Here is an AI Puzzle!
When you solve it just use a 😎 emoji.
NO SPOILERS
A similar puzzle might have each picture that has a hidden meaning of summer, winter, fall, spring, and the answer would be seasons.

Its a little dated now (almost a year), so bottom right might be tough.

Thanks to @johko for the encouragement to post!

If you are interested in Knowledge Graphs, I invented all of this a year ago. It is a Encoder/Decoder that works with Knowledge Graphs. I am glad the world finally realizes this is useful a year later. I tried to tell you. I have not licensed any of the math. I own all of it. I do not have any plans to ever enforce the licensing but I like holding onto it.

https://huggingface.co/blog/TuringsSolutions/pfafresearch

huggingface.co

Probabilistic Fractal Activation Function (P-FAF) and Its Advantages Over Traditional Word Vectorization

A Blog post by Richard A Aragon on Hugging Face

Build an agent with tool-calling superpowers 🦸 using Transformers Agents
Authored by: Aymeric Roucher

This notebook demonstrates how you can use Transformers Agents to build awesome agents!

What are agents? Agents are systems that are powered by an LLM and enable the LLM (with careful prompting and output parsing) to use specific tools to solve problems.

These tools are basically functions that the LLM couldn’t perform well by itself: for instance for a text-generation LLM like Llama-3-70B, this could be an image generation tool, a web search tool, a calculator…

What is Transformers Agents? it’s an extension of our transformers library that provides building blocks to build your own agents! Learn more about it in the documentation.https://huggingface.co/learn/cookbook/agents

huggingface.co

Build an agent with tool-calling superpowers 🦸 using Transformers Agents - Hugging Face Open-Source AI Cookbook

We’re on a journey to advance and democratize artificial intelligence through open source and open science.

RT-DETR - probably the best combination of speed, accuracy, and license for real-time object detection.

I just released a blog post tutorial on fine-tuning RT-DETR on a custom dataset.

shoutout to
@NielsRogge
for all the help!

link: https://blog.roboflow.com/train-rt-detr-custom-dataset-transformers/

↓ key takeawayshttps://pbs.twimg.com/card_img/1821575792448491520/AfYz_BzY?format=jpg&name=small

Roboflow Blog

How to Train RT-DETR on a Custom Dataset with Transformers

Learn how to train RT-DETR on a custom dataset using the Transformers library.

Can we train a VLM to 𝐩𝐫𝐞𝐟𝐞𝐫?

This is now possible, thanks to the new TRL/DPO support for VLMs! 🎉

As an example, we've trained a model to reduce hallucinations.

Check out:
📰 Blog post: https://huggingface.co/blog/dpo_vlm
🐙 TRL: https://github.com/huggingface/trl

Thanks to
@mervenoyann
,
@vwxyzjn
and
@krasul
who helped me with this work!

Agent for self-correcting Text-to-SQL 🧑‍💻

What if the query generated by your Text-to-SQL pipeline is correct SQL but returns wrong results?
👉 We need to add a critique step

✅ That's very simple with an agent!
Check out the notebook!https://huggingface.co/learn/cookbook/agent_text_to_sql

huggingface.co

Agent for text-to-SQL with automatic error correction - Hugging Face Open-Source AI Cookbook

We’re on a journey to advance and democratize artificial intelligence through open source and open science.

The vision language model in this video is 0.5B and can take in image, video and 3D! 🤯
Llava-NeXT-Interleave is a new vision language model trained on interleaved image, video and 3D data

keep reading ⥥⥥

Mistral 7B running on Mac, powered by CoreML! ⚡️

Heavily optimised with the latest updates from WWDC like stateful buffers, ML Tensors and 4-bit palletisation!

Try it today with swift-transformers and chat-ui! 🔥

Llama 405B is here, and it comes with more than expected! 🚨
@AIatMeta
Llama 3.1 comes in 3 sizes, 8B, 70B, and 405B, and speaks 8 languages! 🌍 Llama 3.1 405B matches or beats the Openai GPT-4o across many text benchmarks.

New and improvements of 3.1✨:
🧮 8B, 70B & 405B

Meta Llama 3.1 405B, 70B & 8B are here - Multilingual & with 128K context & Tool-use + agents! Competitive/ beats GPT4o & Claude Sonnet 3.5 unequivocally the best open LLM out there!🐐

Bonus: It comes with a more permissive license, which allows one to train other LLMs on its high-quality outputs 🔥

Llama-405b runs on cpu.
Getting 1.67 token/s output,
10 tokens/words per second input without a gpu.
Slow but usable, summarizing a 2 hour long medtech discussion with it. Will upload 2bit optimized etc here
https://huggingface.co/nisten/meta-405b-instruct-cpu-optimized-gguf/tree/main

Really nice development by
@nvidia
and
@HuggingFace


Launch of Hugging Face Inference-as-a-Service powered by NVIDIA NIM, a new service on the Hugging Face Hub

So, we can use open models with the accelerated compute platform, of NVIDIA DGX Cloud for inference serving.

Code is fully compatible with OpenAI API, allowing you to use the openai’ sdk for inference.

Note: You need access to an Organization with a Hugging Face Enterprise subscription to run Inference.

------

📌 So NVIDIA NIMs is an inference microservices that provide models as optimized containers — to deploy on clouds, data centers or workstations, giving them the ability to easily build generative AI applications for copilots, chatbots and more, in minutes rather than weeks.

📌 Maximizes infrastructure investments and compute efficiency. For example, running Meta Llama 3-8B in a NIM produces up to 3x more generative AI tokens on accelerated infrastructure than without NIM.

Google just dropped Gemma 2 2B! 🔥

> Scores higher than GPT 3.5, Mixtral 8x7B on the LYMSYS arena
> MMLU: 56.1 & MBPP: 36.6
> Beats previous (Gemma 1 2B) by more than 10% in benchmarks
> 2.6B parameters, Multilingual
> 2 Trillion tokens (training set)
> Distilled from Gemma 2 27B (?)
> Trained on 512 TPU v5e

Smaller models beat orders of magnitude bigger models! 🤗
Very cool direction and so many cool ablations for distillation, too!

Kudos to Google & Deepmind for continuing their belief in open source and science! ⚡️

Gemma 2 2B running in a browser, powered by WebLLM & WebGPU! 🔥

100% local & on-device

In less than 24 hours, we've already got the model to the edge! ⚡️

Try it out on an HF space below:

NEW ARENA: Text to Speech Arena for Japanese by
@kotoba_tech
🔥

🔉Sound on

Outside of English, TTS evaluation is quite scarce. The Arena, allows one to test open source models against the closed source giants.

In the leaderboard you can compare open models like Bark, MOE-VITS, Kotoba Speech with closed source models like Google TTS, Open AI TTS and so on.

If you're a Japanese speaker then go check it out and help us find the best Japanese TTS model out there! 👀

Hugging News #114 :ilovepython: @everyone
Gemma
:google: Google releases Gemma 2 2B , ShieldGemma and Gemma Scope
:google: Gemma 2 2B in your browser thanks to MLC, 100% local and super fast with WebLLM + WebGPU!
:google: Gemma 2 2B running in a free Google Colab , powered by Transformers !
:google: Simple instructions to get started with the latest Gemma 2 models + llama.cpp !

Llama 3.1 405B released. 🎏 MagPie-Ultra is the first open dataset using Llama 3.1 405B-Instruct FP8 to generate 50,000 synthetic instruction pairs using the MagPie recipe and
@argilla_io
distilabel. It includes challenging instructions for coding math, data analysis, creative writing, advice seeking, or Brainstorming. ⚗️

MagPie datasets are created by prompting LLMs with "empty" prompts that consist only of starting special tokens, allowing the model to auto-regressively generate user queries and corresponding responses, which are then filtered to select high-quality data. 👨‍🎓

Note: The dataset is unfiltered but includes quality & difficulty scores, embeddings, topics, and safety scores from ArmorRM and LlamaGuard. 🛡

⚗️ Pipeline: https://huggingface.co/datasets/argilla/magpie-ultra-v0.1/blob/main/pipeline.py
🤗 Dataset: https://huggingface.co/datasets/argilla/magpie-ultra-v0.1

Dropping magpie-ultra-v0.1, the first open synthetic dataset built with Llama 3.1 405B.

Created with distilabel, it's our most advanced and compute-intensive pipeline to date.

https://huggingface.co/datasets/argilla/magpie-ultra-v0.1

Let's dig into the details!