundup of those announcements, including the much-anticipated Copilot Edits, Intent Detection, Model Selection, and Code Reviews. We'll also take a look at some of the amazing extensions created by the VS Code team, including using images for prompts with Vision for Copilot, analyzing CSV data with Data Analysis for Copilot, and GitHub Pull Requests.
🔎 Chapters:
00:00 GitHub Universe announcements
00:30 Demo - Copilot Edits
05:30 Demo - Intent Detection
09:06 Demo - Data Analysis for Copilot
10:19 Demo - GitHub Pull Requests Copilot Integration
13:10 Happy Coding
🔗 Links:
https://aka.ms/get-copilot
https://aka.ms/IntroducingCopilotEdits
Featuring: Rob Conery
#vscode #copilot #githubcopilot
Is AI progress slowing down? This piece by Arvind Narayanan and Sayash Kapoor is the single most insightful essay about AI and LLMs I've seen in a long time.
Redefining small LMs performance In this project, we develop technologies for creating, improving, and specializing small LMs (~10B parameters or less). Our research involves self-improvement strategies, feedback-driven teaching methods between large and small models and utilizing domain specific data to specialize LMs. We focus on using richer training signals for teaching small LMs to do […]