AI: LLMs, platforms (hardware) and projects

(AI in Business) - Applied Artificial Intelligence

https://www.bookdepository.com/Applied-Artificial-Intelligence-Where-AI-Can-Be-Used-Business-Francesco-Corea/9783319772516?ref=grid-view&qid=1592465308969&sr=1-1 

Web element detection

OpenDILabCommunity/webpage_element_detection · Hugging Face

Layout parser - doc ingester

https://layout-parser.github.io/

YOLO Object Detection
https://www.v7labs.com/blog/yolo-object-detection

Data generation - Langchain

Synthetic data generation | 🦜️🔗 Langchain

DistilBERT: Faster version of BERT

[1910.01108] DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter (arxiv.org)

DBRX - a 'mixture of experts' LLM

Introducing DBRX: A New State-of-the-Art Open LLM | Databricks

Hermes-2 Mistral 7B - good for "Function Calling" (so LLM delegates creating/modifying an app, to the client)

https://huggingface.co/NousResearch/Hermes-2-Pro-Mistral-7B

multimodal LLMs

HuggingFaceM4/idefics2-8b-chatty · Hugging Face

HuggingFaceM4/idefics2-8b · Hugging Face

HPT 1.5 Air: A New Open-Sourced 8B Multimodal LLM with Llama 3

Datasets - Roboflow (like HuggingFace for data)

https://public.roboflow.com/

Accelerating Hugging Face Transformers with AWS Inferentia2

AWS Inferentia2 (inf2 AWS instances - cheaper than g5)

Prompt hacking and testing

HackAPrompt

https://github.com/Giskard-AI/giskard

Token counts - for cost estimates

https://huggingface.co/Xenova/claude-tokenizer

https://huggngface.co/spaces/Xenova/the-tokenizer-playground

https://docs.aws.amazon.com/bedrock/latest/userguide/model-customization-prepare.html

LLM powered autonomous agents

https://lilianweng.github.io/posts/2023-06-23-agent/

Building a NN from scratch

MNIST Neural Network From Scratch (kaggle.com)

Data science and data trends (via ML) Python libraries

https://www.marktechpost.com/2024/05/15/10-python-packages-revolutionizing-data-science-workflow/

evaluating LLMs

prometheus-2

https://github.com/prometheus-eval/prometheus-eval

LLM GitHub accelerator projects 

https://github.blog/2024-05-23-2024-github-accelerator-meet-the-11-projects-shaping-open-source-ai/

distilbert - can train it to classify intent or toxic

https://huggingface.co/distilbert/distilbert-base-uncased

Prompt engineering guides

- The Prompt Report - https://arxiv.org/pdf/2406.06608

- https://www.promptingguide.ai

- Anthropic - [Answer Key] Anthropic's Prompt Engineering Interactive Tutorial [PUBLIC ACCESS] - Google Spreadsheets

- Claude Prompt Engineering & Function Calls

- Claude - Tool Use

Tiny Multi-modal LLM - regions + OCR

microsoft/Florence-2-large · Hugging Face

AWS Bedrock Tips

- batch inference

https://docs.aws.amazon.com/bedrock/latest/userguide/batch-inference.html

AWS Inferentia - speculative decoding

Faster LLMs with speculative decoding and AWS Inferentia2 | AWS Machine Learning Blog

AWS Prompt Routing and prompt caching

- can auto pick an LLM?

Reduce costs and latency with Amazon Bedrock Intelligent Prompt Routing and prompt caching (preview) | AWS News Blog

AI Papers, Books and Datasets

Learning Transferable Visual Models From Natural Language Supervision = 
https://arxiv.org/abs/2103.00020

Free Datasets

https://www.v7labs.com/blog/best-free-datasets-for-machine-learning

UI Modelling - annotated UIs

https://uimodeling.github.io/

Datasets - Roboflow (like HuggingFace for data)

https://public.roboflow.com/

MoE (Mixture of Experts)

LLM Mixture of Experts Explained (tensorops.ai)

An Introduction to Vision-Language Modeling

Vision AI - VLMs and CNNs
- Vision Language Models
- Convolutional Neural Networks

https://arxiv.org/pdf/2405.17247

Multi-modal LLMs

Cambrian-1: A Fully Open, Vision-Centric Exploration of Multimodal LLMs (cambrian-mllm.github.io)

Movies: rated [for collaborative filtering]

MovieLens Latest Datasets | GroupLens

The Society of Mind

Book by Marvin Minsky

AI Engineering[Book] [O'Reilly] [Zeki recommends]

same author: Designing Machine Learning Systems- DMLS focuses on building applications on top of traditional ML models, which involves more tabular data annotations, feature engineering, and model training