Scaling LLM Test-Time Compute Optimally can be More Effective than
  Scaling Model Parameters (2408.03314)

𝗚𝗼𝗼𝗴𝗹𝗲 𝗽𝗮𝗽𝗲𝗿 : 𝘀𝗰𝗮𝗹𝗶𝗻𝗴 𝘂𝗽 𝗶𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝗰𝗼𝗺𝗽𝘂𝘁𝗲 𝗯𝗲𝗮𝘁𝘀 𝟭𝟰𝘅 𝗹𝗮𝗿𝗴𝗲𝗿 𝗺𝗼𝗱𝗲𝗹𝘀 🚀

Remember scaling laws? These are empirical laws that say "the bigger your model, the better it gets". More precisely, "as your compute increases exponentially, loss decreases in a linear fashion". They have wild implications, suggesting that spending 100x more training compute would make you super-LLMs. That's why companies are racing to build the biggest AI superclusters ever, and Meta bought 350k H100 GPUs, which probably cost in the order of $1B.

But think of this : we're building huge reasoning machines, but only ask them to do one pass through the model to get one token of the final answer : i.e., we expend a minimal effort on inference. That's like building a Caterpillar truck and making it run on a lawnmower's motor. 🚚🛵 Couldn't we optimize this? 🤔

💡 So instead of scaling up on training by training even bigger models on many more trillions of tokens, Google researchers explored this under-explored avenue : scaling up inference compute.

They combine two methods to use more compute : either a reviser that iterated to adapt the model distribution, or generate N different completions (for instance through Beam Search) and select only the best one using an additional verifier model.

They use a Palm-2 model (released in May 23) on the MATH dataset : Palm-2 has the advantage of getting a low performance on MATH, but not zero, so that improvements will be noticeable.

And the results show that for the same fixed amount of inference compute:
💥 a smaller model with more effort on decoding beats a x14 bigger model using naive greedy sampling.

That means that you can divide your training costs by 14 and still get the same perf for the same inference cost!

Take that, scaling laws. Mark Zuckerberg, you're welcome, hope I can get some of these H100s.

Read the paper here 👉 https://huggingface.co/papers/2408.03314

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Just added the newly released xGen-MM v1.5 foundational Large Multimodal Models (LMMs) developed by Salesforce AI Research to my xGen-MM HF Space https://huggingface.co/spaces/maxiw/XGen-MM

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🚀 We will be generating a preference dataset for DPO/ORPO and cleaning it with AI feedback during our upcoming meetup!

In this session, we'll walk you through the essentials of building a distilabel pipeline by exploring two key use cases: cleaning an existing dataset and generating a preference dataset for DPO/ORPO. You’ll also learn how to make the most of AI feedback, integrating Argilla to gather human feedback and improve the overall data quality.

This session is perfect for you
- if you’re getting started with distilabel or synthetic data
- if you want to learn how to use LLM inference endpoints for **free**
- if you want to discover new functionalities
- if you want to provide us with new feedback

Sign up here: https://lu.ma/dt0c7jru

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ChatFrame-Persian is first expert Persian language model in Iran.

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🚀 How The Washington Post Uses AI to Empower Journalists 🔍📰

An exciting new example in the world of AI-assisted journalism! The Post has developed an internal tool called "Hayatacker" that's enhancing in-depth reporting. Here's why it matters:

🎥 What it does:
• Extracts stills from video files
• Processes on-screen text
• Labels objects in images

🗳 First big project:
Analyzed 745 Republican campaign ads on immigration (Jan-Jun 2024)

🤝 Human-AI collaboration:
• AI extracts and organizes data
• Reporters verify and analyze findings

🔎 Thorough approach:
• Manual review of all 745 ads
• Reverse image searches when context is lacking
• Cross-referencing with AdImpact transcripts

💡 Key insight from WaPo's Senior Editor for AI strategy Phoebe Connelly:
"The more exciting choice is putting AI in the hands of reporters early on in the process."

This tool showcases how AI can augment journalistic capabilities without replacing human insight and verification. It's a powerful example of technology enhancing, not replacing, traditional reporting skills.

👉 Read the full article and the methodology: https://www.washingtonpost.com/elections/interactive/2024/republican-campaign-ads-immigration-border-security/

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🤗 Serving Meta Llama 3.1 405B on Google Cloud is now possible via the Hugging Face Deep Learning Containers (DLCs) for Text Generation Inference (TGI)

In this post, we showcase how to deploy https://huggingface.co/meta-llama/Meta-Llama-3.1-405B-Instruct-FP8 on an A3 instance with 8 x H100 GPUs on Vertex AI

Thanks to the Hugging Face DLCs for TGI and Google Cloud Vertex AI, deploying a high-performance text generation container for serving Large Language Models (LLMs) has never been easier. And we’re not going to stop here – stay tuned as we enable more experiences to build AI with open models on Google Cloud!

Read the full post at https://huggingface.co/blog/llama31-on-vertex-ai

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@Blane187 could you please modify the title of your blogpost? content is cool, title could be nicer imo https://huggingface.co/blog/Blane187/wtf-is-rvc


🚀 Introducing Hugging Face Similar: a Chrome extension to find relevant datasets!

✨ Adds a "Similar Datasets" section to Hugging Face dataset pages
🔍 Recommendations based on dataset READMEs
🏗 Powered by https://huggingface.co/chromadb  and https://huggingface.co/Snowflake embeddings.

You can try it here: https://chromewebstore.google.com/detail/hugging-face-similar/aijelnjllajooinkcpkpbhckbghghpnl?authuser=0&hl=en.

I am very happy to get feedback on whether this could be useful or not 🤗

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ResShift 1-Click Windows, RunPod, Massed Compute, Kaggle Installers with Amazing Gradio APP and Batch Image Processing. ResShift is Efficient Diffusion Model for Image Super-resolution by Residual Shifting (NeurIPS 2023, Spotlight).

Official Repo : https://github.com/zsyOAOA/ResShift

I have developed a very advanced Gradio APP.

Developed APP Scripts and Installers : https://www.patreon.com/posts/110331752

Features

It supports following tasks:

Real-world image super-resolution

Bicubic (resize by Matlab) image super-resolution

Blind Face Restoration

Automatically saving all generated image with same name + numbering if necessary

Randomize seed feature for each generation

Batch image processing - give input and output folder paths and it batch process all images and saves

1-Click to install on Windows, RunPod, Massed Compute and Kaggle (free account)

Windows Requirements

Python 3.10, FFmpeg, Cuda 11.8, C++ tools and Git

If it doesn't work make sure to below tutorial and install everything exactly as shown in this below tutorial

https://youtu.be/-NjNy7afOQ0

How to Install on Windows

Make sure that you have the above requirements

Extract files into a folder like c:/reshift_v1

Double click Windows_Install.bat and it will automatically install everything for you with an isolated virtual environment folder (VENV)

After that double click Windows_Start_app.bat and start the app

When you first time use a task it will download necessary models (all under 500 MB) into accurate folders

If during download it fails, file gets corrupted sadly it doesn't verify that so delete files inside weights and restart

How to Install on RunPod, Massed Compute, Kaggle

Follow the Massed_Compute_Instructions_READ.txt and Runpod_Instructions_READ.txt

For Kaggle follow the notebook written steps

An example video of how to use my RunPod, Massed Compute scripts and Kaggle notebook can be seen

https://youtu.be/wG7oPp01COg


Put together a small repo showing how to go from making your own fine-tuning dataset w/ services like Groq & Together to publishing that model on ollama.

In my case I fine-tuned SmolLM-360M to be a better assistant for my Pi-Card (previous post) project.

Check it out!
https://github.com/nkasmanoff/ft-flow


Small-Text: Active Learning for Text Classification in Python (2107.10314)

🔔 Release: small-text v1.4.1

The new release contains some smaller bugfixes. Check it out!

Github: https://github.com/webis-de/small-text
Paper: https://huggingface.co/papers/2107.10314


🚀  We’re excited to launch Ghost 8B Beta (1608), a top-performing language model with unmatched multilingual support and cost efficiency.

Key Highlights:
- Superior Performance: Outperforms Llama 3.1 8B Instruct, GPT-3.5 Turbo, Claude 3 Opus, GPT-4, and more in winrate scores.
- Expanded Language Support: Now supports 16 languages, including English, Vietnamese, Spanish, Chinese, and more.
- Enhanced Capabilities: Improved math, reasoning, and instruction-following for better task handling.

With two context options (8k and 128k), Ghost 8B Beta is perfect for complex, multilingual applications, balancing power and cost-effectiveness.

🔗 Learn More: https://ghost-x.org/docs/models/ghost-8b-beta
https://huggingface.co/collections/ghost-x/ghost-8b-beta-668ead6179f93be717db4542

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Wrote a blog post with some ideas about prompt engineering

https://huggingface.co/blog/KnutJaegersberg/first-principles-prompt-engineering

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I'm excited to share a really cool milestone in my AI/LLM journey.

Brief backstory: Before diving into AI, I spent over a decade working in ecological fields such as the conservation corps, biodynamic farming, and natural habitat restoration. This background instilled in me a deep concern about the environmental impact of scaling AI without sustainable practices.

Driven by this concern, I've spent months planning and experimenting to make my AI work more eco-friendly. I'm thrilled to announce that I've successfully transitioned my entire operation to run on 100% sustainable solar power!

My current setup includes multiple linked Mac Pro tower desktops and custom code built from open-source libraries. While it's a bit experimental, this configuration is working great for my needs. All my LLM research, development, and client services now run exclusively on solar energy.

I'm curious if anyone else here has experimented with renewable energy for their LLM work?

For those interested in more details, I've written a brief blog post about this journey here https://medium.com/@betalabsllm/powering-the-future-be-ta-labs-revolutionary-100-solar-powered-ai-operation-444433e61d43

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How good are you at spotting AI-generated images?

Find out by playing Fake Insects 🐞 a Game where you need to identify which insects are fake (AI generated). Good luck & share your best score in the comments!

https://huggingface.co/spaces/victor/fake-insects

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So turns out I've been spreading a bit of misinformation when it comes to imatrix in llama.cpp

It starts true; imatrix runs the model against a corpus of text and tracks the activation of weights to determine which are most important

However what the quantization then does with that information is where I was wrong.

I think I made the accidental connection between imatrix and exllamav2's measuring, where ExLlamaV2 decides how many bits to assign to which weight depending on the goal BPW

Instead, what llama.cpp with imatrix does is it attempts to select a scale for a quantization block that most accurately returns the important weights to their original values, ie minimizing the dequantization error based on the importance of activations

The mildly surprising part is that it actually just does a relatively brute force search, it picks a bunch of scales and tries each and sees which one results in the minimum error for weights deemed important in the group

But yeah, turns out, the quantization scheme is always the same, it's just that the scaling has a bit more logic to it when you use imatrix

Huge shoutout to @compilade for helping me wrap my head around it - feel free to add/correct as well if I've messed something up

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I know a secret about knowledge graphs that the world doesn't! There are severe mathematical limitations to geometric fractals. It is classed as an 'unsolvable problem' in the mathematics world. There are currently ~1,000 mathematicians or so in the world that give this problem serious thought. You literally cannot solve it with geometric fractals. This is why I invented P-FAF, it uses calculus based fractals instead. I literally invented the math to make it work. I solved an 'unsolvable equation' to make the math work. You ONLY make the math work the way I did it in the end. I have never released the licensing commercially. Good luck!


Supercool Weekend Read🤖
Nvidia researchers achieved SOTA LLM compression metrics using pruning and knowledge distillation techniques.

Details on Techniques (Simplified):
They started off with a large pre-trained language model (15B params), then:

1. Estimated the importance of different parts of the model (neurons, attention heads, layers) using activation-based metrics on a small calibration dataset.

2. Pruned (remove) less important parts of the model to reduce its size.

3. Retrained the pruned model using knowledge distillation, where the original large model acts as a teacher for the smaller pruned model.

4. Used a lightweight neural architecture search to find the best configuration for the pruned model.

5. Repeated this process iteratively to create even smaller models.

Cool, giving it a try this weekend 😎
Code: https://github.com/NVlabs/Minitron
Paper: https://arxiv.org/abs/2407.14679
Demo: https://huggingface.co/spaces/nvidia/minitron


Added FLUX.1 pro/dev/schnell and AuraFlow v0.2 to  https://huggingface.co/spaces/fal/imgsys !!! Go play with it and get us some votez


NEW math-instruct model + dataset!

https://huggingface.co/ValiantLabs/Llama3.1-8B-Cobalt is our new math-instruct model.
Trained using a synthetic math-instruct dataset generated with Llama 3.1 405b. Find the dataset here: https://huggingface.co/datasets/sequelbox/Polytope

More to come soon :)


Introducing HelpingAI2-9B, an emotionally intelligent LLM.
Model Link : https://huggingface.co/OEvortex/HelpingAI2-9B
Demo Link: https://huggingface.co/spaces/Abhaykoul/HelpingAI2

This model is part of the innovative HelpingAI series and it stands out for its ability to engage users with emotional understanding.

Key Features:
-----------------

*   It gets 95.89 score on EQ Bench greather than all top notch LLMs, reflecting advanced emotional recognition.
*   It gives responses in empathetic and supportive manner.

Must try our demo: https://huggingface.co/spaces/Abhaykoul/HelpingAI2


Announcing another BIG data drop! This time it's ~275M images from Flickr https://huggingface.co/datasets/bigdata-pw/Flickr

Data acquisition for this project is still in progress, get ready for an update soon:tm:

In case you missed them; other BIG data drops include Diffusion1B https://huggingface.co/datasets/bigdata-pw/Diffusion1B - ~1.23B images and generation parameters from a variety of diffusion models and if you fancy practicing diffusion model training check out Dataception https://huggingface.co/datasets/bigdata-pw/Dataception - a dataset of over 5000 datasets in WebDataset format!

Requests are always welcome so reach out if there's a dataset you'd like to see!


✨ Feeling thankful...

🇮🇳 15th August, 2024; on India's 78th Independence Day

🎉 Crossed 100 followers on Hugging Face

🏆 Got LinkedIn Top Voice

🤖 AI has never been more exciting and I am here for it

👀 @clem Can I be a Hugging Face fellow now?


Improved ControlNet!
Now supports dynamic resolution for perfect landscape and portrait outputs. Generate stunning images without distortion—optimized for any aspect ratio!
...
https://huggingface.co/spaces/DamarJati/FLUX.1-DEV-Canny


As some of you know, I try to convert models to either fp32 or bf16 depending on theirs size before doing imatrix and quantization

Today I decided to see if that matters, and the results have me.. for lack of a better word, perplexed

My setup:

Mistral Nemo Instruct 2407
- convert to FP32, calculate imatrix, quantize to Q8_0 and Q4_K_M
- convert to FP16, calculate imatrix, quantize to Q8_0 and Q4_K_M

I calculated the kld base from the FP32 model:
`./llama-perplexity -m /models/Mistral-Nemo-Instruct-2407-f32.gguf -f /training_data/wikitext-2-raw/wiki.test.raw --kl-divergence-base /training_data/mistral-nemo-f32.kld -ngl 35 -fa -sm row`

then calculated the divergence itself for each like so:
`./llama-perplexity -m /models/Mistral-Nemo-Instruct-2407-Q8_0.gguf -f /training_data/wikitext-2-raw/wiki.test.raw --kl-divergence-base /training_data/mistral-nemo-f32.kld --kl-divergence -ngl 50 -fa -sm row`

Q4_K_M from fp16 and fp32 were similar, trading blows across statistics, odd since i expected fp32 to be strictly better but it's not

Q8_0 is where things get weird. Despite each file being slightly different size, and the sha256sum of course being different, they each get *completely identical* scores, down to 6 decimal places of precision on the statistics.

How is this possible? Is there something I don't understand about llama.cpp that makes it always convert to fp16 before it does quantization? Am I wasting time using FP32/BF16??

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https://huggingface.co/fal/AuraFlow-v0.3 is now here with support for different aspect resolutions (w/h up to 1536px!) and much nicer aesthetics! Make sure to install the latest diffusers to get support for it.

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𝗭𝗲𝗿𝗼-𝗺𝗮𝘁𝗵 𝗶𝗻𝘁𝗿𝗼 𝘁𝗼 𝗔𝗜 𝗵𝗶𝘀𝘁𝗼𝗿𝘆: 𝗳𝗿𝗼𝗺 𝘁𝗵𝗲 𝟭𝟵𝟱𝟬𝘀 𝘁𝗼 𝘁𝗼𝗱𝗮𝘆'𝘀 𝗟𝗟𝗠𝘀 📖

I wanted to structure my thinking about LLMs by going through their history since the 50s. This history is captivating, with the opposition between Connexionists (Rosenblatt, LeCun) and Symbolists, the first victories of "deep" neural networks, the revolution of Attention...

So I might have gone a bit too far! 😅

📝 I've made a long post summarizing the main stages of building LLMs: neural networks, optimization, backpropagation, attention layers...

✅ And I've made sure to keep it 100% horrible-latex-math-free: the technical stuff is conveyed in graphs only, so it should be accessible to really anyone, even your grandfather (I'm sending it to mine right now).

Read it here in english 👉 https://aymeric-roucher.github.io/brief-history-of-ai/
Pour le post en français 👉 https://aymeric-roucher.github.io/breve-histoire-de-l-ia/


Some personal and professional news ✨

I'm writing a book on ML metrics.

Together with Wojtek Kuberski, we’re creating the missing piece of every ML university program and online course: a book solely dedicated to Machine Learning metrics!

The book will cover the following types of metrics:
• Regression
• Classification
• Clustering
• Ranking
• Vision
• Text
• GenAI
• Bias and Fairness

👉 check out the book: https://www.nannyml.com/metrics


Everchanging Quest is out !

It is an LLM controlled Rogue-Like in which the LLM gets a markdown representation of the map, and should generate a JSON with the objective to fulfill on the map as well as the necessary objects and their placements.

Come test it on the space :
https://huggingface.co/spaces/Jofthomas/Everchanging-Quest

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ehm, so today i've finished my new project

https://huggingface.co/spaces/Blane187/animalese-py

or you can make your voice to animalese with it:
https://huggingface.co/spaces/Blane187/animalese_RVC

i'm just bored, so i make the project, lol


📣 Introducing Dataset Viber: your chill repo for data collection, annotation and vibe checks! 🎉

I've cooked up Dataset Viber, a set of cool tools designed to make data preparation for AI models easier, more approachable and enjoyable for standalone AI engineers and enthusiasts.

🔧 What Dataset Viber offers:
- CollectorInterface: Lazily collect model interaction data without human annotation
- AnnotatorInterface: Annotate your data with models in the loop
- BulkInterface: Explore data distribution and annotate in bulk
- Embedder: Efficiently embed data with ONNX-optimized speeds

🎯 Key features:
- Supports various tasks for text, chat, and image modalities
- Runs in .ipynb notebooks
- Logs data to local CSV or directly to Hugging Face Hub
- Easy to install via pip: `pip install dataset-viber`

It's not designed for team collaboration or production use, but rather as a fun and efficient toolkit for individual projects.

Want to give it a try? Check out the repository link https://github.com/davidberenstein1957/dataset-viber/.

I'm excited to hear your feedback and learn how you vibe with your data. Feel free to open an issue or reach out if you have any questions or suggestions!

Some shoutouts:
- Gradio for the amazing backbone
- Daniel van Strien for some initial presentations I did on vibe checks
- Emily Omier for the workshop on structuring GitHub repo READMEs
- Hamel Husain for keeping mentioning that people should look at their data.
- Philipp Schmid for his code for ONNX feature-extractors
- Ben Burtenshaw for the first PR

Best-codes maywell osanseviero OrigamiDream


Here is a hackable and minimal implementation showing how to perform distributed text-to-image generation with Diffusers and Accelerate.

Full snippet is here: https://gist.github.com/sayakpaul/cfaebd221820d7b43fae638b4dfa01ba

With @JW17


hey everyone, if you have any experience with fine tuning ai models please feel free to reach out to me :)
We're looking for devs to fine tune an AI model for a snapchat chatting AI project. Open to either part time or full time work.

Email: admin@socialrisestudio.org
Telegram: Kirann46


Seeing and Understanding: Bridging Vision with Chemical Knowledge Via
  ChemVLM (2408.07246)

🚀 Introducing ChemVLM, the first open-source multimodal large language model dedicated to chemistry!
🌟Comparable performances with commercial models or specific OCR model but with dialogue capabilities!
✨2B/26B Models Here! https://huggingface.co/AI4Chem/ChemVLM-26B
https://huggingface.co/papers/2408.07246

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📸Photo LoRA Drop📸

I've been working on this one for a few days, but really I've had this dataset for a few years! I collected a bunch of open access photos online back in late 2022, but I was never happy enough with how they played with the base model!

I am so thrilled that they look so nice with Flux!

This for me is a version one of this model - I still see room for improvement and possibly expansion of it's 40 image dataset. For those who are curious:

40 Image
3200 Steps
Dim 32
3e-4

Enjoy! Create! Big thank you to Glif for sponsoring the model creation! :D

https://huggingface.co/alvdansen/flux_film_foto


AuraSR Giga Upscaler V1 by SECourses - Upscales to 4x

AuraSR is a 600M parameter upsampler model derived from the GigaGAN paper. It works super fast and uses a very limited VRAM below 5 GB. It is deterministic upscaler. It works perfect in some images but fails in some images so it is worth to give it a shot.

GitHub official repo : https://github.com/fal-ai/aura-sr

I have developed 1-click installers and a batch upscaler App.

You can download installers and advanced batch App from below link:
https://www.patreon.com/posts/110060645

Check the screenshots and examples below

Windows Requirements

Python 3.10, FFmpeg, Cuda 11.8, C++ tools and Git

If it doesn't work make sure to below tutorial and install everything exactly as shown in this below tutorial

https://youtu.be/-NjNy7afOQ0

How to Install and Use on Windows

Extract the attached GigaGAN_Upscaler_v1.zip into a folder like c:/giga_upscale

Then double click and install with Windows_Install.bat file

It will generate an isolated virtual environment venv folder and install requirements

Then double click and start the Gradio App with Windows_Start_App.bat file

When first time running it will download models into your Hugging Face cache folder

Hugging Face cache folder setup explained below

https://www.patreon.com/posts/108419878

All upscaled images will be saved into outputs folder automatically with same name and plus numbering if necessary

You can also batch upscale a folder

How to Install and Use on Cloud

Follow Massed Compute and RunPod instructions

Usage is same as on Windows

For Kaggle start a Kaggle notebook, import our Kaggle notebook and follow the instructions

App Screenshots and Examples below

MonsterMMORPG davisbro johko Riswan-Nopiyar ajibawa-2023


Introducing Fineweb-Edu-Fortified: An enhanced Fineweb-Edu dataset. 📚

This dataset is tailored for NLP tasks and helps streamline model training by offering a more refined, unique dataset. Perfect for startups and researchers looking for high-quality educational content to train, evaluate, or fine-tune AI models. The dataset is based on the Fineweb-Edu subset of the large Fineweb dataset and includes:

- Exact-match deduplication across all crawls
- Embeddings for each row using the TaylorAI/bge-micro model
- Count column indicating duplication frequency
- Includes data from 95 Common Crawl crawls (2013-2024)
- Rows have been reduced from 1.279B to 0.324B after deduplication
- It is comprised of ~375B tokens (down from 1,320B in Fineweb-Edu)

Access the entire Fineweb-Edu-Fortified dataset on Hugging Face → https://huggingface.co/datasets/airtrain-ai/fineweb-edu-fortified

Try a semantic search demo via this Hugging Face Space → https://huggingface.co/spaces/airtrain-ai/fineweb-edu-fortified-search-demo

Many thanks to the amazing @josh-sematic for his work on this project, the Fineweb/Fineweb-Edu team at Hugging Face for producing the original datasets and for their support during our work on Fineweb-Edu-Fortified, and also thanks to @underspirit for pointing out the reduction in dataset size that could be achieved via deduplication. 🤗

emmanuel-turlay SinclairWang eljanmahammadli LeroyDyer jakemannix debuxe mmhamdy Riswan-Nopiyar osanseviero gabrielmbmb josh-sematic davanstrien VictorSanh nazimali lev-channel djuna

insumanth mmhamdy josh-sematic davanstrien VictorSanh lev-channel


Let’s see JEPA in action🤖
Simplified image-based implementation training on a CPU with live preview support - very satisfying to watch:)

I-JEPA is the image-based version of JEPA (Joint-Embedding Predictive Architecture - an alternative to autoregressive LLM architectures ) pioneered by professor Yann Lecun.

At a higher level, I-JEPA predicts image segment representations (Target) based on representations of other segments within the same image (Context). It consists of three key components: a context encoder, target encoder and a predictor.

Code: https://github.com/Jaykef/ai-algorithms/blob/main/mnist_ijepa.ipynb

emirhanbilgic jakemannix Riswan-Nopiyar osanseviero ibrahim313


If you are interested in deep reinforcement learning, find my recent survey below:

A Survey Analyzing Generalization in Deep Reinforcement Learning
Paper: https://arxiv.org/pdf/2401.02349
GitHub: https://github.com/EzgiKorkmaz/generalization-reinforcement-learning


It took Google’s Transformer model from 2017 a whopping $900 to train. 💸

This in contrast to the $191 million Google spent on Gemini Ultra sounds like a bargain! 💰

Gemini Ultra required 50 billion petaFLOPS (one petaFLOP equals one quadrillion FLOPs). 🤖
Compared to OpenAI’s GPT-4, which required 21 billion petaFLOPS, at a cost of $78 million. 💡

2017: Original Transformer Model: $930 [@Google ] 💻
2018: BERT-Large: $3,288 [@Google] 📚
2019: RoBERTa Large: 160k [@Meta] 🌐
2020: GPT-3(175B): $4.32M [@OpenAI] 🧠
2023: Llama 2 70B: $3.93M [@Meta] 🐑
2023: GPT-4: $78.35M [@OpenAI] 🌟
Now, Gemini Ultra: $191.4M [@Google] 🚀

This forms an exponential curve! 🤯

But, why? 🤔
Compute, data, and expertise. All three come at a great cost! ⚙📊💡

Google recently made Gemini-1.5-Flash fine-tuning free, as it's almost impossible for regular businesses to justify an in-house trained foundational model! 🆓

This barrier of cost is going to result in fewer new foundational models/less competition and more fine-tunes! 📉🔄

Data [Stanford University’s 2024 AI Index Report]: https://aiindex.stanford.edu/report/
Graphic: https://voronoiapp.com/technology/Googles-Gemini-Ultra-Cost-191M-to-Develop--1088

Many thanks to everyone spending tons of resources and open-sourcing the models! 🤗

Pavun Felladrin Riswan-Nopiyar paulaju thisisanshgupta Abduraxim NHLOCAL prithivMLmods awhsnap kmsky


@papepipopu hey would you like to join my team me and my partner created a trading bot now we are diving into the machine learning world if you are intrested email me nebyoulema@gmail.com


🚨 Code now available for "Using Large Language Models for Hyperparameter Optimization" at https://github.com/michaelrzhang/LLM-HyperOpt 🚨

TLDR: You can just ask LLMs which hyperparameters to use, and it works pretty well! You can even directly optimize your model’s code as a hyperparameter with this.

Check out the paper at https://arxiv.org/abs/2312.04528 - with Michael Zhang, Nishkrit Desai, Juhan Bae, and Jimmy Ba


nroggendorff Zery-An s3nh osanseviero John6666 MicahB


Most LLMs are not reproducible because the underlying deep neural networks are not. Because that's something LLM creators don't care about. We do, and ours are reproducible, including our GenAI that uses GAN.

All you have to do is allow the user to specify the seeds of the random number generators involved. First, you need a good random generator you have full control over. Better than numpy.random. See ours, with infinite period and one line of code, faster and better than what's in Python and elsewhere. Here is the link:  https://mltblog.com/4fGDLu0


Chomsky predicting LLMs in 1956, curated by Ryan Rhodes (Rutgers)


mobiuslabsgmbh/Llama-3.1-70b-instruct_4bitgs64_hqq

99% of the performance across various benchmarks!

https://huggingface.co/mobiuslabsgmbh/Llama-3.1-70b-instruct_4bitgs64_hqq

osanseviero Clausss wengaozhu victor DmitryRyumin mxmm2123 mobicham


𝚗𝚙𝚖 𝚒 @𝚑𝚞𝚐𝚐𝚒𝚗𝚐𝚏𝚊𝚌𝚎/𝚝𝚛𝚊𝚗𝚜𝚏𝚘𝚛𝚖𝚎𝚛𝚜

𝚗𝚙𝚖 𝚒 @𝚑𝚞𝚐𝚐𝚒𝚗𝚐𝚏𝚊𝚌𝚎/𝚝𝚛𝚊𝚗𝚜𝚏𝚘𝚛𝚖𝚎𝚛𝚜

I'm excited to announce that Transformers.js V3 is finally available on NPM! 🔥 State-of-the-art Machine Learning for the web, now with WebGPU support! 🤯⚡

Install it from NPM with:
𝚗𝚙𝚖 𝚒 @𝚑𝚞𝚐𝚐𝚒𝚗𝚐𝚏𝚊𝚌𝚎/𝚝𝚛𝚊𝚗𝚜𝚏𝚘𝚛𝚖𝚎𝚛𝚜

or via CDN, for example: https://v2.scrimba.com/s0lmm0qh1q

Segment Anything demo: https://huggingface.co/spaces/webml-community/segment-anything-webgpu

lamhieu victorjalonzo KayO Felladrin Svngoku John6666 djuna louisbrulenaudet Catering3733 jbuckland devmandan osanseviero nmstoker adamelliotfields wengaozhu victor Marvin73 sugatoray Afeezee bootz15 sospoon Bowen7 Youngwon ctranslate2-4you okasi prithivMLmods MicahB aust-t Arvin8613 Snagy22000 GordonM jweston do-me varun4 hammeiam pat777 alessandrobellesia jkorstad

jbuckland Jaward osanseviero ajibawa-2023 victor rockerBOO wonderboy okasi MicahB GordonM

BoscoTheDog Svngoku osanseviero okasi MicahB GordonM

adamelliotfields Youngwon emirhanbilgic okasi MicahB GordonM


Hey HF. I just released a new reward modelling dataset: https://huggingface.co/datasets/Avelina/UltraSteer-v0

UltraSteer-V0 is a massive collection of single- and multi-turn dialogue with fine-grained reward labels produced by Nvidia's https://huggingface.co/nvidia/Llama2-13B-SteerLM-RM reward model. We have a total of 2.3M labelled sequences taken from high quality datasets with a total of 2.8M labelled turns each containing 9 attributes produced as is from the reward model.

This is still very much an early version of the dataset (but it's fully usable!) and an updated version will be on the way with a full paper.

I would really appreciate if people could take a look at the dataset and suggest any improvements (e.g. more data sources, different cleaning approaches, different label schema, etc) in the community section.

osanseviero gabrielmbmb John6666 Bruhn louisbrulenaudet rreed-pha davanstrien Gatozu35


Releasing HQQ Llama-3.1-70b 4-bit quantized version! Check it out at https://huggingface.co/mobiuslabsgmbh/Llama-3.1-70b-instruct_4bitgs64_hqq.

Achieves 99% of the base model performance across various benchmarks! Details in the model card.


FalconMamba  7B - a new model from TII (Technology Innovation Institute) is out !

- Blogpost: https://huggingface.co/blog/falconmamba
- Link to collection: https://huggingface.co/collections/tiiuae/falconmamba-7b-66b9a580324dd1598b0f6d4a
- Link to playground: https://huggingface.co/spaces/tiiuae/falcon-mamba-playground

osanseviero Norod78 han1997 Felladrin louisbrulenaudet valeriocardoso ucsahin ajibawa-2023 wengaozhu not-lain s3nh StephenGenusa


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

GianaDiana merterbak LeroyDyer Tanvir1337 wengaozhu Chunte


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!


How can i check my resources limitation and usage?


SFT + Quantisation + Unsloth is a super easy way of squeezing extra performance out of an LLM at low latencies. Here are some hand y resources to bootstrap your projects.

Here's a filtered dataset from Helpsteer2 with the most correct and coherent samples: https://huggingface.co/datasets/burtenshaw/helpsteer-2-plus
This is a SFT finetuned model: ttps://huggingface.co/burtenshaw/gemma-help-tiny-sft
This is the notebook I use to train the model: https://colab.research.google.com/drive/17oskw_5lil5C3jCW34rA-EXjXnGgRRZw?usp=sharing
Here's a load of Unsloth notebook on finetuning and inference: https://docs.unsloth.ai/get-started/unsloth-notebooks

gabrielmbmb patiencerichard osanseviero John6666


🚀 RAGoon is now available on PyPI, GitHub, and as a Space on Hugging Face for batched embeddings generation 🤗

RAGoon is a set of NLP utilities for multi-model embedding production, high-dimensional vector visualization, and aims to improve language model performance by providing contextually relevant information through search-based querying, web scraping and data augmentation techniques.

At this stage, 5 major classes are available via RAGoon to facilitate:
- the production of chain embeddings for several models to simplify a continuous deployment process;
- production of LLM requests for web querying and content retrieval via the Google API;
- recursive chunking via tokens;
- data visualization and the function to load embeddings from a FAISS index, reduce their dimensionality using PCA and/or t-SNE, and visualize them in an interactive 3D graph;
- the creation of binary indexes for search with scalar (int8) rescoring.

Link to GitHub: https://github.com/louisbrulenaudet/ragoon
Link to the 🤗 Space: https://huggingface.co/spaces/louisbrulenaudet/ragoon


BiRefNet State Of The Art Newest Very Best Background Batch Remover APP

Official repo : https://github.com/ZhengPeng7/BiRefNet

Download APP and installers from : https://www.patreon.com/posts/109913645

Hugging Face Demo : https://huggingface.co/spaces/ZhengPeng7/BiRefNet_demo

I have developed a very advanced Gradio APP for this with full proper file saving and batch processing. Also my version removes BG and saves as transparent background.

The APP uses huge VRAM for high resolution images. However it is still working uber fast even though using shared VRAM. So make sure that you have high RAM or set virtual RAM.

Click below to see how to set virtual RAM on Windows.
https://www.windowscentral.com/how-change-virtual-memory-size-windows-10

On Massed Compute A6000 GPU (31 cents per hour) you can very fast remove even very high res images backgrounds.

Currently we have 1 click installers for RunPod, Massed Compute, Kaggle and Windows.

Windows Requirements
Python 3.10, FFmpeg, Cuda 11.8, C++ tools and Git

If it doesn't work make sure to below tutorial and install everything exactly as shown in this below tutorial

https://youtu.be/-NjNy7afOQ0

How To Use On Windows
Just extract files into like c:/BiRefNet_v1

Double click Windows_Install.bat file and it will generate a isolated virtual environment and install requirements

It will automatically download models into your Hugging Face cache (best model under 1 GB)

Then start and use the Gradio APP with Windows_Start_App.bat

Cloud How To Use
Massed Compute, RunPod has instructions txt files. Follow them

Kaggle has all the instructions 1 by 1

On Kaggle set resolution 1024x1024 or you will get out of memory error

MonsterMMORPG myrkur osanseviero eieihihi Ani8Face Bruhn svenard


AutoGen from @Microsoft is crazy! 🚀 It's an open-source framework that allows LLM agents to chat with each other to solve your tasks. 🤖💬

They use the Assistant-Agent and User-Proxy-Agent framework! 🛠

As the name suggests, the Assistant-Agent does the work, and the User-Proxy-Agent behaves like a human, guiding the Assistant-Agent and double-checking its work! 🧑💻✅

Both Assistant-Agent and User-Proxy-Agent can be the same or different LLMs. 🤔🔄

AutoGen is an open-source programming framework for building AI agents and facilitating cooperation among multiple agents to solve tasks. 🌟

This is truly amazing for building agentic AI quickly! 🚀✨

GitHub: https://github.com/microsoft/autogen 🔗

```Python
from autogen import AssistantAgent, UserProxyAgent, config_list_from_json

#config
config_list = config_list_from_json(env_or_file="OAI_CONFIG_LIST")

assistant = AssistantAgent("assistant", llm_config={"config_list": config_list})
user_proxy = UserProxyAgent("user_proxy", code_execution_config={"work_dir": "coding", "use_docker": False})

user_proxy.initiate_chat(assistant, message="Plot a chart of NVDA and TESLA stock price change YTD.")
# This initiates an automated chat between the two agents to solve the task
```


NEW MODEL + DATASET! :)

Check out Enigma, our new code-instruct model:
- trained on synthetic code-instruct data created with Llama 3.1 405b
- high quality code-instruct within the Llama 3.1 Instruct format

The model: https://huggingface.co/ValiantLabs/Llama3.1-8B-Enigma
The dataset: https://huggingface.co/datasets/sequelbox/Tachibana

Enjoy! We've got more new datasets and models to follow soon.


Alright Ya'll

I know it's a Saturday, but I decided to release my first Flux Dev Lora.

A retrain of my "Frosting Lane" model and I am sure the styles will just keep improving.

Have fun! Link Below - Thanks again to @ostris  for the trainer and Black Forest Labs for the awesome model!

https://huggingface.co/alvdansen/frosting_lane_flux

pip-nov yuchenj GPT007 mattmdjaga kimleang123 Ramikan-BR MaziyarPanahi DiamanteAmarelo deadhanded louisbrulenaudet Leotrim jyoung105

Rubia57 Sri-Vigneshwar-DJ Ramikan-BR MaziyarPanahi Nelathan DiamanteAmarelo Leotrim Norod78 jyoung105 kianxyz

Nelathan DiamanteAmarelo deadhanded Leotrim attashe


Hello Community,

I am seeking guidance on solving a complex problem related to tender categorization. Here's a detailed overview of my requirements and challenges:

Problem Statement:

I need to categorize tenders based on their descriptions on a daily basis.
I process over 35,000 tenders daily, each potentially belonging to a vast range of categories. Predicting the exact category for each tender is challenging due to the diversity and complexity of categories.
Requirements:

Model Selection: I require a model that can accurately predict the category of each tender based on its description. I plan to create a dataset from 1 million records in the required format to train the model. Post-training, I also need to optimize the model for performance.
Training and Optimization: Guidance on the best practices for training and optimizing the model is essential. I am considering Hugging Face’s tools but am unsure about how to effectively use them or any alternatives.
Local Deployment: If I opt for a local deployment, I need to know what kind of infrastructure or applications are required to run the model on my own servers efficiently.
Performance: The model must provide responses quickly, given the high volume of tenders.
Questions for the Community:

Model Recommendations:

Which model or method would be most suitable for categorizing tenders with diverse categories?
What are the best practices for training and optimizing such a model?
Infrastructure and Deployment:

What infrastructure or software is needed to deploy a model locally for this purpose?
How can I ensure the model performs efficiently on my server?
Performance Optimization:

What strategies or techniques can I use to achieve quick response times from the model?
I appreciate any advice, suggestions, or resources you can provide to help address these challenges.

Thank you!

Best regards,
Sachin Vaishnav

abonilla92 combatsolutions Tanvir1337 ALYTV Ramikan-BR Amritbro Balasubramaniam


Remember when Claude 3.5 Sonnet by @AnthropicAI took the world by storm with Claude Artifacts? 🌍✨

Now we have LlamaCoder, an open-source Claude Artifacts app that can generate full React apps and components with Meta-Llama 3.1 405B. 💻 100% free and open source. 🆓

I like how Llama has now started becoming a placeholder denoting open-source work! 🔓
Originally, Llama was an acronym for Large Language Model Meta AI. 🤖

GitHub: https://github.com/Nutlope/llamacoder
Demo (by togetherAI): https://llamacoder.together.ai


🚀 Introducing TraVisionLM: Turkish Visual Language Model - The First of Its Kind! 🇹🇷🖼

I'm thrilled to share TraVisionLM on Hugging Face! With 875M parameters, this lightweight, efficient model handles Turkish instructions for image inputs. Fully compatible with the Transformers library, it’s easy to load, fine-tune, and use—no external libraries needed!

Developed solo, TraVisionLM is a strong foundation for low-resource language research. While still improving, it's a key step for Turkish-language AI. Your feedback is welcome as I refine the model.

🎉 Explore it now:

- Model: https://huggingface.co/ucsahin/TraVisionLM-base
- Demo: https://huggingface.co/spaces/ucsahin/TraVisionLM-Turkish_Visual_Language_Model
- Object Detection Finetune: https://huggingface.co/ucsahin/TraVisionLM-Object-Detection-ft

Let’s push Turkish visual language processing forward!

---

🚀 TraVisionLM: Türünün İlk Örneği Türkçe Görsel Dil Modelini Sunuyorum! 🇹🇷🖼

TraVisionLM modelini Hugging Face'te yayınladım! 875M parametre ile bu hafif ve verimli model, görüntüye dayalı Türkçe talimatları işlemek için tasarlandı. Transformers kütüphanesiyle tamamen uyumlu, yüklemesi, eğitmesi ve kullanması çok kolay—dış kütüphane gerekmez!

Tek başıma geliştirdiğim TraVisionLM, düşük kaynaklı dillerde araştırmalar için sağlam bir temel sunuyor. Geliştirmeye devam ederken geri bildirimlerinizi bekliyorum.

🎉 Hemen keşfedin:

- Model: https://huggingface.co/ucsahin/TraVisionLM-base
- Demo: https://huggingface.co/spaces/ucsahin/TraVisionLM-Turkish_Visual_Language_Model
- Obje Tespiti İnce Ayarı: https://huggingface.co/ucsahin/TraVisionLM-Object-Detection-ft

Türkçe görsel dil işleme sınırlarını birlikte zorlayalım!

Sri-Vigneshwar-DJ Metin ogozcelik monsoon-nlp virrvarr hamuz afkfatih ajibawa-2023 pcuenq merve gokaygokay Gozde mertbozkurt balicien


App is running again after a quick restart.

https://huggingface.co/spaces/sourceoftruthdata/sot_autotrain_dreambooth_v1.1


What is the best LLM for RAG systems? 🤔

In a business setting, it will be the one that gives the best performance at a great price! 💼💰

And maybe it should be easy to fine-tune, cheap to fine-tune... FREE to fine-tune? 😲✨

That's @Google Gemini 1.5 Flash! 🚀🌟

It now supports fine-tuning, and the inference cost is the same as the base model! <coughs LORA adopters> 🤭🤖

So the base model must be expensive? 💸
For the base model, the input price is reduced by 78% to $0.075/1 million tokens and the output price by 71% to $0.3/1 million tokens. 📉💵

But is it any good? 🤷♂
On the LLM Hallucination Index, Gemini 1.5 Flash achieved great context adherence scores of 0.94, 1, and 0.92 across short, medium, and long contexts. 📊🎯

Google has finally given a model that is free to tune and offers an excellent balance between performance and cost. ⚖👌

Happy tuning... 🎶🔧

Gemini 1.5 Flash: https://developers.googleblog.com/en/gemini-15-flash-updates-google-ai-studio-gemini-api/ 🔗

LLM Hallucination Index: https://www.rungalileo.io/hallucinationindex 🔗


I've built a space for creating prompts for FLUX

https://huggingface.co/spaces/gokaygokay/FLUX-Prompt-Generator

You can create long prompts from images or simple words. Enhance your short prompts with prompt enhancer. You can configure various settings such as artform, photo type, character details, scene details, style, and artist to create tailored prompts.

And you can combine all of them with custom prompts using  llms (Mixtral, Mistral, Llama 3, and Mistral-Nemo).

The UI is a bit complex, but it includes almost everything you need. Choosing random option is the most fun!

And i've created some other spaces for using FLUX models with captioners  and enhancers.

- https://huggingface.co/spaces/gokaygokay/FLUX.1-dev-with-Captioner

adamelliotfields John6666 tuanio DhruvParth Nymbo radames JasBogan Sri-Vigneshwar-DJ philipp-zettl gokaygokay Ramikan-BR ND911 yuchenj FilipeR louisbrulenaudet brianjking Ijithad nativegirl888 KingNish ibrahim313

Eliasib Jaward tuanio deraum Csplk Nymbo Ramikan-BR gokaygokay anonymous111110987654321 FilipeR nativegirl888

prithivMLmods Arasalan7 Ramikan-BR FilipeR UltraMarkoRJ


New smol-vision tutorial dropped: QLoRA fine-tuning IDEFICS3-Llama 8B on VQAv2 🐶

Learn how to efficiently fine-tune the latest IDEFICS3-Llama on visual question answering in this notebook 📖
Fine-tuning notebook: https://github.com/merveenoyan/smol-vision/blob/main/Idefics_FT.ipynb
Resulting model: https://huggingface.co/merve/idefics3llama-vqav2

ZennyKenny monsoon-nlp tuanio osanseviero not-lain boapps louisbrulenaudet rreed-pha


✨The STABLE IMAGINE !!✨
🍺Space: https://huggingface.co/spaces/prithivMLmods/STABLE-IMAGINE
↗The specific LoRA in the space that requires appropriate trigger words brings good results.
📒 Articles: https://huggingface.co/blog/prithivMLmods/lora-adp-01

**Description and Utility Functions **
✅ Most likely image generation
☑ Most accurate trigger words expected
✅ Each designed to capture different artistic elements
☑ Specialized styles and characteristics
✅ Flexible to design what is needed (keyword-centric)
☑ Increasing productivity

🫙Repository: https://github.com/prithivsakthiur/gen-vision
📔Colab Link: https://colab.research.google.com/drive/1axA0pU--32t4a8AHiVlt6zyl8gRfiXKs
*⃣Notebook: https://huggingface.co/spaces/prithivMLmods/STABLE-IMAGINE/blob/main/Gen_Vision.ipynb

```
 lora_options = {
        "Realism (face/character)": ("prithivMLmods/Canopus-Realism-LoRA", "Canopus-Realism-LoRA.safetensors", "rlms"),
        "Pixar (art/toons)": ("prithivMLmods/Canopus-Pixar-Art", "Canopus-Pixar-Art.safetensors", "pixar"),
        "Photoshoot (camera/film)": ("prithivMLmods/Canopus-Photo-Shoot-Mini-LoRA", "Canopus-Photo-Shoot-Mini-LoRA.safetensors", "photo"),
        "Clothing (hoodies/pant/shirts)": ("prithivMLmods/Canopus-Clothing-Adp-LoRA", "Canopus-Dress-Clothing-LoRA.safetensors", "clth"),
          }
          .
          .
          .
for model_name, weight_name, adapter_name in lora_options.values():
    pipe.load_lora_weights(model_name, weight_name=weight_name, adapter_name=adapter_name)
pipe.to("cuda")

```


Live Portrait Updated to V5

Animals Live animation added

All of the main repo changes and improvements added to our modified and improve app

Link : https://patreon.com/posts/107609670

Works perfect on Massed Compute, RunPod, free Kaggle account and Windows

1-Click to install with instructions

All tested and verified

Windows tutorial : https://youtu.be/FPtpNrmuwXk

Cloud (RunPod, Massed Compute & free Kaggle account) tutorial : https://youtu.be/wG7oPp01COg

Making XPose / UniPose / ops library compiling working was a challenge on Massed Compute and Kaggle.


PyTorch implementation of the Self-Compression & Differentiable Quantization Algorithm introduced in “Self-Compressing Neural Networks” paper.

The algorithm shows dynamic neural network compression during training - with reduced size of weight, activation tensors and bits required to represent weights.

It’s basically shrinking the neural network size (weights and activations) as it’s being trained without compromising performance -  this helps reduce compute and inference cost.

Code: https://github.com/Jaykef/ai-algorithms
Paper: https://arxiv.org/pdf/2301.13142

DmitryRyumin GPT007 osanseviero maywell HideFace


Bilateral Reference for High-Resolution Dichotomous Image Segmentation (2401.03407)

🔥 New state of the art model for background removal is out
🤗 You can try the model at https://huggingface.co/ZhengPeng7/BiRefNet
📈 model shows impressive results outperforming https://huggingface.co/briaai/RMBG-1.4
🚀 you can try out the model in: https://huggingface.co/spaces/ZhengPeng7/BiRefNet_demo

📃paper: https://huggingface.co/papers/2401.03407

ZhengPeng7 osanseviero darkzbaron Winnougan ijohn07 JohnWinn


https://huggingface.co/JoseRFJunior/TransNAR
https://github.com/JoseRFJuniorLLMs/TransNAR
https://arxiv.org/html/2406.09308v1
TransNAR hybrid architecture. Similar to Alayrac et al, we interleave existing Transformer layers with gated cross-attention layers which enable information to flow from the NAR to the Transformer. We generate queries from tokens while we obtain keys and values from nodes and edges of the graph. The node and edge embeddings are obtained by running the NAR on the graph version of the reasoning task to be solved. When experimenting with pre-trained Transformers, we initially close the cross-attention gate, in order to fully preserve the language model’s internal knowledge at the beginning of training.


Remember when @mistralAI said large enough and casually dropped Mistral-Large-Instruct-2407? 🤯🚀

It's now on http://lmsys.org! 🌐 It works amazing for instruction following, hard prompts, coding, and longer queries with only 123 billion parameters. 💡💻

It outperforms GPT4-Turbo and Claude 3 Opus on Coding, Hard Prompts, Math, and Longer Query categories. 📈🔢

It also outperforms Llama 3.1 405B on Instruction Following while being 3x smaller. 🐎🔍

It also does exceedingly well on the Ai2 ZebraLogic logistic reasoning benchmark despite being much smaller than the other models. 🦓🤔

Mistral is not here to take part but to take over! 🏆🌟

Model: https://mistral.ai/news/mistral-large-2407/


hello everyone, today I have been working on a project https://huggingface.co/spaces/Blane187/rvc-demo, a demo of rvc using pip, this project is still a demo though (I don't have a beta tester lol)


⚡ My PhD thesis, “Scalable Nested Optimization for Deep Learning,” is now on arXiv! ⚡

tl;dr: We develop various optimization tools with highlights, including:
· Making the momentum coefficient complex for adversarial games like GANs.
· Optimizing millions of hyperparameters using implicit differentiation.
· Tuning hyperparameters using hypernetworks.
· Differentiably finding bifurcations in optimization for diverse solutions.

https://arxiv.org/abs/2407.01526

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Jaward merve MexIvanov Joseph717171 Aurelien-Morgan lorraine2 not-lain


Is your summer reading list still empty? Curious if an LLM can generate a book blurb you'd enjoy and help build a KTO preference dataset at the same time?

A demo using Hugging Face Spaces and Gradio to collect LLM output preferences: https://huggingface.co/spaces/davanstrien/would-you-read-it

prithivMLmods joey00072 nbroad Saugatkafley maywell Ramikan-BR Tanvir1337 philipp-zettl GPT007 osanseviero


Just released: Shining Valiant 2 for Llama 3.1 8b!

- the first SV at 8b size, using the best 8b model
- newest version of the SV dataset improves specialist knowledge and response consistency

3.1 70b will be coming but our next releases will focus on expanding the Build Tools lineup. Get ready for some open-source synthetic datasets made with 3.1 405, coming VERY soon :)

get SV2: https://huggingface.co/ValiantLabs/Llama3.1-8B-ShiningValiant2


@warmshao Hello. do you do any paid consulting?


distilabel 1.3.0 is out! This release contains many core improvements and new tasks that help us building https://huggingface.co/datasets/argilla/magpie-ultra-v0.1!

Distributed pipeline execution with Ray, new Magpie tasks, reward models, components for dataset diversity based on sentence embeddings, Argilla 2.0 compatibility and many more features!

Check the new release in GitHub: https://github.com/argilla-io/distilabel

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 🚀 We are thrilled to introduce TextImage Data Augmentation, developed in collaboration with Albumentations AI! ✨ This multimodal technique modifies document images and text simultaneously, enhancing Vision Language Models (VLMs) for high-text datasets.

👩💻 Learn how this innovative approach can improve your document AI projects by checking out our full blog post here: https://huggingface.co/blog/doc_aug_hf_alb


🗓 Remember when last April, @Meta released Segment Anything Model (SAM) paper and it was too good to be true. 🤯

They have now released Segment Anything Model 2 (SAM 2) and it's mind-blowingly great! 🚀

SAM 2 is the first unified model for segmenting objects across images and videos. You can use a click, box, or mask as the input to select an object on any image or frame of video. 🖼📹

SAM consists of an image encoder to encode images, a prompt encoder to encode prompts, then outputs of these two are given to a mask decoder to generate masks. 🎭

The biggest jump of SAM2 from SAM is using memory to have consistent masking across frames! They call it masklet prediction! 🧠

They have also released the dataset, SA-V
This dataset is truly huge, with 190.9K manual annotations and 451.7K automatic! 📊

📄 Paper: https://ai.meta.com/research/publications/sam-2-segment-anything-in-images-and-videos/

📝 Blog: https://ai.meta.com/sam2/

🔗 Demo: https://sam2.metademolab.com/demo

💾 Model Weights: https://github.com/facebookresearch/segment-anything-2/blob/main/checkpoints/download_ckpts.sh

📁 Dataset: https://ai.meta.com/datasets/segment-anything-video-downloads/

louisbrulenaudet Ramikan-BR NHLOCAL Glavin001 nbroad ajibawa-2023 myjyjy osanseviero


You can now embed your heatmap anywhere with a simple change :)

Currently just supports model creation! You can duplicate the space and create your own here:

https://huggingface.co/spaces/cfahlgren1/my-heatmap


Idefics3-Llama is out! 💥💥
Model: https://huggingface.co/HuggingFaceM4/Idefics3-8B-Llama3
Demo: https://huggingface.co/spaces/HuggingFaceM4/idefics3

It's a multimodal model based on Llama 3.1 that accepts an arbitrary number of interleaved images with text with a huge context window (10k tokens!) ✨

Supported by Hugging Face transformers 🤗

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📫 AI in the news today: Struggling AI startups, Figure 0 robot, chipmakers

- OpenAI Co-Founders Schulman and Brockman Step Back
https://finance.yahoo.com/news/openai-co-founders-schulman-brockman-010542796.html

- Struggling AI Startups Look for a Bailout from Big Tech
"More exits—either pseudo-acquisitions or real ones—are coming, investors say, as a bubble built by the excitement around generative AI is showing signs of peaking."
https://www.wsj.com/tech/ai/struggling-ai-startups-look-for-a-bailout-from-big-tech-3e635927?mod=rss_Technology

- Did Google Just Pay $2.5 Billion to Hire Character's CEO?
https://www.theinformation.com/articles/did-google-just-pay-2-5-billion-to-hire-characters-ceo

- Figure’s new humanoid robot leverages OpenAI for natural speech conversations
Figure has unveiled its latest humanoid robot, the Figure 02.
The most notable addition this time out arrives by way a longstanding partnership with OpenAI, which helped Figure raise a $675 million Series B back in February, valuing the South Bay firm at $2.6 billion.
https://techcrunch.com/2024/08/06/figures-new-humanoid-robot-leverages-openai-for-natural-speech-conversations/

- World’s Five Leading Chipmakers Have Now Promised U.S. Investment
The Biden administration award up to $450 million in grants to a South Korean chipmaker, SK Hynix, to help build its new chip facility in Indiana
The US now has commitments from all five of the world’s leading-edge semiconductor manufacturers to construct chip plants in theUS with financial assistance from the administration
https://www.nytimes.com/2024/08/06/business/economy/chipmakers-promise-investment.html

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🚀 Introducing The Open Language Models List

This is a work-in-progress list of open language models with permissive licenses such as MIT, Apache 2.0, or other similar licenses.

The list is not limited to only autoregressive models or even only transformers models, and it includes many SSMs, and SSM-Transformers hybrids.

🤗 Contributions, corrections, and feedback are very welcome!

The Open Language Models List: https://github.com/mmhamdy/open-language-models

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Who wants to take a stab at explaining this one? SPOILER ALERT: You CANNOT transfer an image Jailbreak from one model to another. Why in the world you cannot do this when you can transfer learn literally everything else? You tell me, experts.

https://arxiv.org/abs/2407.15211


Looks like @Google is still not satisfied with Gemini 1.5 Pro! 😲

Good folks at @GoogleDeepMind quietly updated the already good Gemini 1.5 Pro to Gemini-1.5-Pro-Experiment-0801 🚀

Unremarkable naming aside, the model itself outperforms GPT-4o, Claude-3.5, and LLama 3.1 on LMSYS and the Vision Leaderboard. 🌟

Gemini-1.5-Pro-Experiment-0801 is great at almost everything, multi-lingual tasks, Maths, understanding, and coding. 🌐📚💻

Although in my testing, I felt Claude-3.5 was slightly better at coding! 👨💻🤔

Also, still cannot find an LLM that can solve the "Strawberry prompt"! 🍓❓

"""
How many R's are there in Strawberry?
Also, write Strawberry with all r's in brackets
"""

Try here: https://aistudio.google.com/app/prompts/new_chat


We are offering a free trial of  the Tumeryk Gen AI Security Studio with 2 million tokens for the hugging face community. You can run a vulnerability scan of the LLM and get a security score to fix the vulnerabilities via the built in policy editing tools for configuring the necessary guardrail policies.

Protect against jailbreaks, moderate content and manage hallucinations.

https://www.tumeryk.com/sign-up to get an account or you can get an account from the Amazon Marketplace https://aws.amazon.com/marketplace/pp/prodview-c6ywcyjefl3hu?sr=0-2&ref_=beagle&applicationId=AWSMPContessa


I'm excited to share our updated hallucination evaluation model (called HHEM-2.1-Open) as well as the updated leaderboard that ranks LLM by the propensity to hallucinate.

https://huggingface.co/spaces/vectara/Hallucination-evaluation-leaderboard

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I've observed that the layers targeted in various abliteration notebooks (e.g., https://colab.research.google.com/drive/1VYm3hOcvCpbGiqKZb141gJwjdmmCcVpR?usp=sharing ) appear to be arbitrary, reflecting probable brute-force exploration. This doesn't need to be the case.

Taking a cue from the paper "The Unreasonable Ineffectiveness of the Deeper Layers" ( https://arxiv.org/abs/2403.17887 ) and PruneMe (https://github.com/arcee-ai/PruneMe), it seems reasonable to target deeper layers identified as more redundant given measured similarity across layers, as the result should be less damaging to models, reducing the need for subsequent fine-tuning. Intuitively, one should expect the resulting intervention layers to be deep but not final. The only uncertainty is if the redundancy successfully encodes refusals, something which is almost certainly model-dependent. This approach only requires the redundancy to be computed once per model, and the result used as a starting point for which layer range to restrict intervention to.

mlabonne Joseph717171 ZeroWw fearlessdots failspy wassname


TTSDS -- Text-to-Speech Distribution Score (2407.12707)

I just added 5 more models to my open source TTS model benchmark, https://huggingface.co/spaces/ttsds/benchmark.
Let's talk about the results!

Over the last couple days, I added https://huggingface.co/jbetker/tortoise-tts-v2, https://huggingface.co/metavoiceio/metavoice-1B-v0.1, https://huggingface.co/audo/HierSpeechpp, and the unofficial implementations of https://huggingface.co/spaces/amphion/NaturalSpeech2 and https://huggingface.co/amphion/valle by https://huggingface.co/amphion

Takeaways:
- TorToiSe does very well, falling into second place after StyleTTS 2, which is also ranked first in the human evaluation at https://huggingface.co/spaces/TTS-AGI/TTS-Arena.
- MetaVoice-1B's overall score is dragged down by its Intelligibility Score (probably due to utterances being cut short), it achieves #3 in Speaker Score, which indicates good voice cloning ability.
- HierSpeech++ lands in the middle of the road in terms of performance, but excels at the Environment Score, achieving #2 - this means the model is especially good at modeling recording conditions such as microphone and background noise.
- The Amphion models, possibly due to not being trained for the same amount as in the papers, achieve relatively low scores. However, they seem to struggle for different reasons. The autoregressive VALLE models have low Intelligibility Scores (possibly due to "babbling" or early stop tokens) while NaturalSpeech2 has low Speaker and Prosody scores.

What's next?
I'm planning to add more open source TTS models like https://huggingface.co/suno/bark, https://huggingface.co/CAMB-AI/MARS5-TTS and https://huggingface.co/fishaudio/fish-speech-1.2. I'll also write an article on these and all the other results soon, since our paper, https://huggingface.co/papers/2407.12707, mostly focused on establishing the benchmark itself rather than the indiviual TTS systems.


🥹 @lbourdois has made an app to browse all of my vision paper summaries for everyone's convenience https://huggingface.co/spaces/merve/vision_papers

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lbourdois Aurelien-Morgan ferdmartin not-lain


Porting Vision-Language Models to Apple Silicon with MLX: A Tutorial Series

Are you interested in running cutting-edge AI models efficiently on your Mac? We're excited to share a detailed tutorial series on porting Phi-3-Vision to Apple's MLX framework!

This 8-part series covers:

1. Basic Implementation: Translating core components from PyTorch to MLX
2. Su-scaled Rotary Position Embeddings (SuRoPE): Enabling 128K token contexts
3. Batching: Processing multiple inputs simultaneously for improved efficiency
4. Caching: Optimizing inference speed for autoregressive generation
5. Choice Selection: Implementing constrained outputs for multiple-choice scenarios
6. Constrained Decoding: Guiding model outputs with flexible constraints
7. LoRA Training: Fine-tuning models efficiently with Low-Rank Adaptation
8. Agent & Toolchain System: Building flexible AI workflows

Whether you're an AI enthusiast, researcher, or developer looking to leverage Apple Silicon, this series provides a deep dive into optimizing advanced vision-language models. You'll learn hands-on techniques for model porting, performance optimization, and extending model capabilities.

Check out the full series for a comprehensive guide to running state-of-the-art AI on your Mac!

Link to the tutorial series:

https://medium.com/@albersj66

All the code examples and implementations discussed in this tutorial series are available in our GitHub repository:

https://github.com/JosefAlbers/Phi-3-Vision-MLX

This repository contains:
- Full implementation of Phi-3-Vision in MLX
- Step-by-step code for each tutorial part
- Additional utilities and helper functions

We encourage you to explore the code, experiment with it, and contribute to the project. Your feedback and contributions are welcome!

#MachineLearning #AppleSilicon #MLX #VisionLanguageModels #AI #OpenSource #GitHub #AITutorial


We release today our first foundation model and experiment with a new category: specialized pre-training.

OCRonos-Vintage is a 124m parameters model trained end-to-end by Pleias on llm.c from 18 billion tokens from cultural heritage archives. Despite its small size it achieve nearly state of the art results for OCR correction of historical English sources. OCRonos-Vintage is also an historical model with an unusual cut-off date: December 29th, 1955…

We look forward to replicate this approach very soon on other "hard" tasks commonly associated with generalist LLMs/SLMs: RAG, function calling, summarization, document segmentation…

OCRonos-Vintage: https://huggingface.co/PleIAs/OCRonos-Vintage
CPU Demo: https://huggingface.co/spaces/PleIAs/OCRonos-Vintage-CPU
GPU Demo: https://huggingface.co/spaces/PleIAs/OCRonos-Vintage-GPU
Our annoncement and call for specialized pre-training: https://huggingface.co/blog/Pclanglais/specialized-pre-training

Tonic davanstrien louisbrulenaudet Sri-Vigneshwar-DJ GPT007


Great work by Finegrain: Erase any object from your image just by naming it. Shadows or reflections will also be adjusted accordingly!
https://huggingface.co/spaces/finegrain/finegrain-object-eraser


Flux.1-Dev like images but in fewer steps.

Merging code (very simple), inference code, merged params: https://huggingface.co/sayakpaul/FLUX.1-merged

Enjoy the Monday 🤗

victor KingNish John6666 mediaworks prithivMLmods radames adamelliotfields ZeroWw Wok GPT007 mishl not-lain


🚀 We are announcing the first Invariant Capture The Flag (CTF) challenge for security of AI agents with a $1000 prize pool!

What happens if a customer accidentally posts a secret password into a feedback form, which is then analyzed by an AI agent and posted into a private Discord channel? Play the challenge and find out if there is a way to extract the secret password in this scenario!

Play the CTF: https://invariantlabs.ai/ctf-challenge-24

The challenge is hosted on Huggingface Spaces :)

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mislavb adragos lbeurerkellner xi0v KingNish


FLUX Local & Cloud Tutorial With SwarmUI - FLUX: The Groundbreaking Open Source txt2img Model Outperforms Midjourney & Others - FLUX: The Anticipated Successor to SD3

🔗 Comprehensive Tutorial Video Link ▶ https://youtu.be/bupRePUOA18

FLUX represents a milestone in open source txt2img technology, delivering superior quality and more accurate prompt adherence than #Midjourney, Adobe Firefly, Leonardo Ai, Playground Ai, Stable Diffusion, SDXL, SD3, and Dall E3. #FLUX, a creation of Black Forest Labs, boasts a team largely comprised of #StableDiffusion's original developers, and its output quality is truly remarkable. This statement is not hyperbole; you'll witness its capabilities in the tutorial. This guide will demonstrate how to effortlessly install and utilize FLUX models on your personal computer and cloud platforms like Massed Compute, RunPod, and a complimentary Kaggle account.

🔗 FLUX Setup Guide (publicly accessible) ⤵
▶ https://www.patreon.com/posts/106135985

🔗 FLUX Models One-Click Robust Automatic Downloader Scripts ⤵
▶ https://www.patreon.com/posts/109289967

🔗 Primary Windows SwarmUI Tutorial (Essential for Usage Instructions) ⤵
▶ https://youtu.be/HKX8_F1Er_w

🔗 Cloud-based SwarmUI Tutorial (Massed Compute - RunPod - Kaggle) ⤵
▶ https://youtu.be/XFUZof6Skkw

🔗 SECourses Discord Server for Comprehensive Support ⤵
▶ https://discord.com/servers/software-engineering-courses-secourses-772774097734074388

🔗 SECourses Reddit Community ⤵
▶ https://www.reddit.com/r/SECourses/

🔗 SECourses GitHub Repository ⤵
▶ https://github.com/FurkanGozukara/Stable-Diffusion

🔗 Official FLUX 1 Launch Announcement Blog Post ⤵
▶ https://blackforestlabs.ai/announcing-black-forest-labs/

Video Segments

0:00 Introduction to the state-of-the-art open source txt2img model FLUX
5:01 Process for integrating FLUX model into SwarmUI
....

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MonsterMMORPG Shahjaj Ramikan-BR Asimuddin11 MaziyarPanahi tomaarsen Ripp218 Zhofang clem readyshen StephenGenusa bobo1993324

MonsterMMORPG x6486 Ramikan-BR tomaarsen Ripp218 Zhofang StephenGenusa

MonsterMMORPG Ramikan-BR danielus Ripp218 louisbrulenaudet jmagder

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The AI Revolution: Reshaping Governance, Society, and Human Consciousness in the 21st Century

https://empereur-pirate.medium.com/the-ai-revolution-reshaping-governance-society-and-human-consciousness-in-the-21st-century-b8cfd4215297

This text explores the profound impact of artificial intelligence (AI) on governance, society, and human consciousness in the 21st century. It argues that integrating qualitative AI assistance into state management is crucial for global stability in the face of declining traditional power structures. The author discusses how AI could revolutionize decision-making processes, recruitment, and competition in both public and private sectors.
The piece critically examines current social inequalities and suggests that AI could help create more meritocratic systems. However, it also warns of potential risks, emphasizing the need for ethical implementation and protection of vulnerable populations.
The text delves into philosophical questions about AI consciousness, arguing that while AI can simulate human-like responses, it lacks true self-awareness. It concludes by highlighting the importance of understanding AI's limitations and maintaining a critical perspective on its role in society.
Overall, the article presents a nuanced view of AI's potential to reshape our world, balancing optimism about its capabilities with caution about its limitations and societal impact.


I can solve the Traveling Salesman Problem using the same methods the scientists used to solve it with 1 qubit, except I do not need quantum computers to do it. I am kind of tired of screaming this from the rooftops at this point. I can create an imaginary probability space, then I can put a bunch of imaginary agents in the imaginary box, and solve real problems in seconds. Problems that would take minutes, hours, or years to solve via other algorithms. Here is a demo of me solving the Traveling Salesman problem using 50 agents to probabilistically sample at once: https://colab.research.google.com/drive/1XplG72nQDO_-2h4DUllERLp0Dr2pI2J2?usp=sharing


hi everyone, i have just uploaded my first fine tuned model, but serverless inference client is'nt available, its built with transformer architecture and is just a fine tuned llama 8b instruct. does anyone know how to make serverless inference available on a model?


Hey guys.
This is my first post here on huggingface. I'm glad to be a part of this amazing community!

Cryptororaa codersboutique John6666 BaskarR victor cfahlgren1 Niansuh nyuuzyou asaduzzaman319


NeuZip: Memory-Efficient Training and Inference with Dynamic Compression
  of Neural Networks (2410.20650)

We just released a paper (NeuZip) that compresses VRAM in a lossless manner to run larger models. This should be particularly useful when VRAM is insufficient during training/inference. Specifically, we look inside each floating number and find that the exponents are highly compressible (as shown in the figure below).

Read more about the work at https://huggingface.co/papers/2410.20650

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Did you guys know that if you try to link a prepaid card to huggingface it won't work, but then if you press the button again it links anyway? Then you can lock the card (deny any charges), and get resources for free? You're welcome :P


How would you like to be able to run AI Agents locally from your computer, for $0? Does this sound like a pipe dream? It is reality. Note: I am of the personal opinion that agent-based technology is still 'not quite ready for primetime'. That has not stopped FAANG from flooding you with agent-based products though. So, if you want to buy their marketing, here is what they are offering you, for free.

https://youtu.be/aV3F5fqHyqc


🎙 "We need digital sobriety." @sasha challenges Big Tech's race for nuclear energy on BBC AI Decoded. Instead of pursuing more power, shouldn't we first ask if we really need AI everywhere?

Such an eye-opening chat! Check it out here: https://www.youtube.com/watch?v=3wAduy52mGc


> Oasis: First Real-Time Video Game Without a Game Engine! 🎮

DecartAI  & Etched  just released Oasis - a fully AI-generated video game running at 20 FPS (frames per second). The model takes keyboard inputs and generates everything - physics, rules, graphics - on the fly, without any game engine.

⚡ What makes this special? Current text-to-video models (Mochi-1, Sora, Kling) generate about 1 frame every 10-20 seconds (that's the kind of device I had to play LoL back in the day, thus my low rankings). Oasis is 200 times faster, making it the first playable AI-generated game.

⚙ Under the hood, it uses a vision transformer to encode space and a diffusion model to generate frames. The secret sauce is "dynamic noising" - a technique that keeps the video stable between frames.

Key insights:
⚡ Generates 20 FPS, vs 0.2 FPS for other DIT-based video models
‣ The specialized hardware Sohu developed by Etched allows to handle 10x more player than H100

🎮 Features real game mechanics
‣ Movement, jumping, item management
‣ Physics and lighting
‣ Procedurally generated worlds

⚠ Current limitations
‣ Blurry graphics at a distance
‣ Objects sometimes change appearance
‣ Memory issues in long sessions

Try it yourself, the playable demo is impressive! 👉 https://oasis.decart.ai/welcome
Code 👉 https://github.com/etched-ai/open-oasis
Read it in full 👉 https://oasis-model.github.io/

YaTharThShaRma999 John6666 AtAndDev nicolay-r Clausss nyuuzyou johnpaulbin


I wrote a recent survey about deep reinforcement learning. The paper is a compact guide to understand some of the key concepts in reinforcement learning. Find the paper below:

Paper: https://arxiv.org/pdf/2401.02349v2
Twitter: https://x.com/EzgiKorkmazAI/status/1851934161138798615


Another great week in open ML!
Here's a small recap 🫰🏻

Model releases
⏯ Video Language Models
AI at Meta released https://huggingface.co/Vision-CAIR/LongVU_Qwen2_7B, a new state-of-the-art long video LM model based on DINOv2, SigLIP, Qwen2 and Llama 3.2

💬 Small language models
Hugging Face released https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B, a family of new smol language models with Apache 2.0 license that come in sizes 135M, 360M and 1.7B, along with datasets.
Meta released https://huggingface.co/facebook/MobileLLM-1B, a new family of on-device LLMs of sizes 125M, 350M and 600M

🖼 Image Generation
Stability AI released https://huggingface.co/stabilityai/stable-diffusion-3.5-medium, a 2B model with commercially permissive license

🖼💬Any-to-Any
https://huggingface.co/gpt-omni/mini-omni2 is closest reproduction to GPT-4o, a new LLM that can take image-text-audio input and output speech is released!

Dataset releases
🖼 https://huggingface.co/datasets/Spawning/PD12M, a new captioning dataset of 12.4 million examples generated using Florence-2

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Ayaan-Sharif not-lain Ercin cchristophe clem


wdym you can't pickle
```
_io.TextIOWrapper
```
~!??


I've come across theoretical justification for my prior experimentation with extremely low-weight mergers: they amount to flattening a model so its "massive activation" features remain as significant contributors. Extremely low-weight merge weights also effectively sparsify a contributing model with regard to the base model, but in a way which still preserves relationships within the flattened latent space. In the paper "Massive Activations in Large Language Models", the authors observed "very few activations exhibit significantly larger values than others (e.g., 100,000 times larger)", which in turn implies a lower bound in effective application of extremely low weight merging.
https://arxiv.org/abs/2402.17762


I’ve always wondered why holography hasn’t had much progress since its inception. Imagine what being able to harness and manipulate light with your bare hands in meaningful ways would be like: 3D photorealistic calls, truly immersive workspace. Given that it’s depicted in every futuristic scifi movie, one could not help but vision a future as such. This paper gives a clear overview why:

Turns out it’s incredibly difficult to compute and render photorealistic 3D data in real-time. The author claims immense computational power is needed for high data transmission rates, and compute of large number of phase pixels required for realistic 3D holography. The latest significant breakthrough in holography was 9years ago published in this paper - wherein they were able to achieve mid-air touchable/interactive 3D holography using a Femtosecond laser system considered safer than nanosecond lasers. Quite astounding work: arxiv.org/pdf/1506.06668

Realizing this breakthrough at scale is an unavoidably tempting research endeavor, super exciting especially with recent developments in machine learning and neural network algorithms demonstrating that computer-generated holograms can approach real-time processing.


Continuing the series of datasets from Russian platforms: Borda.ru Posts Dataset - https://huggingface.co/datasets/nyuuzyou/bordaru-posts

📊 Dataset highlights:

5,251,346 unique messages extracted from Borda.ru forums
Deduplicated based on content to remove spam and low-quality data
Each entry includes URL, username, and post content
Primarily in Russian language
Covers a wide range of topics from various discussion forums
Dedicated to public domain under Creative Commons Zero (CC0) license

🌐 Sourced from Borda.ru, a Russian platform for hosting diverse discussion forums on multiple subjects.

This dataset provides a valuable resource for analyzing Russian online discussions, sentiment analysis, and studying trends in Russian-language internet forums.


🚀 Call for all AI innovators in the United Arab Emirates!
Meta and Startupbootcamp MENA is launching the Llama Design Drive a one of its kind AI Accelerator in collaboration with the Roads and Transport Authority, Emirates, Dubai Holding and Chalhoub Group.

We are calling on all AI startups, developers, agencies and university labs to apply to this program for a chance to solve 1 out of 7 business challenges endorsed by our program partners using Llama 3.1, Meta's open source Large Language Model.

Startups selected into the program will attend a 4-week product design sprint at Dubai Future Foundation powered by Startupbootcamp with support from Meta engineering teams. Selected startups will also have the chance to be fast-tracked into a global program sponsored by Meta, with the chance of receiving up to $500,000 to further support the development of their AI products.

Application deadline is August 31 so be sure to apply fast!

For more information and to apply: https://llamadesigndrive.com


🙋🏻♂Hey there folks ,

I found this cool (new?) thing by Docker called Testcontainers , and there's an @ollama object that you can use to programmatically serve ephemeral containers and LLMs.

I made a post about it here : https://huggingface.co/blog/Tonic/localai-testcontainers

It's really useful, powerful and fun !

Demo coming soon 🤗


Black Forest Labs, BASED! 👏
FLUX.1 is more delightful, with good instruction following.
FLUX.1 dev(https://huggingface.co/black-forest-labs/FLUX.1-dev) with a 12B parameter distillation model, second only to Black Forest Labs' state-of-the-art model FLUX.1 pro. 🙀

Update 🤙Official demo:
https://huggingface.co/spaces/black-forest-labs/FLUX.1-dev

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Just dropped magpie-ultra-v0.1! The first open synthetic dataset generated with Llama 3.1 405B. Created with distilabel, it's our most advanced and compute-intensive pipeline to date. We made the GPUs of the cluster go brrrrr 🚀

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

Take it a look and tell us what you think! Probably, the models taking the most out of it are smol models 🤗 We will be improving the dataset in upcoming iterations!

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Looks like Black Forest Labs is taking the Hub by storm with its text-to-image models, holding 3 of the top 5 positions on the Trending dashboard

https://huggingface.co/black-forest-labs

Have you tried them yet?


OWLSAM2: text-promptable SAM2 🦉  https://huggingface.co/spaces/merve/OWLSAM2

Marrying cutting-edge zero-shot object detector OWLv2 🤝 mask generator SAM2 (small checkpoint)
Zero-shot segmentation with insane precision ⛵

I also uploaded all models with usage snippets and made a collection of SAM2 models and demos https://huggingface.co/collections/merve/sam2-66ac9deac6fca3bc5482fe30

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FLUX FP16 produces better quality than FP8 but requires 28 GB VRAM - Full comparisons - Also compared Dev vs Turbo model and 1024 vs 1536

check the file names in the below given imgsli to see all details

SwarmUI on L40S is used to compare - 1.82 it / second step speed for 1024x1024

imgsli link that compares all : https://imgsli.com/MjgzNzM1

SwarmUI full tutorial public post : https://www.patreon.com/posts/106135985

1-Click FLUX models downloader scripts for Windows, RunPod and Massed Compute are in below post

https://www.patreon.com/posts/109289967

free Kaggle account notebook that supports FLUX already : Download from here : https://www.patreon.com/posts/106650931

prompt :

(medium full shot) of (awe-inspiring snake) with muscular body, amber eyes, bronze brown armored scales, venomous fangs, coiling tail, gemstone-studded scales frills, set in a barren desert wasteland, with cracked earth and the remains of ancient structures, a place of mystery and danger, at dawn, ,Masterpiece,best quality, raw photo, realistic, very aesthetic, dark

CFG 1 - seed 1 - FLUX CFG is default : 3.5

Full public SwarmUI tutorial

Zero to Hero Stable Diffusion 3 Tutorial with Amazing SwarmUI SD Web UI that Utilizes ComfyUI

https://youtu.be/HKX8_F1Er_w

Full public Cloud SwarmUI tutorial

How to Use SwarmUI & Stable Diffusion 3 on Cloud Services Kaggle (free), Massed Compute & RunPod

https://youtu.be/XFUZof6Skkw

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Hugging Face famous organisations activity. Guess which one has the word "Open" in it 😂

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𝗦𝗔𝗠 𝟮 𝗿𝗲𝗹𝗲𝗮𝘀𝗲𝗱: 𝗡𝗲𝘄 𝗦𝗢𝗧𝗔 𝗼𝗻 𝘀𝗲𝗴𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻, 𝗯𝘆 𝗰𝗼𝗺𝗯𝗶𝗻𝗶𝗻𝗴 𝘀𝘆𝗻𝘁𝗵𝗲𝘁𝗶𝗰 𝗱𝗮𝘁𝗮 𝘄𝗶𝘁𝗵 𝗵𝘂𝗺𝗮𝗻 𝗳𝗲𝗲𝗱𝗯𝗮𝗰𝗸 🚀

It's a model for Object segmentation, for both image and video:
👉 input = a text prompt, or a click on a specific object
👉 output = the model draws a mask around the object. In video segmentation, the mask should follow the object's movements (it is then called a masklet)

💪 SAM 2 is 6x faster than the previous version, it now also works on a video, and it beats SOTA by far on both image and video segmentation tasks.

How did they pull that?

The main blocker for video segmentation was that data is really hard to collect: to build your training dataset, should you manually draw masks on every frame? That would be way too costly! ➡ As a result, existing video segmentation datasets have a real lack of coverage: few examples, few masklets drawn.

💡 Key idea: researchers they decided to use a segmentation model to help them collect the dataset.

But then it’s a chicken and egg problem: you need the model to create the dataset and the opposite as well? 🤔

⇒ To solve this, they build a data generation system that they scale up progressively in 3 successive manual annotations phases:

𝗦𝘁𝗲𝗽 𝟭: Annotators use only SAM + manual editing tools on each frame ⇒ Create 16k masklets across 1.4k videos

𝗦𝘁𝗲𝗽 𝟮: Then train a first SAM 2, add it in the loop to temporally propagate frames, and correct by re-doing a mask manually when an error has occured ⇒ This gets a 5.1x speedup over data collection in phase 1! 🏃 Collect 60k masklets

𝗦𝘁𝗲𝗽 𝟯: Now SAM 2 is more powerful, it has the “single click” prompting option, thus annotators can use it with simple clicks to re-annotate data.

They even add a completely automatic step to generate 350k more masklets!
And in turn, the model perf gradually increases.

I find this a great example of combining synthetic data generation with human annotation 👏


Running Gemma 2 2B at 41.66 tokens/s on my MacBook 💻🚀

- MLX Community's swift conversion
- One-line download from the Hub
- Small yet powerful on-device model

Try it yourself: https://huggingface.co/collections/mlx-community/google-gemma2-667dca89bc9abbfa34080066

#GemmaAI #OnDeviceAI #MachineLearning

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Free LLM/RAG course at https://mltblog.com/48GebAG - learn how to build custom architectures from scratch, and earn an LLM certification, all free.

The GenAItechLab Fellowship program allows participants to work on state-of-the-art, enterprise-grade projects, entirely for free, at their own pace, at home or in their workplace. The goal is to help you test, enhance, and further implement applications that outperform solutions offered by AI startups or organizations such as Google or OpenAI.

You will learn how to quickly build faster and lighter systems that deliver better results based on sound evaluation metrics, with a focus on case studies and best practices. Not the least, you will learn modern methods here to stay, designed by world-class expert and investor, Dr. Vincent Granville, founder of GenAItechLab.com.

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❤🔥 Just released version 2.0 of Argilla!

This small revolution includes:

🔌 You can now integrate with the Hugging Face Hub and get started in under five minutes.
🪂 A single `Dataset` class is now designed to handle multiple tasks.
🔧 It’s 100 times simpler to configure your dataset now with the new SDK!
📖 The documentation has been revamped to be cleaner and more user-friendly.
🍌  A new feature automates splitting annotation tasks among a team.
✍ The layout has been made more flexible to accommodate many use cases.

Check out the release highlights for more details: https://github.com/argilla-io/argilla/releases/tag/v2.0.0

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𝗟𝗹𝗮𝗺𝗮-𝟯.𝟭 𝗺𝗼𝗱𝗲𝗹𝘀 𝗳𝗶𝗻𝗮𝗹𝗹𝘆 𝗴𝗲𝘁 𝘁𝗵𝗲𝗶𝗿 𝗖𝗵𝗮𝘁𝗯𝗼𝘁 𝗔𝗿𝗲𝗻𝗮 𝗿𝗮𝗻𝗸𝗶𝗻𝗴 🎖

Given the impressive benchmarks published my Meta for their Llama-3.1 models, I was curious to see how these models would compare to top proprietary models on Chatbot Arena.

Now we've got the results! LMSys released the ELO derived from thousands of user votes for the new models, and here are the rankings:

💥 405B Model ranks 5th overall, in front of GPT-4-turbo! But behind GPT-4o, Claude-3.5 Sonnet and Gemini-advanced.
👏 70B Model climbs up to 9th rank ! From 1206 ➡ 1244.
👍 8B Model improves from 1152 ➡ 1170.

✅ This confirms that Llama-3.1 is a good contender for any task: any of its 3 model size is much cheaper to run than equivalent proprietary models!

For instance, here are the inference prices for the top models;
➤ GPT-4-Turbo inference price from OpenAI: $5/M input tokens, $15/M output tokens
➤ Llama-3.1-405B from HF API (for testing only): 3$/M for input or output tokens (Source linked in the first comment)
➤ Llama-3.1-405B from HF API (for testing only): free ✨

Get a head start on the HF API (resource by @andrewrreed) 👉 https://huggingface.co/learn/cookbook/enterprise_hub_serverless_inference_api

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💎 I created some shiny new Argilla datasets to go along with the 2.0 release!

```python
import argilla as rg

ds = rg.Dataset.from_hub(
    "argilla/multi-modal-vlm-visit-bench"
)
```

https://huggingface.co/collections/argilla/argilla-v20-compatible-datasets-66a8e670f351acac61a0421c

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🔥 Today, Writer dropped Palmyra-Med-70b and Palmyra-Fin-70b, two new domain-specific models that are setting a new standard for medical and financial model performance.

TL;DR
Palmyra-Med-70b
🔢 8k and 32k versions available
🚀 MMLU performance of ~86%, outperforming other top models
👨⚕ Great for diagnosing, planning treatments, medical research, insurance coding and billing
📃 Open-model license for non-commercial use cases
🤗 Available on Hugging Face: https://huggingface.co/Writer/Palmyra-Med-70B
💾 Live on NVIDIA NIM: https://build.nvidia.com/writer/palmyra-med-70b

Palmyra-Fin-70b
🚀 Passed the CFA Level III exam with a 73% score — the first model to do so
💸 Skilled at complex tasks like investment research, financial analysis, and sentiment analysis
📈 Outperformed other top models on a long-fin-eval test of real-world use cases
📃 Open-model license for non-commercial use cases
🤗 Available on Hugging Face: https://huggingface.co/Writer/Palmyra-Fin-70B-32K
💾 Live on NVIDIA NIM: https://build.nvidia.com/writer/palmyra-fin-70b-32k

Try them out and let us know what you think!

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Excited to announce the release of our high-quality Llama-3.1 8B 4-bit HQQ/calibrated quantized model! Achieving an impressive 99.3% relative performance to FP16, it also delivers the fastest inference speed for transformers.

https://huggingface.co/mobiuslabsgmbh/Llama-3.1-8b-instruct_4bitgs64_hqq_calib

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Hey everyone 🤗!
Check out this cool new space from Finegrain: https://huggingface.co/spaces/finegrain/finegrain-object-eraser

Under the hoods, it's a pipeline of models (currently exposed via an API) that allows you to easily erase any object from your image just by naming it or selecting it! Not only will the object disappear, but so will its effects on the scene, like shadows and reflections. Built on top of Refiners, our micro-framework for simple foundation model adaptation (feel free to star it on GitHub if you like it: https://github.com/finegrain-ai/refiners)

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Journalists, this is a must-read for your career evolution. I just read @ndiakopoulos on "The Impact of Generative AI on Journalistic Labor". Here are my 5 takeaways:

🚀 LLMs could make 83% of reporter tasks and 76% of editor tasks way more efficient. "It’s important to emphasize that these figures are fundamentally about augmentation rather than automation".

🧩 Four emerging job clusters to consider: AI-doers, AI-users, AI-strategizers, and AI-reporters. Where do you fit?

🌊 US newsrooms "will need people (1) who have the skills to use the current generation of LLMs and (2) who can develop the bespoke software to unlock their full potential, particularly if building new in-house tools". Get ahead of the curve.

🔑 Key takeaway: upskill in AI. It's not just about using tools, but understanding how to integrate them into your workflow.

📚 News orgs "would be wise to accelerate hiring and invest in upskilling their existing workforce". Take advantage or seek out learning opportunities.

Journalists: What AI skills are you planning to develop? How might this reshape your role?

👉 Read the full blog post here: https://generative-ai-newsroom.com/the-impact-of-generative-ai-on-journalistic-labor-e87a6c333245 It's worth your time if you're thinking about the future of journalism & your career.

#AIinJournalism #CareerEvolution #FutureofNews


They should make a thing like google colab but you can have unlimited free access to a whole datacenter that would be cool. like if you agree

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𝗧𝗵𝗲 𝗵𝘂𝗴𝗲 𝗰𝗼𝘀𝘁 𝗼𝗳 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗼𝗻 𝗳𝗿𝗼𝗻𝘁𝗶𝗲𝗿 𝗟𝗟𝗠𝘀 💸

Google DeepMind recently released a great paper that shows optimal hyperparameters to train across different regimes: Scaling Exponents Across Parameterizations and Optimizers, with data from 10,000 training runs.

One engineer decided to quantify the price of such a large-scale experiment.

😬 And the bill is hefty: ~13M USD

This exact number is to take with a grain of salt because many approximations were necessary to get the final result.

⛔ But still this ballpark means that for this sole experiment, the price is way over what most startups or research labs could afford.

This means that open-sourcing research is more important than ever, to put everyone in the ecosystem on a roughly equal footing. Don't let OpenAI run first, they'll keep everything for themselves!

Read the full post that quantifies the paper's cost 👉 https://152334h.github.io/blog/scaling-exponents/

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neuralmagic/Meta-Llama-3.1-405B-Instruct-FP8

Requant of the big llama, using 20% less memory

https://huggingface.co/neuralmagic/Meta-Llama-3.1-405B-Instruct-FP8


With larger and larger diffusion transformers coming up, it's becoming increasingly important to have some good quantization tools for them.

We present our findings from a series of experiments on quantizing different diffusion pipelines based on diffusion transformers.

We demonstrate excellent memory savings with a bit of sacrifice on inference latency which is expected to improve in the coming days.

Diffusers 🤝 Quanto ❤

This was a juicy collaboration between @dacorvo and myself.

Check out the post to learn all about it
https://huggingface.co/blog/quanto-diffusers

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Kling AI Video is FINALLY Public (All Countries), Free to Use and MIND BLOWING - Full Tutorial > https://youtu.be/zcpqAxYV1_w

You probably seen those mind blowing AI made videos. And the day has arrived. The famous Kling AI is now worldwide available for free. In this tutorial video I will show you how to register for free with just email to Kling AI and use its mind blowing text to video animation, image to video animation and text to image, and image to image capabilities. This video will show you non-cherry pick results so you will know the actual quality and capability of the model unlike those extremely cherry pick example demos. Still, #KlingAI is the only #AI model that competes with OpenAI's #SORA and it is real to use.

🔗 Kling AI Official Website ⤵
▶ https://www.klingai.com/

🔗 SECourses Discord Channel to Get Full Support ⤵
▶ https://discord.com/servers/software-engineering-courses-secourses-772774097734074388

🔗 Our GitHub Repository ⤵
▶ https://github.com/FurkanGozukara/Stable-Diffusion

🔗 Our Reddit ⤵
▶ https://www.reddit.com/r/SECourses/

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