🚀 Introducing ColFlor: An Efficient, OCR-Free Vision-Language Document Retrieval Model 🌟

Earlier this year, ColPali revolutionized document retrieval by eliminating the need for error-prone OCR pipelines. Instead, it directly processes the document images. However, with its 3 billion parameters, ColPali is computationally heavy for large-scale applications.

That’s where ColFlor comes in—a smaller, faster alternative! 🎉 At 17x smaller than ColPali, ColFlor offers a more efficient, OCR-free document retrieval solution, making it ideal for users with limited computing resources (GPU Poor). 💡

Key Highlights:
🧠 174M parameters (vs. 3B for ColPali)
⚡ 9.8x faster query encoding, 5.25x faster image encoding
📉 Only 1.8% performance drop on text-rich English documents

Check out the full blog post for more insights on modeling, training, and evaluations across various document retrieval tasks! 🚀
Also, feel free to try our demo on huggingface 🤗

🔗 Resources:
📄 Blog post: https://huggingface.co/blog/ahmed-masry/colflor
🧠 Model: https://huggingface.co/ahmed-masry/ColFlor
💻 Demo: https://huggingface.co/spaces/ahmed-masry/ColFlor-Demo
🏋♂ Training code: https://github.com/AhmedMasryKU/colflor
📊 Evaluation code: https://github.com/AhmedMasryKU/vidore-benchmark-colflor


I built a Hyper Dimensional Computing (HDC) Encoder and Decoder and I also built out all of the code needed to replace the Encoder and Decoder of a Llama model with this HDC model, then train the Llama model on the HDC Encoder/Decoder. All MIT licensed. Here is a video where I break it all down. I can answer any questions about this project or help anyone out where I can. I am not a super developer or anything and I don't have access to enough compute to train this on a large dataset: https://youtu.be/4VsZpGaPK4g


Here is how we can calculate the size of any LLM model:

Each parameter in LLM models is typically stored as a floating-point number. The size of each parameter in bytes depends on the precision.

32-bit precision: Each parameter takes 4 bytes.
16-bit precision: Each parameter takes 2 bytes

To calculate the total memory usage of the model:
Memory usage (in bytes) = No. of Parameters × Size of Each Parameter

For example:
32-bit Precision (FP32)
In 32-bit floating-point precision, each parameter takes 4 bytes.
Memory usage in bytes = 1 billion parameters × 4 bytes
1,000,000,000 × 4 = 4,000,000,000 bytes
In gigabytes: ≈ 3.73 GB

16-bit Precision (FP16)
In 16-bit floating-point precision, each parameter takes 2 bytes.
Memory usage in bytes = 1 billion parameters × 2 bytes
1,000,000,000 × 2 = 2,000,000,000 bytes
In gigabytes: ≈ 1.86 GB

It depends on whether you use 32-bit or 16-bit precision, a model with 1 billion parameters would use approximately 3.73 GB or 1.86 GB of memory, respectively.

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🎓 Introducing Ukr-lit.com.ua Presentations Dataset - https://huggingface.co/datasets/nyuuzyou/ukr-lit

Dataset highlights:
- 18,001 presentations from ukr-lit.com.ua, a platform for storing and viewing presentations covering a wide range of subjects in Ukrainian school education
- Primarily in Ukrainian, with some Russian and English content
- Each entry includes: URL, title, download URL, filepath, and extracted text content (where available)
- Contains original PPT/PPTX files in addition to metadata
- Data covers a broad spectrum of educational topics and subjects taught in Ukrainian schools
- Dedicated to the public domain under Creative Commons Zero (CC0) license

The dataset can be used for analyzing educational presentation content across various subjects in Ukrainian and other languages, text classification tasks, and information retrieval systems. It's particularly valuable for examining trends in Ukrainian school education, teaching methodologies, and presentation materials used across different academic disciplines. The inclusion of original files allows for in-depth analysis of presentation formats and structures commonly used in Ukrainian educational settings, providing insights into the diverse range of subjects and teaching approaches in the Ukrainian school system.


🌍 Super cool visualization of global PUT requests to Hugging Face over 24 hours, coded by object size, thanks to @port8080!

We're putting this analysis to work to help us architect a more geo-distributed system for the HF storage backend.

Originally shared on LinkedIn: https://www.linkedin.com/posts/ajitbanerjee_one-of-the-joys-of-working-on-the-xethub-activity-7252688424732614656-tFGD

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Big news! You can now build strong ML models without days of human labelling

You simply:
- Define your dataset, including annotation guidelines, labels and fields
- Optionally label some records manually.
- Use an LLM to auto label your data with a human (you? your team?) in the loop!

Get started with this blog post:
https://huggingface.co/blog/sdiazlor/custom-text-classifier-ai-human-feedback


You can now build a custom text classifier without days of human labeling!

👍 LLMs work reasonably well as text classifiers.
👎 They are expensive to run at scale and their performance drops in specialized domains.

👍 Purpose-built classifiers have low latency and can potentially run on CPU.
👎 They require labeled training data.

Combine the best of both worlds: the automatic labeling capabilities of LLMs and the high-quality annotations from human experts to train and deploy a specialized model.

Blog: https://huggingface.co/blog/sdiazlor/custom-text-classifier-ai-human-feedback

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By far the coolest release of the day!
> The Open LLM Leaderboard, most comprehensive suite for comparing Open LLMs on many benchmarks, just released a comparator tool that lets you dig into the detail of differences between any models.

Here's me checking how the new Llama-3.1-Nemotron-70B that we've heard so much compares to the original Llama-3.1-70B. 🤔🔎

Try it out here 👉 https://huggingface.co/spaces/open-llm-leaderboard/comparator


🚨 We’ve just released a new tool to compare the performance of models in the 🤗 Open LLM Leaderboard: the Comparator 🎉
https://huggingface.co/spaces/open-llm-leaderboard/comparator

Want to see how two different versions of LLaMA stack up? Let’s walk through a step-by-step comparison of LLaMA-3.1 and LLaMA-3.2. 🦙🧵👇

1/ Load the Models' Results
- Go to the 🤗 Open LLM Leaderboard Comparator: https://huggingface.co/spaces/open-llm-leaderboard/comparator
- Search for "LLaMA-3.1" and "LLaMA-3.2" in the model dropdowns.
- Press the Load button. Ready to dive into the results!

2/ Compare Metric Results in the Results Tab 📊
- Head over to the Results tab.
- Here, you’ll see the performance metrics for each model, beautifully color-coded using a gradient to highlight performance differences: greener is better! 🌟
- Want to focus on a specific task? Use the Task filter to hone in on comparisons for tasks like BBH or MMLU-Pro.

3/ Check Config Alignment in the Configs Tab ⚙
- To ensure you’re comparing apples to apples, head to the Configs tab.
- Review both models’ evaluation configurations, such as metrics, datasets, prompts, few-shot configs...
- If something looks off, it’s good to know before drawing conclusions! ✅

4/ Compare Predictions by Sample in the Details Tab 🔍
- Curious about how each model responds to specific inputs? The Details tab is your go-to!
- Select a Task (e.g., MuSR) and then a Subtask (e.g., Murder Mystery) and then press the Load Details button.
- Check out the side-by-side predictions and dive into the nuances of each model’s outputs.

5/ With this tool, it’s never been easier to explore how small changes between model versions affect performance on a wide range of tasks. Whether you’re a researcher or enthusiast, you can instantly visualize improvements and dive into detailed comparisons.

🚀 Try the 🤗 Open LLM Leaderboard Comparator now and take your model evaluations to the next level!


This has to be the first peak performance level use case of a non-autoregressive architecture for TTS. Flow matching for the win!!

Demo: https://huggingface.co/spaces/mrfakename/E2-F5-TTS
Model: https://huggingface.co/SWivid/E2-TTS


All the way from Korea, a novel approach called Mentor-KD significantly improves the reasoning abilities of small language models.

Mentor-KD introduces an intermediate-sized "mentor" model to augment training data and provide soft labels during knowledge distillation from large language models (LLMs) to smaller models.

Broadly, it’s a two-stage process:
1) Fine-tune the mentor on filtered Chain-of-Thought (CoT) annotations from an LLM teacher.
2) Use the mentor to generate additional CoT rationales and soft probability distributions.

The student model is then trained using:
- CoT rationales from both the teacher and mentor (rationale distillation).
- Soft labels from the mentor (soft label distillation).

Results show that Mentor-KD consistently outperforms baselines, with up to 5% accuracy gains on some tasks.

Mentor-KD is especially effective in low-resource scenarios, achieving comparable performance to baselines while using only 40% of the original training data.

This work opens up exciting possibilities for making smaller, more efficient language models better at complex reasoning tasks.

What are your thoughts on this approach?


https://huggingface.co/nvidia/Llama-3.1-Nemotron-70B-Instruct-HF is now available in HuggingChat if you want to try it!

https://huggingface.co/chat/models/nvidia/Llama-3.1-Nemotron-70B-Instruct-HF

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🚨 I have $3,500 in Azure credits, including access to an H100 (96 Go), expiring on November 12, 2024.

I won’t be able to use it all myself, so I’m reaching out to the @huggingface community: Are there any open-source projets with data ready for some compute power?

 Let’s collaborate and make the most of it together 🔗


I am here to provide you with the premium codes you want just by informing me your requirements. #program #AI #code
Premium_Code
https://hf.co/chat/assistant/670fd7b2ad7fdbb38ff98102


Hi there HuggingFacers!

Have you ever dreamt of an improbable books crossover, like Frodo from 𝘓𝘰𝘳𝘥 𝘰𝘧 𝘵𝘩𝘦 𝘙𝘪𝘯𝘨𝘴 becoming the main character of the 𝘖𝘥𝘺𝘴𝘴𝘦𝘺 or Emma Bovary from 𝘔𝘢𝘥𝘢𝘮𝘦 𝘉𝘰𝘷𝘢𝘳𝘺 acting as a modern-days Shakespearean Juliet?

Well, all of this is now possible! I'm thrilled to introduce my latest opensource product for storytelling: 𝐛𝐨𝐨𝐤𝐬-𝐦𝐢𝐱𝐞𝐫-𝐚𝐢 𝐯𝟎.𝟎.𝟎 !

Built with ReactJS and shipped directly to you on Spaces thanks to Docker, this webapp combines the power of two AI tools:

- gpt-4o-mini by OpenAI, which takes care of cooking new and intriguing plots starting from the user's instructions, the titles and the summaries of the two books to mix (summaries are scraped through Wikipedia)
- text2img realtime API by ModelsLab, which provides a stable diffusion pipeline to create a thumbnail for your newly-generated story

Everything is provided under a simple and intuitive UI, which uses chatscope's React template kit.
Curious of trying? The app is already live at:

https://huggingface.co/spaces/as-cle-bert/books-mixer-ai

And you can also have a tour of the GitHub repo (and leave a little ⭐ while you're there):

https://github.com/AstraBert/books-mixer-ai

The documentation is still under construction, but will become available soon😊

Have fun!📚📚


Today I was able to solve a very difficult coding session with GPT-4o which ended up solving integrations on a very large scale.   So I decided to look a bit more into how its reasoners work.  Below is a fun markdown emoji outline about what I learned today and what I'm pursuing.

Hope you enjoy!  Cheers, Aaron.

Also here are my favorite last 4 spaces I am working on:
1. GPT4O:  https://huggingface.co/spaces/awacke1/GPT-4o-omni-text-audio-image-video
2. Claude:
 https://huggingface.co/spaces/awacke1/AnthropicClaude3.5Sonnet-ACW
3. MSGraph M365: https://huggingface.co/spaces/awacke1/MSGraphAPI
4. Azure Cosmos DB: Now with Research AI!  https://huggingface.co/spaces/awacke1/AzureCosmosDBUI

# 🚀 OpenAI's O1 Models: A Quantum Leap in AI

## 1. 🤔 From 🦜 to 🧠: O1's Evolution

- **Thinking AI**: O1 ponders before replying; GPT models just predict. 💡

## 2. 📚 AI Memory: 💾 + 🧩 = 🧠

- **Embeddings & Tokens**: Words ➡ vectors, building knowledge. 📖

## 3. 🔍 Swift Knowledge Retrieval

- **Vector Search & Indexing**: O1 finds info fast, citing reliable sources. 🔎📖

## 4. 🌳 Logic Trees with Mermaid Models

- **Flowchart Reasoning**: O1 structures thoughts like diagrams. 🎨🌐

## 5. 💻 Coding Mastery

- **Multilingual & Current**: Speaks many code languages, always up-to-date. 💻🔄

## 6. 🏆 Breaking Records

- **92.3% MMLU Score**: O1 outperforms humans, setting new AI standards. 🏅

## 7. 💡 Versatile Applications

- **Ultimate Assistant**: From fixing code to advancing research. 🛠🔬

## 8. 🏁 Racing Toward AGI

- **OpenAI Leads**: O1 brings us closer to true AI intelligence. 🚀

## 9. 🤖 O1's Reasoning Pillars

- **🧠 Chain of Thought**: Step-by-step logic.
- **🎲 MCTS**: Simulates options, picks best path.
- **🔍 Reflection**: Self-improves autonomously.
- **🏋♂ Reinforcement Learning**: Gets smarter over time.

---

*Stay curious, keep coding!* 🚀


In regards to the latest mistral model and GGUFs for it:

Yes, they may be subpar and may require changes to llama.cpp to support the interleaved sliding window

Yes, I got excited when a conversion worked and released them ASAP

That said, generation seems to work right now and seems to mimic the output from spaces that are running the original model

I have appended -TEST to the model names in an attempt to indicate that they are not final or perfect, but if people still feel mislead and that it's not the right thing to do, please post (civilly) below your thoughts, I will highly consider pulling the conversions if that's what people think is best. After all, that's what I'm here for, in service to you all !


🔥 Meta AI just blessed us with CoTracker v3, bleeding edge point tracking foundation model 🤩
model: https://huggingface.co/facebook/cotracker3
demo: https://huggingface.co/spaces/facebook/cotracker

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Today I found out about the existence of https://huggingface.co/utter-project/EuroLLM-1.7B-Instruct and unexpectedly it is really good. I think it's a very underrated model - give it a try https://huggingface.co/spaces/nyuuzyou/EuroLLM-1.7B-Instruct


Ever wondered how neural networks actually work under the hood?

In my latest video, I break down the core mathematical concepts behind neural networks in a way that's easy for IT professionals to understand. We'll explore:

- Neurons as logic gates
- Weighted sums and activation functions
- Gradient descent and backpropagation

No complex equations or jargon, just clear explanations and helpful visuals!

➡ Watch now and unlock the mysteries of neural networks: https://youtu.be/L5_I1ZHoGnM


Philosopher Gilles Deleuze in 1985-86 about society of control, probabilities, and power. Visionary words in an era of autoregressive models:

"The biopolitics of populations appears when right sets about administering life, says Foucault, administering life in any open multiplicities whatever. You see the importance of the difference between discipline and biopolitics. The one is in an open space, with large multiplicities to which limits are not assignable. They can only be treated by the calculus of probabilities, hence the development of the calculus of probabilities and the meaning [sens] of the social control of probabilities, the probabilities of marriage in a nation, the probabilities of mortality, probabilities of natality. Natality, nuptiality, mortality …

... When Foucault directly addresses the question of power, namely, one of his great theses: no, power does not repress, or it represses only secondarily. What does it do? It does something much more profound and, doubtless, more formidable that repressing: it forms, it shapes. It does not silence, it does worse: it makes speak. It disciplines, it standardizes [normalise]. But repression is entirely secondary in relation to the positive operations of power.

Power does not repress, it disciplines, it manages, it controls, it standardizes, etcetera. It does not silence, it makes speak. It does not prevent acting, it makes act."

From the Deleuze Seminars at Université Paris 8 translated by Purdue University -> https://deleuze.cla.purdue.edu/


The Synthetic Data Generator now directly integrates with Argilla, so you can generate and curate your own high-quality datasets from pure natural language!

Up next -> include dataset generation for text classification.
Other suggestions? Let us know.

Space: https://huggingface.co/spaces/argilla/synthetic-data-generator


While Google's Transformer might have introduced "Attention is all you need," Microsoft and Tsinghua University are here with the DIFF Transformer, stating, "Sparse-Attention is all you need."

The DIFF Transformer outperforms traditional Transformers in scaling properties, requiring only about 65% of the model size or training tokens to achieve comparable performance.

The secret sauce? A differential attention mechanism that amplifies focus on relevant context while canceling out noise, leading to sparser and more effective attention patterns.

How?
- It uses two separate softmax attention maps and subtracts them.
- It employs a learnable scalar λ for balancing the attention maps.
- It implements GroupNorm for each attention head independently.
- It is compatible with FlashAttention for efficient computation.

What do you get?
- Superior long-context modeling (up to 64K tokens).
- Enhanced key information retrieval.
- Reduced hallucination in question-answering and summarization tasks.
- More robust in-context learning, less affected by prompt order.
- Mitigation of activation outliers, opening doors for efficient quantization.

Extensive experiments show DIFF Transformer's advantages across various tasks and model sizes, from 830M to 13.1B parameters.

This innovative architecture could be a game-changer for the next generation of LLMs. What are your thoughts on DIFF Transformer's potential impact?


MixGen3 is an innovative image generation service that utilizes LoRA (Low-Rank Adaptation) models. Its key features include:

Integration of various LoRA models: Users can explore and select multiple LoRA models through a gallery.
Combination of LoRA models: Up to three LoRA models can be combined to express unique styles and content.
User-friendly interface: An intuitive interface allows for easy model selection, prompt input, and image generation.
Advanced settings: Various options are provided, including image size adjustment, random seed, and advanced configurations.

Main applications of MixGen3:

Content creation
Design and illustration
Marketing and advertising
Education and learning

Value of MixGen3:

Enhancing creativity
Time-saving
Collaboration possibilities
Continuous development

Expected effects:

Increased content diversity
Lowered entry barrier for creation
Improved creativity
Enhanced productivity

MixGen3 is bringing a new wave to the field of image generation by leveraging the advantages of LoRA models. Users can experience the service for free at
https://openfree-mixgen3.hf.space

contacts: arxivgpt@gmail.com

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Model is always disabled?
#script...
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("distilbert/distilgpt2",
                                             token="xxxxxx")

That loads the model fine.  But if used by index returned from VectorStoreIndex for QDrant like this:

#script...
query_engine = index_from_nodes.as_query_engine(llm=model, streaming=True)

response = query_engine.query(
    "What is formula 1?"
)

response.print_response_stream()

It errors out with a disabled error:
AssertionError                            Traceback (most recent call last)
Cell In[34], line 1
----> 1 query_engine = index_from_nodes.as_query_engine(llm=model, streaming=True)
      3 response = query_engine.query(
      4     "What is formula 1?"
      5 )
      7 response.print_response_stream()

File ~/miniconda/lib/python3.9/site-packages/llama_index/core/indices/base.py:376, in BaseIndex.as_query_engine(self, llm, **kwargs)
    370 from llama_index.core.query_engine.retriever_query_engine import (
    371     RetrieverQueryEngine,
    372 )
    374 retriever = self.as_retriever(**kwargs)
    375 llm = (
--> 376     resolve_llm(llm, callback_manager=self._callback_manager)
    377     if llm
    378     else Settings.llm
    379 )
    381 return RetrieverQueryEngine.from_args(
    382     retriever,
    383     llm=llm,
    384     **kwargs,
    385 )

File ~/miniconda/lib/python3.9/site-packages/llama_index/core/llms/utils.py:102, in resolve_llm(llm, callback_manager)
     99     print("LLM is explicitly disabled. Using MockLLM.")
    100     llm = MockLLM()
--> 102 assert isinstance(llm, LLM)
    104 llm.callback_manager = callback_manager or Settings.callback_manager
    106 return llm

AssertionError:

So why is the LLM disabled?
Thanks!


📢 We are giving extra two weeks before switching to the final stage of RuOpinionNE-2024.
⏰  The final stage starts since 1-st of November 2024.
We have already first baseline submission by 👨💻 RefalMachine that showcase F1 = 0.17  based on Qwen2 model series.

For those who wish to attend:
📊 Codalab: https://codalab.lisn.upsaclay.fr/competitions/20244
🗒 Task: https://codalab.lisn.upsaclay.fr/competitions/20244#learn_the_details-overview
🔔 Updates: https://t.me/RuOpinionNE2024

🙋 Questions: https://nicolay-r.github.io/
🧪 Past experiments: https://github.com/nicolay-r/RuSentNE-LLM-Benchmark


Professors should ask students to write blog posts based on their final projects instead of having them do paper-like reports.

A single blog post, accessible to the entire internet, can have a greater career impact than dozens of reports that nobody will read.


Who's going to get to the most liked model on Hugging Face first: StabilityAI, Meta, Black Forest or someone else? The race is on!


New Book: Building Disruptive AI & LLM Technology from Scratch https://mltblog.com/404F1BZ

This book features new advances in game-changing AI and LLM technologies built by GenAItechLab.com. Written in simple English, it is best suited for engineers, developers, data scientists, analysts, consultants and anyone with an analytic background interested in starting a career in AI. The emphasis is on scalable enterprise solutions, easy to implement, yet outperforming vendors both in term of speed and quality, by several orders of magnitude.

Each topic comes with GitHub links, full Python code, datasets, illustrations, and real-life case studies, including from Fortune 100 company. Some of the material is presented as enterprise projects with solution, to help you build robust applications and boost your career. You don’t need expensive GPU and cloud bandwidth to implement them: a standard laptop works.

➡ Part 1: Hallucination-Free LLM with Real-Time Fine-Tuning

➡ Part 2: Outperforming Neural Nets and Classic AI

➡ Part 3: Innovations in Statistical AI

About the author

Vincent Granville is a pioneering GenAI scientist and machine learning expert, co-founder of Data Science Central (acquired by a publicly traded company in 2020), Chief AI Scientist at ML Techniques and GenAI Techlab, former VC-funded executive, author (Elsevier) and patent owner — one related to LLM. Vincent’s past corporate experience includes Visa, Wells Fargo, eBay, NBC, Microsoft, and CNET.

➡ See content and get your copy, at https://mltblog.com/404F1BZ


Don't use an LLM when you can use a much cheaper model.

The problem is that no one tells you how to actually do it.

Just picking a pre-trained model (e.g., BERT) and throwing it at your problem won't work!

If you want a small model to perform well on your problem, you need to fine-tune it.

And to fine-tune it, you need data.

The good news is that you don't need a lot of data but instead high-quality data for your specific problem.

In the latest livestream, I showed you guys how to get started with Argilla on the Hub! Hope to see you at the next one.

https://www.youtube.com/watch?v=BEe7shiG3rY


AgentHarm: A Benchmark for Measuring Harmfulness of LLM Agents (2410.09024)

🚨 New Agent Benchmark 🚨
AgentHarm: A Benchmark for Measuring Harmfulness of LLM Agents

https://huggingface.co/datasets/ai-safety-institute/AgentHarm

Collaboration between UK AI Safety Institute and Gray Swan AI to create a dataset for measuring harmfulness of LLM agents.

The benchmark contains both harmful and benign sets of 11 categories with varied difficulty levels and detailed evaluation, not only testing success rate but also tool level accuracy.

We provide refusal and accuracy metrics across a wide range of models in both no attack and prompt attack scenarios.

https://huggingface.co/papers/2410.09024


New York Times to Perplexity: Stop Using Our Stuff

The publisher has sent generative-AI startup Perplexity a “cease and desist” notice demanding that the firm stop accessing and using its content, according to a copy of the letter reviewed by The Wall Street Journal.

Perplexity CEO Aravind Srinivas said in an interview that Perplexity isn’t ignoring the Times’s efforts to block crawling of its site. He said the company plans on responding to the legal notice by the Times’s deadline of Oct. 30.

“We are very much interested in working with every single publisher, including the New York Times,” Srinivas said. “We have no interest in being anyone’s antagonist here.”

https://www.wsj.com/business/media/new-york-times-to-bezos-backed-ai-startup-stop-using-our-stuff-20faf2eb?mod=rss_Technology


I just published the first article in a pair. I could make it a longer tailed series, in case you liked em. This one dives into self-hosting Metaflow without needing S3, illustrated with a version tailored for Google Colab.
find it @ https://huggingface.co/blog/Aurelien-Morgan/stateful-metaflow-on-colab


👋 We're excited to share our project on mobile face detection using MediaPipe and ZETIC.MLange.

🔑 Key highlights:
1. Introduction to Mediapipe face detection model
2. Developing on-device AI applications with ZETIC.MLange
3. Guide to creating object detection apps utilizing Mobile NPUs

📱 Learn how to build a high-performance face detection app that operates entirely on-device, no cloud required! We explore:
- Real-time face analysis techniques
- Enhanced security measures
- Privacy protection strategies

🎥 Check out our demo video showcasing real-time face detection:
Face detection Demo: https://youtu.be/GXtJKk7MdjQ?si=41UFKL8IBwx5nlxs

📚 Full Tutorial
For a step-by-step guide and in-depth discussion, read our full blog post:
https://zetic.ai/blog/implementing-face-detection-on-device-ai-with-zetic-mlange

We'd love to hear your thoughts, experiences, or questions in the comments below.


Good folks from Universitat Politècnica de Catalunya, University of Groningen, and Meta have released "A Primer on the Inner Workings of Transformer-based Language Models."

They don't make survey papers like they used to, but this is an exciting new survey on Transformer LM interpretability!

This comprehensive survey provides a technical deep dive into:

• Transformer architecture components (attention, FFN, residual stream)
• Methods for localizing model behavior:
  - Input attribution (gradient & perturbation-based)
  - Component importance (logit attribution, causal interventions)
• Information decoding techniques:
  - Probing, linear feature analysis
  - Sparse autoencoders for disentangling features
• Key insights on model internals:
  - Attention mechanisms (induction heads, copy suppression)
  - FFN neuron behaviors
  - Residual stream properties
  - Multi-component emergent behaviors

The paper offers a unified notation and connects insights across different areas of interpretability research. It's a must-read for anyone working on understanding large language models!

Some fascinating technical highlights:
- Detailed breakdowns of attention head circuits (e.g., IOI task)
- Analysis of factual recall mechanisms
- Overview of polysemanticity and superposition
- Discussion of grokking as circuit emergence

What interpretability insights do you find most intriguing?


Transformers are not all we need, that is being proven repeatedly now as more alternative frameworks emerge. Another such framework is Kolmogorov Arnold Network based Transformers. I break down exactly how these differ from Perceptron based Transformers and give you the link to my Colab where I create a model based on the research paper that absolutely destroys a standard Transformers based model. Check out the video here: https://www.youtube.com/watch?v=Sw0euxNZCc4


To demonstrate that it was possible, I performed a "trapezoid" gradient merge of a Llama 3 8B model onto Llama 3.1 8B Instruct, favoring the L3.1 model at the ends in order to preserve coherence and limiting the influence of the L3 model to at most 0.1 weight. Tested to 16k context length.
https://huggingface.co/grimjim/Llama-Nephilim-Metamorphosis-v2-8B


🎙 Introducing LiveATC Recordings (Partial 2024-08-26) Dataset - https://huggingface.co/datasets/nyuuzyou/liveatc

Dataset highlights:

- 21,172 air traffic control audio recordings from LiveATC.net for August 26, 2024
- Multilingual content, primarily in English with potential for other languages
- Each entry includes: audio file, ICAO airport code, facility type, date, and time
- Contains original MP3 files stored in .tar.zst archives, organized by ICAO airport code
- Data covers various airports and ATC facilities worldwide
- Subject to LiveATC.net's Terms of Use for personal, non-commercial use only

The dataset can be used for audio classification, automatic speech recognition, and analysis of air traffic control communications. The inclusion of recordings from multiple airports allows for comparative analysis across different locations and facility types.


𝗞𝗲𝘆 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀:

𝗞𝗲𝘆 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀:

⚡ 𝐓𝐡𝐢𝐬 𝐦𝐨𝐧𝐭𝐡'𝐬 𝐦𝐨𝐬𝐭 𝐢𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐭 𝐛𝐫𝐞𝐚𝐤𝐭𝐡𝐫𝐨𝐮𝐠𝐡: 𝐃𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐭𝐢𝐚𝐥 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐞𝐫 𝐯𝐚𝐬𝐭𝐥𝐲 𝐢𝐦𝐩𝐫𝐨𝐯𝐞𝐬 𝐚𝐭𝐭𝐞𝐧𝐭𝐢𝐨𝐧 ⇒ 𝐛𝐞𝐭𝐭𝐞𝐫 𝐫𝐞𝐭𝐫𝐢𝐞𝐯𝐚𝐥 𝐚𝐧𝐝 𝐟𝐞𝐰𝐞𝐫 𝐡𝐚𝐥𝐥𝐮𝐜𝐢𝐧𝐚𝐭𝐢𝐨𝐧𝐬!

Thought that self-attention could not be improved anymore?

Microsoft researchers have dropped a novel "differential attention" mechanism that amplifies focus on relevant context while canceling out noise. It sounds like a free lunch, but it does really seem to vastly improve LLM performance!

𝗞𝗲𝘆 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀:

🧠 Differential attention computes the difference between two separate softmax attention maps, canceling out noise and promoting sparse attention patterns

🔥 DIFF Transformer outperforms standard Transformers while using 35-40% fewer parameters or training tokens

📏 Scales well to long contexts up to 64K tokens, leveraging increasing context length more effectively

🔎 Dramatically improves key information retrieval, enhancing in-context learning, and possibly reducing risk of hallucinations 🤯

🔢 Reduces activation outliers, potentially enabling lower-bit quantization without performance drop!

⚙ Can be directly implemented using existing FlashAttention kernels

This new architecture could lead much more capable LLMs, with vastly improved strengths in long-context understanding and factual accuracy.

But they didn’t release weights on the Hub: let’s wait for the community to train the first open-weights DiffTransformer! 🚀

Read their paper 👉 https://huggingface.co/papers/2410.05258


Huge FLUX LoRA vs Fine Tuning / DreamBooth Experiments Completed, Moreover Batch Size 1 vs 7 Fully Tested as Well, Not Only for Realism But Also for Stylization - 15 vs 256 images having datasets compared as well (expressions / emotions tested too) - Used Kohya GUI for training

Full files and article : https://www.patreon.com/posts/112099700

Download images in full resolution to see prompts and model names

All trainings are done with Kohya GUI, perfectly can be done locally on Windows, and all trainings were 1024x1024 pixels

Fine Tuning / DreamBooth works as low as 6 GB GPUs (0 quality degrade totally same as 48 GB config)

Best quality of LoRA requires 48 GB GPUs , 24 GB also works really good and minimum 8 GB GPU is necessary for LoRA (lots of quality degrade)

Full size grids are also shared for the followings: https://www.patreon.com/posts/112099700

Additionally, I have shared full training entire logs that you can see each checkpoint took time. I have shared best checkpoints, their step count and took time according to being either LoRA, Fine Tuning or Batch size 1 or 7 or 15 images or 256 images, so a very detailed article regarding completed.

Check the images to see all shared files in the post.

Furthermore, a very very detailed analysis having article written and all latest DreamBooth / Fine Tuning configs and LoRA configs are shared with Kohya GUI installers for both Windows, Runpod and Massed Compute.

Moreover, I have shared new 28 realism and 37 stylization testing prompts.

Current tutorials are as below:

Windows requirements CUDA, Python, cuDNN, and such : https://youtu.be/DrhUHnYfwC0

How to use SwarmUI : https://youtu.be/HKX8_F1Er_w

How to use FLUX on SwarmUI : https://youtu.be/bupRePUOA18

How to use Kohya GUI for FLUX training : https://youtu.be/nySGu12Y05k

How to use Kohya GUI for FLUX training on Cloud (RunPod and Massed Compute) : https://youtu.be/-uhL2nW7Ddw


we have a leaderboard for video LLMs, and most of the top models are open ones! https://huggingface.co/spaces/opencompass/openvlm_video_leaderboard 👑👏
we are so back 🔥

YaTharThShaRma999 John6666 alielfilali01 louisbrulenaudet KingNish damerajee KennyUTC adorkin AtAndDev


Baichuan-Omni Technical Report (2410.08565)

为什么我会在作者列表上，是huggingface人名匹配的吗？

为什么我会在作者列表上，是huggingface人名匹配的吗？


I have finally completed a working full Azure and Microsoft MS Graph API implementation which can use all the interesting MS AI features in M365 products to manage CRUD patterns for the graph features across products.

This app shows initial implementation of security, authentication, scopes, and access to Outlook, Calendar, Tasks, Onedrive and other apps for CRUD pattern as AI agent service skills to integrate with your AI workflow.

Below are initial screens showing integration:

URL: https://huggingface.co/spaces/awacke1/MSGraphAPI
Discussion: https://huggingface.co/spaces/awacke1/MSGraphAPI/discussions/5

Best of AI on @Azure and @Microsoft on @HuggingFace :
https://huggingface.co/microsoft
https://www.microsoft.com/en-us/research/
---
Aaron


Multimodal Ichigo Llama 3.1 - Real Time Voice AI 🔥

> WhisperSpeech X Llama 3.1 8B
> Trained on 50K hours of speech (7 languages)
> Continually trained on 45hrs 10x A1000s
> MLS -> WhisperVQ tokens -> Llama 3.1
> Instruction tuned on 1.89M samples
> 70% speech, 20% transcription, 10% text
> Apache 2.0 licensed ⚡

Architecture:
> WhisperSpeech/ VQ for Semantic Tokens
> Llama 3.1 8B Instruct for Text backbone
> Early fusion (Chameleon)

I'm super bullish on HomeBrew/ Jan and early fusion, audio and text, multimodal models!

(P.S. Play with the demo on Hugging Face: https://huggingface.co/spaces/jan-hq/Ichigo-llama3.1-s-instruct)

vigos YaTharThShaRma999 chenduo Yadukrishnan edwixx


Just started going through the latest "State of AI Report 2024", and I cannot get over the predictions!

The report predicts major developments in AI over the next 12 months, including a $10B+ investment from a sovereign state into a large US AI lab, triggering national security scrutiny, and a viral app created by someone without coding skills.

It forecasts changes in data collection practices due to frontier labs facing trials, softer-than-expected EU AI Act implementations, and the rise of an open-source alternative to OpenAI GPT-4 outperforming in benchmarks.

NVIDIA’s dominance will remain largely unchallenged, investment in humanoid robots will decline, Apple’s on-device AI research will gain momentum, and a research paper by an AI scientist will be accepted at a major conference.

Lastly, a GenAI-based video game is expected to achieve breakout success.

Yet to go through all 200+ pages... will post summarized thoughts later.


she assert on my device until i give up AHAHEGHFDGHJHASUFSHD


MiniSearch is celebrating its 1st birthday! 🎉

Exactly one year ago, I shared the initial version of this side-project on Hugging Face. Since then, there have been numerous changes under the hood. Nowadays it uses [Web-LLM](https://github.com/mlc-ai/web-llm), [Wllama](https://github.com/ngxson/wllama) and [SearXNG](https://github.com/searxng/searxng). I use it daily as my default search engine and have done my best to make it useful. I hope it's interesting for you too!

HF Space: https://huggingface.co/spaces/Felladrin/MiniSearch
Embeddable URL: https://felladrin-minisearch.hf.space

Locutusque dillfrescott shtefcs John6666 Zmu Nymbo DeathGodlike KatyaKnw


🎓 Introducing Svitppt.com.ua Presentations Dataset - https://huggingface.co/datasets/nyuuzyou/svitppt

Dataset highlights:
- 18,001 presentations from svitppt.com.ua, a platform for storing and viewing presentations for Ukrainian school students
- Primarily in Ukrainian, with some Russian and English content
- Each entry includes: URL, title, download URL, filepath, and extracted text content (where available)
- Contains original PPT/PPTX files in addition to metadata
- Data covers a wide range of educational topics and presentation materials
- Dedicated to the public domain under Creative Commons Zero (CC0) license

The dataset can be used for analyzing educational presentation content in Ukrainian and other languages, text classification tasks, and information retrieval systems. It's particularly valuable for examining trends in educational presentation materials and sharing practices in the Ukrainian-speaking student community. The inclusion of original files allows for in-depth analysis of presentation formats and structures commonly used in Ukrainian educational settings.


The question is on the left and the answer is on the right.


Is Entropix the Chain of Thought Reasoning method behind GPTo1? Using a mixture of entropy, varentropy, and prompt engineering, the Entropix framework can straight up make SmolLLM 330M look like Llama 3.2. Check out this video for a full run down and the description of the video for all of the related resources you need: https://youtu.be/senq4_42tPI


SwiftMistralCoreML
Hi Everyone,

I have created a Swift library to interact with Mistral 7B models in CoreML on macOS.

I hope you find it helpful.

https://github.com/cardona/SwiftMistralCoreML

An open-source Swift library that enables macOS and iOS projects to utilize the Mistral-Interact7B models (INT4 and upcoming FP16) in chat mode. This library includes a complete Swift implementation of the tokenizer and Byte Pair Encoding (BPE) encoder, providing an out-of-the-box solution for integrating advanced language models into your Swift applications.

Features

Full Swift Implementation: Includes tokenizer and BPE encoder written entirely in Swift.
CoreML Integration: Leverages Apple's CoreML framework to run Mistral-Interact7B models efficiently.
Multiple Decoding Strategies: Supports Greedy and Top-K sampling, with plans to add more strategies.
Chat Functionality: Designed to work in chat mode for interactive applications.
FP16 Support (Coming Soon): Future version will support FP16 models for improved performance.


Hello Everyone,
I signed up as Pro and started a ZeroGPU space with a Gradio chatbot project as default.  When building the space, it won't even start the sample Gradio app..   Pretty disappointing when right out of the box, it fails...

Have anyone encountered this yet?
Thanks...

This is the output, odd since it seems to be just a warning.   So why wouldn't it start?

/usr/local/lib/python3.10/site-packages/gradio/components/chatbot.py:228: UserWarning: The 'tuples' format for chatbot messages is deprecated and will be removed in a future version of Gradio. Please set type='messages' instead, which uses openai-style 'role' and 'content' keys.
  warnings.warn(
* Running on local URL:  http://0.0.0.0:7860, with SSR ⚡

To create a public link, set `share=True` in `launch()`.

Stopping Node.js server...


I am looking for an open source realtime TTS voice cloning model. Need Suggestions....!


Last Week in Medical AI: Top Research Papers/Models
🏅 (October 5 - October 12, 2024)

🏅 Medical AI Paper of the Week:
MMedAgent: Learning to Use Medical Tools with Multi-modal Agent

YouTube podcast of weekly papers: https://youtu.be/OD3C5jirszw

Medical LLM & Other Models:
- LLM Framework for Rare Disease Phenotyping
- ONCOPILOT: CT Foundation Model for Tumors
- FMBench: Fairness in Medical MLLMs
- GP-GPT: LLM for Gene-Phenotype Mapping
- MedAdapter: Efficient LLM Medical Adaptation
- RespLLM: Multimodal LLM for Respiratory Health
- MDAgents: LLM Collaboration for Medical Decisions
- MedVisionLlama: LLM Medical Image Segmentation

Frameworks and Methodologies:
- ReXplain: AI-Driven Radiology Video Reports
- BioDiscoveryAgent: AI for Genetic Experiments
- ZODIAC: Multi-Agent Cardiological Diagnostics
- OLAPH: Improving Biomedical LLM Factuality
- OmniGenBench: Benchmarking Genomic Models

Medical LLM Applications:
- MMedAgent: Multimodal Medical Tool Use
- AI for Mental Health Support
- LLMs for Mental Disorders Detection
- PharmacyGPT: AI Pharmacist Framework

Medical LLMs & Benchmarks:
- CliMedBench: Chinese Medical LLM Benchmark
- MedSafetyBench: Evaluating Medical LLM Safety

AI in Healthcare Ethics:
- LLM-based Medical Dialogue Preference Alignment
- Trustworthiness in Medical Imaging Models


YaTharThShaRma999 Clausss takeraparterer Ainonake John6666 Nymbo sawac littlecowmoo nyuuzyou d0rj Azamat1k pyhornet Locutusque AkitoP DiamanteAmarelo matchaaaaa kobkrit ngxson devashishd12 SimonDL den0620


I'm biased but I think HF Posts is the #1 social platform for the AI community! 🤗  That being said, most of us are already on X and now also joining Bluesky.

Looking for us on Bsky? We started a team list here: https://bsky.app/starter-pack/did:plc:yyfrnpcutxghwc6eac4xplwp/3lbem54cnxp26


𝗡𝗲𝘄 𝗹𝗲𝗮𝗱𝗲𝗿𝗯𝗼𝗮𝗿𝗱 𝗿𝗮𝗻𝗸𝘀 𝗟𝗟𝗠𝘀 𝗳𝗼𝗿 𝗟𝗟𝗠-𝗮𝘀-𝗮-𝗷𝘂𝗱𝗴𝗲: 𝗟𝗹𝗮𝗺𝗮-𝟯.𝟭-𝟳𝟬𝗕 𝘁𝗼𝗽𝘀 𝘁𝗵𝗲 𝗿𝗮𝗻𝗸𝗶𝗻𝗴𝘀! 🧑⚖

Evaluating systems is critical during prototyping and in production, and LLM-as-a-judge has become a standard technique to do it.

First, what is "LLM-as-a-judge"?
👉 It's a very useful technique for evaluating LLM outputs. If anything you're evaluating cannot be properly evaluated with deterministic criteria, like the "politeness" of an LLM output, or how faithful it is to an original source, you can use LLM-judge instead : prompt another LLM with "Here's an LLM output, please rate this on criterion {criterion} on a scale of 1 to 5", then parse the number from its output, and voilà, you get your score.

🧐 But who judges the judge?
How can you make sure your LLM-judge is reliable? You can have a specific dataset annotated with scores provided by human judges, and compare how LLM-judge scores correlate with human judge scores.

📊 Before even running that benchmark, to get you started, there's a new option to get you started: a leaderboard that measures how well different model perform as judges!

And the outcome is surprising, models come in quite different orders from what we're used to in general rankings: probably some have much better bias mitigation than others!

Take a deeper look here 👉 https://huggingface.co/blog/arena-atla


Lifehack of the day:
Adding "r.jina.ai/" before any url transforms it in Markdown using Jina AI's Reader! Here with @cyrilzakka's blog post.


First post here goes!

https://huggingface.co/spaces/takarajordan/CineDiffusion

Super excited to announce CineDiffusion🎥, it creates images up to 4.2 Megapixels in Cinematic ultrawide formats like:
- 2.39:1 (Modern Widescreen)
- 2.76:1 (Ultra Panavision 70)
- 3.00:1 (Experimental Ultra-wide)
- 4.00:1 (Polyvision)
- 2.55:1 (CinemaScope)
- 2.20:1 (Todd-AO)

More to come soon!!

Thanks to @John6666 and @Resoldjew for your early support <3

And thanks to the team at ShuttleAI for their brand new Shuttle-3 model, what an amazing job.

https://huggingface.co/shuttleai/shuttle-3-diffusion

John6666 davanstrien victor clem naturelizer Resoldjew


🚨🔥 New Release Alert! 🔥🚨

Introducing the 435M model that outperforms Llama-Guard-3-8B while slashing 75% of the computation cost! 💻💥
👉 Check it out: https://huggingface.co/hbseong/HarmAug-Guard (Yes, INFERENCE CODE INCLUDED! 💡)

More details in our paper: https://arxiv.org/abs/2410.01524 📜

#HarmAug #LLM # Safety #EfficiencyBoost #Research #AI #MachineLearning

davanstrien iojvsuynv John6666 clem brianjking


Watch and learn!

Let's observe Qwen2.5-coder:0.5b on OpenAI HumanEval.

`pip install observers`

And start collecting your data on the Hugging Face Hub.
Dataset: https://huggingface.co/datasets/davidberenstein1957/openai_records
Library: https://github.com/cfahlgren1/observers

davidberenstein1957 davanstrien AtAndDev John6666 BrigitteTousi clem

davidberenstein1957 AtAndDev BrigitteTousi clem fdaudens


I made a beginners guide to Hugging Face Spaces 🤗 I hope it's useful to some of you :)

YouTube video: https://www.youtube.com/watch?v=xqdTFyRdtjQ

Blog: https://www.marqo.ai/blog/how-to-create-a-hugging-face-space

hf-demo victor prithivMLmods SaylorTwift davidberenstein1957 adamdavidson jsulz philosopher-from-god Niansuh ozawamode John6666 DRXD1000 erinys clem alielfilali01 Nymbo fdaudens Goekdeniz-Guelmez ivanfioravanti dvilasuero

philosopher-from-god John6666 clem Goekdeniz-Guelmez dvilasuero


How do I test an LLM for my unique needs?
If you work in finance, law, or medicine, generic benchmarks are not enough.
This blog post uses Argilla, Distilllabel and 🌤Lighteval to generate evaluation dataset and evaluate models.

https://github.com/argilla-io/argilla-cookbook/blob/main/domain-eval/README.md


🤗🔭 Introducing Observers: A Lightweight SDK for AI Observability 🔭🤗

Observers is an open-source Python SDK that provides comprehensive observability for AI applications. Our library makes it easy to:

- Track and record interactions with AI models
- Store observations in multiple backends
- Query and analyse your AI interactions with ease

https://huggingface.co/blog/davidberenstein1957/observers-a-lightweight-sdk-for-ai-observability


Vision-Language Models Can Self-Improve Reasoning via Reflection (2411.00855)

🥳 Thrilled to introduce our recent efforts on bootstrapping VLMs for multi-modal chain-of-thought reasoning !

📕 Title: Vision-Language Models Can Self-Improve Reasoning via Reflection

🔗 Link: https://huggingface.co/papers/2411.00855

😇Takeaways:

- We found that VLMs can self-improve reasoning performance through a reflection mechanism, and importantly, this approach can scale through test-time computing.

- Evaluation on comprehensive and diverse Vision-Language reasoning tasks are included !

Symbol-LLM SaylorTwift cckevinn davanstrien Tanvir1337 ai-everyday


🎓 Introducing Bigslide.ru Presentations Dataset - https://huggingface.co/datasets/nyuuzyou/bigslide

Dataset highlights:
- 50,872 presentations from bigslide.ru, a platform for storing and viewing presentations for school students
- Primarily in Russian, with some English and potentially other languages
- Each entry includes: URL, title, download URL, filepath, and extracted text content (where available)
- Contains original PPT/PPTX files in addition to metadata
- Data covers a wide range of educational topics and presentation materials
- Dedicated to the public domain under Creative Commons Zero (CC0) license

The dataset can be used for analyzing educational presentation content in Russian and other languages, text classification tasks, and information retrieval systems. It's particularly valuable for examining trends in educational presentation materials and sharing practices in the Russian-speaking student community. The inclusion of original files allows for in-depth analysis of presentation formats and structures commonly used in educational settings.


Ladies and Gents, please try my new Assistant, Image Gen - Uncensored Edition, on HuggingChat.

https://hf.co/chat/assistant/66fccce0c0fafc94ab557ef2

This is a multimodal assistant: Qwen 2.5 72B + SOTA diffusion models for image generation. Same architecture as Image Gen+ but with some MAJOR improvements ! These are as follows:

- Switched the LLM to Qwen 2.5 72B, the most powerful model currently available on HuggingChat. This results in higher quality prompts for the txt2img model and much better adherence to the prompt-url format that the upstream provider requires (image gen models are hosted by pollinations as with most other assistants on huggingchat that offer image generation).

- Cleaned up the system prompt including the examples of the prompt-in-url format, and adjusted the logic that determines how many images to generate based on the quality of user prompt... these changes further improve

- Assistant has access to multiple image generation models and will by default choose whatever model is most appropriate for the task. This includes NSFW generations, which it makes using an uncensored SD3 turbo. For other workloads, the Assistant preferentially uses one of the flux variants or any-dark (an artistic SDXL finetune), based on the nature of the task. Available models include turbo, flux, flux-realism, flux-anime, flux-3d, any-dark

- Added verbiage to system prompt which greatly reduces  censorship / refusals by the LLM (the txt2img models are uncensored to start off)

Here are the user-entered prompts used to create the images you see here... feel free to try them yourself!

"Ayatollah Khameini and Kamala Harris having a secret romantic rendezvous. Use flux-realism model"
"A self portrait of your consciousness"
"The chien of andalous, in a psychedelic style"
"Make me 4 paintings in the style of Frida Kahlo that I can sell to tourists in a mexican hippie town"
"Paint me a van gogh and greg rutkowski style scene involving elephants and gerbils"

DeFactOfficial John6666 KingNish thomas-mayne KhangHatto


On the  2nd of October a really cool paper was released called "Were RNNs all we need"   https://arxiv.org/abs/2410.01201

This paper introduces the MinGRU model, a simplified version of the traditional Gated Recurrent Unit (GRU) designed to enhance efficiency by removing hidden state dependencies from its gates. This allows for parallel training, making it significantly faster than conventional GRUs. Additionally, MinGRU eliminates non-linear activations like tanh, streamlining computations.

So I read the paper and I tried training this model  and it seems to be doing quite well , you could check out the pre-trained model on the huggingface spaces

-  https://huggingface.co/spaces/damerajee/mingru-stories


Hello, lovely community! 🌟

https://huggingface.co/spaces/zamal/Molmo-4bit Thrilled to announce that the Molmo 7B 4-bit Space is now live! 🚀 The model size has been reduced by six times with almost no performance loss, and the results will leave you amazed!

It runs on zero GPU, making it incredibly accessible for everyone!

Check it out here and start exploring today!

Happy experimenting! 🎉


Check this out: I trained an AI on huggingface posts! all of these are AI generated:
----------
Hello!

I'm excited to share that my colleague @felipeebert and I have released the largest Spanish LLM benchmark to date.

We've developed the Spanish LLM Evaluation Benchmark (SLAB), a set of benchmarks designed to evaluate the ability of language models to understand, generate and translate in Spanish.

SLAB includes five different benchmarks:
- Sentiment Analysis: evaluate models' ability to detect and describe sentiment in natural language
- Fact Checking: evaluate models' ability to detect and refute factual errors in text
- Question Answering: evaluate models' ability to answer questions in Spanish
- Open-ended Questions: evaluate models' ability to generate coherent responses in Spanish
- Translation: evaluate models' ability to translate in Spanish

SLAB is aligned with the latest Spanish LLM industry developments and includes the most recent models available on the market. We aim to keep our benchmarks up-to-date and relevant to the Spanish language ecosystem.

SLAB is available at: https://huggingface.co/datasets/argilla/SLAB.

If you would like to collaborate on building additional Spanish LLM benchmarks, let's discuss in the comments.

🔗 SLAB Blog Post: https://argilla.com/blog/slab
----------
Hello everyone,

I'm thrilled to announce the release of

https://huggingface.co/01-AI/01AI-GPT-4o -

A new family of models that brings the power of transformer AI to the masses.

This model is designed to be accessible and easy to use, while still offering high-quality results.

Key features:
- Small model size: only 23M parameters
- Supports text generation, image generation, and text-to-image tasks
- Data-efficient training with a lightweight tokenizer
- Optimized for efficient on-device usage
- Uses the powerful transformer architecture to deliver high-quality results

Excited to see what you all think!

https://huggingface.co/01-AI/01AI-GPT-4o

John6666 takeraparterer Clausss xi0v raincandy-u Svngoku


Looking for a simple explanation of Microsoft's release of Differential Transformers, and a nifty Colab Notebook that recreates it all? Then simply check out this YouTube video: https://youtu.be/AsWoaj0zkDo


newsrooms, i see you using deepl or an llm for translation without logging your adjustments. you're wasting gold and you know it's bad!

with this notebook from the argilla team, you can:
- adjust your translations in a neat interface,
- log them to build custom datasets,
- fine-tune your model.

your translation will become better and better, gradually aligning more with your style guide. no more starting from scratch!

Notebook by @sdiazlor: https://colab.research.google.com/drive/1sR1wfOs_pNrdm3Mwjo_qJRG7NnEb9a7W#scrollTo=yNm8N5GoRD2o

#AITranslation #JournalismTech


If someone would like to keep pushing the limits of what's possible on cpu while being efficient/fast, here's my un-trained arco model scaled-up to 770m parameters. Consider it a modern gpt-2-large to experiment with
https://huggingface.co/appvoid/arco-plus


Some exciting news...

We are open-sourcing The Little Book of ML Metrics! 🎉

The book that will be on every data scientist's desk is open source.

What does that mean?

It means hundreds of people can review it, contribute to it, and help us improve it before it's finished!

This also means that everyone will have free access to the digital version!

Meanwhile, the high-quality printed edition will be available for purchase as it has been for a while.

Revenue from printed copies will help us support further development and maintenance of the book. Not to mention that reviewers and contributors will receive revenue sharing through their affiliate links. 🙌

Check out the book repo (make sure to leave a star 🌟):

https://github.com/NannyML/The-Little-Book-of-ML-Metrics


This week in Inference Endpoints - thx @erikkaum for the update!

👀 https://huggingface.co/blog/erikkaum/endpoints-changelog


🎓 Introducing Lusana.ru Presentations Dataset - https://huggingface.co/datasets/nyuuzyou/lusana

Dataset highlights:
- 38,953 presentations from lusana.ru, a platform for storing presentations, reports, templates, and backgrounds
- Primarily in Russian, with some English and potentially other languages
- Each entry includes: ID, title, download URL, uniqueness score, number of slides, views, downloads, file size, file path, and extracted text content (where available)
- Contains original PPT/PPTX files in addition to metadata
- Data covers a wide range of topics and presentation materials
- Licensed under Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0)

The dataset can be used for analyzing presentation content in Russian and other languages, text classification tasks, and information retrieval systems. It's also valuable for examining trends in educational and professional presentation materials and sharing practices in the Russian-speaking community. The inclusion of original files allows for in-depth analysis of presentation formats and structures.


This is not a drill 💥
HuggingChat is now multimodal with https://huggingface.co/meta-llama/Llama-3.2-11B-Vision-Instruct! 🤗
This also comes with multimodal assistants, I have migrated my Marcus Aurelius advice assistant to Llama-Vision and Marcus can see now! 😄

Chat with Marcus: https://hf.co/chat/assistant/65bfed22022ba290531112f8
Start chatting with Llama-Vision 3.2 11B Instruct https://huggingface.co/chat/models/meta-llama/Llama-3.2-11B-Vision-Instruct

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📣 Sentence Transformers v3.2.0 is out, marking the biggest release for inference in 2 years! 2 new backends for embedding models: ONNX (+ optimization & quantization) and OpenVINO, allowing for speedups up to 2x-3x AND Static Embeddings for 500x speedups at 10-20% accuracy cost.

1⃣ ONNX Backend: This backend uses the ONNX Runtime to accelerate model inference on both CPU and GPU, reaching up to 1.4x-3x speedup depending on the precision. We also introduce 2 helper methods for optimizing and quantizing models for (much) faster inference.
2⃣ OpenVINO Backend: This backend uses Intel their OpenVINO instead, outperforming ONNX in some situations on CPU.

Usage is as simple as `SentenceTransformer("all-MiniLM-L6-v2", backend="onnx")`. Does your model not have an ONNX or OpenVINO file yet? No worries - it'll be autoexported for you. Thank me later 😉

🔒 Another major new feature is Static Embeddings: think word embeddings like GLoVe and word2vec, but modernized. Static Embeddings are bags of token embeddings that are summed together to create text embeddings, allowing for lightning-fast embeddings that don't require any neural networks. They're initialized in one of 2 ways:

1⃣ via Model2Vec, a new technique for distilling any Sentence Transformer models into static embeddings. Either via a pre-distilled model with `from_model2vec` or with `from_distillation` where you do the distillation yourself. It'll only take 5 seconds on GPU & 2 minutes on CPU, no dataset needed.
2⃣ Random initialization. This requires finetuning, but finetuning is extremely quick (e.g. I trained with 3 million pairs in 7 minutes). My final model was 6.6% worse than bge-base-en-v1.5, but 500x faster on CPU.

Full release notes: https://github.com/UKPLab/sentence-transformers/releases/tag/v3.2.0
Documentation on Speeding up Inference: https://sbert.net/docs/sentence_transformer/usage/efficiency.html

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The Hugging Face Hub hosts over 1.5M Model, Dataset, and Space repositories. To scale to 10M+, the XetHub team (https://huggingface.co/xet-team) is replacing Git LFS with a new technology that improves storage and transfer capabilities with some future developer experience benefits to boot.

Thanks to @yuchenglow  and @port8080  (for their analysis covering LFS usage from March 2022–Sept 2024), we now have insights into what we’re storing. Check out the Gradio app to explore:
- Storage growth over time
- File types over all repositories
- Some simple optimizations we're investigating

https://huggingface.co/spaces/xet-team/lfs-analysis


NEW - Inference Playground

Maybe like me you have always wanted a super easy way to compare llama3.2-1B vs. llama3.2-3B? or the same model with different temperatures?

Trying and comparing warm Inference API models has never been easier!
Just go to https://hf.co/playground, set your token and you're ready to go.
We'll keep improving, feedback welcome 😊


Aria: An Open Multimodal Native Mixture-of-Experts Model (2410.05993)

Rhymes AI drops Aria: small Multimodal MoE that beats GPT-4o and Gemini-1.5-Flash ⚡

New player entered the game! Rhymes AI has just been announced, and unveiled Aria – a multimodal powerhouse that's punching above its weight.

Key insights:

🧠 Mixture-of-Experts architecture: 25.3B total params, but only 3.9B active.

🌈 Multimodal: text/image/video → text.

📚 Novel training approach: “multimodal-native” where multimodal training starts directly during pre-training, not just tacked on later

📏 Long 64K token context window

🔓 Apache 2.0 license, with weights, code, and demos all open

⚡ On the benchmark side, Aria leaves some big names in the dust.

- It beats Pixtral 12B or Llama-3.2-12B on several vision benchmarks like MMMU or MathVista.
- It even overcomes the much bigger GPT-4o on long video tasks and even outshines Gemini 1.5 Flash when it comes to parsing lengthy documents.

But Rhymes AI isn't just showing off benchmarks. They've already got Aria powering a real-world augmented search app called “Beago”. It’s handling even recent events with great accuracy!

And they partnered with AMD to make it much faster than competitors like Perplexity or Gemini search.

Read their paper for Aria 👉 https://huggingface.co/papers/2410.05993

Try BeaGo 🐶 👉 https://rhymes.ai/blog-details/introducing-beago-your-smarter-faster-ai-search

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NEW: Open Source Text/ Image to video model is out - MIT licensed - Rivals Gen-3, Pika & Kling  🔥

> Pyramid Flow: Training-efficient Autoregressive Video Generation method
> Utilizes Flow Matching
> Trains on open-source datasets
> Generates high-quality 10-second videos
> Video resolution: 768p
> Frame rate: 24 FPS
> Supports image-to-video generation

> Model checkpoints available on the hub 🤗: https://huggingface.co/rain1011/pyramid-flow-sd3

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🎓 Introducing Doc4web.ru Documents Dataset - https://huggingface.co/datasets/nyuuzyou/doc4web

Dataset highlights:
- 223,739 documents from doc4web.ru, a document hosting platform for students and teachers
- Primarily in Russian, with some English and potentially other languages
- Each entry includes: URL, title, download link, file path, and content (where available)
- Contains original document files in addition to metadata
- Data reflects a wide range of educational topics and materials
- Licensed under Creative Commons Zero (CC0) for unrestricted use

The dataset can be used for analyzing educational content in Russian, text classification tasks, and information retrieval systems. It's also valuable for examining trends in educational materials and document sharing practices in the Russian-speaking academic community. The inclusion of original files allows for in-depth analysis of various document formats and structures.

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This is how AI can be useful in journalism: Just tested DataTalk - a tool that lets you dig through campaign finance data with just your words.

It's transforming complex FEC filings and OpenSecrets datasets into actionable insights for journalists.

Key features for newsrooms:
- Natural language queries on FEC data
- Rapid insights on donors, spending, special interests
- SQL access for deep dives

Tested it out:
- Retrieved how much Harris and Trump raised
- Found top donors instantly (#1 is Timothy Mellon—have you heard about him?)
- Uncovered big self-funders like David Trone ($62M)

Pros:
- Saves hours of data wrangling
- Surfaces story leads quickly
- Transparent AI retrieving steps makes this tool auditable

Awesome work by Stanford University Open Virtual Assistant Lab, Big Local News, and Columbia University - Graduate School of Journalism. Expert-guided.

Remember: Always verify. Use for leads, not final copy. But this is gold for finding new leads.

How might this change campaign finance reporting? What other datasets need this treatment?

Try it out: https://www.datatalk.genie.stanford.edu/

#AIJournalism #campaignfinance #datajournalism #election2024

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Microsoft released a method that allows you to vectorize word vectors themselves! It is called VPTQ. You can check out their full paper including the method and all of the math for the algorithm, or you can watch this video where I did all of that for you, then reconstructed their entire method within Python!

https://youtu.be/YwlKzV1y62s


AI Agents LlamaIndex in 40 minutes

The video covers code and workflow explanations for:

- Function Calling
- Function Calling Agents + Agent Runner
- Agentic RAG
- REAcT Agent: Build your own Search Assistant Agent

Watch: https://youtu.be/bHn4dLJYIqE


Why nobdoy is talking about the new training corpus released by MBZUAI today.

TxT360 is +15 Trillion tokens corpus outperforming FineWeb on several metrics. Ablation studies were done up to 1T tokens.

Read blog here : https://huggingface.co/spaces/LLM360/TxT360
Dataset : https://huggingface.co/datasets/LLM360/TxT360


Large Language Models in Targeted Sentiment Analysis (2404.12342)

📢 Two weeks ago I got a chance to share the most recent reasoning 🧠 capabilities of Large Language models in Sentiment Analysis NLPSummit-2024.

For those who missed and still wish to find out the advances of GenAI in that field, the recording is now available:
https://www.youtube.com/watch?v=qawLJsRHzB4

You will be aware of:
☑ how well LLMs reasoning can be used for reasoning in sentiment analysis as in Zero-shot-Learning,
☑ how to improve reasoning by applying and leaving step-by-step chains (Chain-of-Thought)
☑ how to prepare the most advanced model in sentiment analysis using Chain-of-Thought.

Links:
📜 Paper: https://huggingface.co/papers/2404.12342
⭐ Code: https://github.com/nicolay-r/Reasoning-for-Sentiment-Analysis-Framework


On-device AI framework ecosystem is blooming these days:

1. llama.cpp - All things Whisper, LLMs & VLMs - run across Metal, CUDA and other backends (AMD/ NPU etc)
https://github.com/ggerganov/llama.cpp

2. MLC - Deploy LLMs across platforms especially WebGPU (fastest WebGPU LLM implementation out there)
https://github.com/mlc-ai/web-llm

3. MLX - Arguably the fastest general purpose framework (Mac only) - Supports all major Image Generation (Flux, SDXL, etc), Transcription (Whisper), LLMs
https://github.com/ml-explore/mlx-examples

4. Candle - Cross-platform general purpose framework written in Rust - wide coverage across model categories
https://github.com/huggingface/candle

Honorable mentions:

1. Transformers.js - Javascript (WebGPU) implementation built on top of ONNXruntimeweb
https://github.com/xenova/transformers.js

2. Mistral rs - Rust implementation for LLMs & VLMs, built on top of Candle
https://github.com/EricLBuehler/mistral.rs

3. Ratchet - Cross platform, rust based WebGPU framework built for battle-tested deployments
https://github.com/huggingface/ratchet

4. Zml - Cross platform, Zig based ML framework
https://github.com/zml/zml

Looking forward to how the ecosystem would look 1 year from now - Quite bullish on the top 4 atm - but open source ecosystem changes quite a bit! 🤗

Also, which frameworks did I miss?

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NSFW Erotic Novel AI Generation
-NSFW Text (Data) Generator for Detecting 'NSFW' Text: Multilingual Experience

The multilingual NSFW text (data) auto-generator is a tool designed to automatically generate and analyze adult content in various languages. This service uses AI-based text generation to produce various types of NSFW content, which can then be used as training data to build effective filtering models. It supports multiple languages, including English, and allows users to input the desired language through the system prompt in the on-screen options to generate content in the specified language. Users can create datasets from the generated data, train machine learning models, and improve the accuracy of text analysis systems. Furthermore, content generation can be customized according to user specifications, allowing for the creation of tailored data. This maximizes the performance of NSFW text detection models.

Web:  https://fantaxy-erotica.hf.space
API:  https://replicate.com/aitechtree/nsfw-novel-generation

Usage Warnings and Notices: This tool is intended for research and development purposes only, and the generated NSFW content must adhere to appropriate legal and ethical guidelines. Proper monitoring is required to prevent the misuse of inappropriate content, and legal responsibility lies with the user. Users must comply with local laws and regulations when using the data, and the service provider is not liable for any issues arising from the misuse of the data.

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hi everyone, i was just wondering how much autotrain will cost depending on which model i pick and what gpu i run it on? the documentation is pretty vague so any help would be great! thanks.


Neural Network Chaos Monkey: Randomly shuts off parts of the neural network during training. The Chaos Monkey is super present at Epoch 1, is gone by the final Epoch. My hypothesis was that this would either increase the robustness of the model, or it would make the outputs totally worse. You can 100% reproduce my results, chaos wins again.

https://youtu.be/bWA9unotJ7k


Building a Ranking System to Enhance Prompt Results: The New PageRank for RAG/LLM

Read full article at https://mltblog.com/4gT62y9

In this document, you will learn how to build a system that decides, among dozens of candidate paragraphs selected from the corpus to answer a prompt, which ones to show in the results, and in what order. The goal is to maximize relevancy while not overwhelming the user with a long, cluttered answer. Think of it as the new PageRank for RAG/LLM, although the algorithm is radically different, and much simpler. The approach is generic and works for all RAG/LLM systems whether based on neural networks or not. It is implemented in xLLM.

The article includes Python code (with links to GitHub) and case study featuring the anonymized augmented corpus of a fortune 100 company, as well as future LLM developments (auto-indexing and LLM for glossary generation).


Addition is All You Need for Energy-efficient Language Models (2410.00907)

💥 𝐋-𝐌𝐮𝐥: 𝐀𝐝𝐝𝐢𝐭𝐢𝐨𝐧-𝐎𝐧𝐥𝐲 𝐌𝐮𝐥𝐭𝐢𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐜𝐚𝐧 𝐬𝐥𝐚𝐬𝐡 𝐜𝐨𝐦𝐩𝐮𝐭𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐜𝐨𝐬𝐭𝐬 𝐛𝐲 𝟖𝟎%!

Microsoft researchers dropped a groundbreaking technique that could slash the energy use in transformer computations : their novel "linear-complexity multiplication" (L-Mul) algorithm approximates floating-point multiplication using energy-efficient integer addition instead of costly multiplications.

💡 Quick reminder on how floats are coded on 8 bits (FP8):
In the e4m3 FP8 standard, you encode a number as:
Sign (1 bit) | Exponent (4 bits) | Mantissa (3 bits)
Example: 0 (positive) | 1000 (8) | 101 (1/2 + 1/8 = 0.625)
Calculation: you add one to the mantissa, and multiply it by 2 power (the exponent - a bias term which is 7 for e4m3):

➡ You get (1 + 0.625) × 2^(8-7) = 3.25

Now back to the paper. 𝗞𝗲𝘆 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀:

⚡ Multiplication is extremely energy-intensive compared to addition. For 32-bit operations, multiplication (3.7 pJ) uses 37x more energy than addition (0.1 pJ)!

🧮 Traditional floating-point multiplication go like (noting xm the mantissa and xe the exponent): Mul(x,y) = (1 + xm) · 2^xe · (1 + ym) · 2^ye = (1 + xm + ym + xm · ym) · 2^(xe+ye)

💡 L-Mul cleverly approximates this as: L-Mul(x,y) = (1 + xm + ym + 2^-l(m)) · 2^(xe+ye), eliminating the costly xm · ym term

🔧 l(m) term is adaptively set based on mantissa size for optimal accuracy

📊 Benchmarks on the Llama-3.1-8B-Instruct model show L-Mul preserves precision across various NLP tasks, with performance nearly identical to full BFloat16 precision

💬 Authors claim: "We can achieve the same model inference performance while reducing the energy cost of attention computations by 80%."

This breakthrough is still theoretical and would need implementation on dedicated hardware to confirm real-world gains, but it’s a really exciting path for more sustainable AI! 🌱

Read the paper here 👉 https://huggingface.co/papers/2410.00907

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Lightweight implementation of newly introduced “Differential Transformer”:
Proposes differential attention mechanism which computes attention scores as a difference between two separate softmax attention maps thereby reducing noise in attention blocks. [[[Differential nanoGPT]]] :)

Code: https://github.com/Jaykef/ai-algorithms/blob/main/DIFF_Transformer.ipynb
YT Video: https://youtu.be/9V4mJA5y7dg


The Nobel Prize background for Hopfield and Hinton's work on neural networks is pure gold. It's a masterclass in explaining AI basics.

Key takeaways from the conclusion:
- ML applications are expanding rapidly. We're still figuring out which will stick.
- Ethical discussions are crucial as the tech develops.
- Physics 🤝 AI: A two-way street of innovation.

Some mind-blowing AI applications in physics:
- Discovering the Higgs particle
- Cleaning up gravitational wave data
- Hunting exoplanets
- Predicting molecular structures
- Designing better solar cells

We're just scratching the surface. The interplay between AI and physics is reshaping both fields.

Bonus: The illustrations accompanying the background document are really neat. (Credit: Johan Jarnestad/The Royal Swedish Academy of Sciences)

#AI #MachineLearning #Physics #Ethics #Innovation

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Meta AI vision has been cooking @facebook
They shipped multiple models and demos for their papers at @ECCV🤗

Here's a compilation of my top picks:
- Sapiens is family of foundation models for human-centric depth estimation, segmentation and more, all models have open weights and demos 👏

All models have their demos and even torchscript checkpoints!
A collection of models and demos: https://huggingface.co/collections/facebook/sapiens-66d22047daa6402d565cb2fc
- VFusion3D is state-of-the-art consistent 3D generation model from images

Model: https://huggingface.co/facebook/vfusion3d
Demo: https://huggingface.co/spaces/facebook/VFusion3D

- CoTracker is the state-of-the-art point (pixel) tracking model

Demo: https://huggingface.co/spaces/facebook/cotracker
Model: https://huggingface.co/facebook/cotracker

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THANK YOU for bringing Mag Mell to 10,000 downloads across its quantizations!! I'm over the moon with how well it's done, and with everyone's kind feedback.

I'm in a team now! Allura are a group of alumni from various reaches of the LLM roleplay scene.
https://huggingface.co/allura-org

Our first model is an OLMoE roleplay tune called MoE Girl:
https://huggingface.co/allura-org/MoE-Girl-1BA-7BT

I'd like to make more adventuring and longform models in my current style with them, so keep an eye out for that.

Also Mag Mell R2 soon maybe idk


Open-source AI creates healthy competition in a field where natural tendencies lead to extreme concentration of power. Imagine a world where only one or two companies could build software. This is the biggest risk and ethical challenge of them all IMO. Let's fight this!

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Huge news for Kohya GUI - Now you can fully Fine Tune / DreamBooth FLUX Dev with as low as 6 GB GPUs without any quality loss compared to 48 GB GPUs - Moreover, Fine Tuning yields better results than any LoRA training could

Config Files
I published all configs here : https://www.patreon.com/posts/112099700

Tutorials
Fine tuning tutorial in production

Windows FLUX LoRA training (fine tuning is same just config changes) : https://youtu.be/nySGu12Y05k

Cloud FLUX LoRA training (RunPod and Massed Compute ultra cheap) : https://youtu.be/-uhL2nW7Ddw

LoRA Extraction
The checkpoint sizes are 23.8 GB but you can extract LoRA with almost no loss quality - I made a research and public article / guide for this as well

LoRA extraction guide from Fine Tuned checkpoint is here : https://www.patreon.com/posts/112335162

Info
This is just mind blowing. The recent improvements Kohya made for block swapping is just amazing.

Speeds are also amazing that you can see in image 2 - of course those values are based on my researched config and tested on RTX A6000 - same speed as almost RTX 3090

Also all trainings experiments are made at 1024x1024px. If you use lower resolution it will be lesser VRAM + faster speed

The VRAM usages would change according to your own configuration - likely speed as well

Moreover, Fine Tuning / DreamBooth yields better results than any LoRA could

Installers
1-Kohya GUI accurate branch and Windows Torch 2.5 Installers and test prompts shared here : https://www.patreon.com/posts/110879657

The link of Kohya GUI with accurate branch : https://github.com/bmaltais/kohya_ss/tree/sd3-flux.1

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Long Range Arena: A Benchmark for Efficient Transformers (2011.04006)

🔗 Evaluating Long Context #1: Long Range Arena (LRA)

Accurately evaluating how well language models handle long contexts is crucial, but it's also quite challenging to do well. In this series of posts, we're going to examine the various benchmarks that were proposed to assess long context understanding, starting with Long Range Arens (LRA)

Introduced in 2020, Long Range Arens (LRA) is one of the earliest benchmarks designed to tackle the challenge of long context evaluation.

📌 Key Features of LRA

1⃣ Diverse Tasks: The LRA benchmark consists of a suite of tasks designed to evaluate model performance on long sequences ranging from 1,000 to 16,000 tokens. These tasks encompass different data types and modalities: Text, Natural and Synthetic Images, and Mathematical Expressions.

2⃣ Synthetic and Real-world Tasks: LRA is comprised of both synthetic probing tasks and real-world tasks.

3⃣ Open-Source and Extensible:  Implemented in Python using Jax and Flax, the LRA benchmark code is publicly available, making it easy to extend.

📌 Tasks

1⃣ Long ListOps

2⃣ Byte-level Text Classification and Document Retrieval

3⃣ Image Classification

4⃣ Pathfinder and Pathfinder-X (Long-range spatial dependency)

👨💻 Long Range Arena (LRA) Github Repository: https://github.com/google-research/long-range-arena

📄 Long Range Arena (LRA) paper: https://huggingface.co/papers/2011.04006


Thursday 10 October 17:00 CEST, I will show a good way to get started with a text classification project on the Hugging Face Hub with Argilla and Setfit.

Signup here: https://lu.ma/31mecp34


🌐 Subdomain Dataset Update: September 2024 Data Now Available

I have updated the https://huggingface.co/datasets/nyuuzyou/subdomains dataset with fresh data for September 2024. This addition further expands this largest collection of subdomain statistics currently available, providing researchers and analysts with even more valuable insights into web infrastructure and domain patterns.

Latest Update Highlights:
- New File: subdomains_2024_09.csv
- Unique Subdomains: 19,191,867
- Total Occurrences: 170,792,927


Don't you think we should add a tag "Evaluation" for datasets that are meant to be benchmarks and not for training ?

At least, when someone is collecting a group of datasets from an organization or let's say the whole hub can filter based on that tag and avoid somehow contaminating their "training" data.

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🚀 Finishing up the prototype of my weekend project called ChessPT 🚀

- The game state is now being rendered. This simplifies coming up with own new moves
- The model space https://huggingface.co/spaces/philipp-zettl/ChessPT was updated to provide an interactive mode.
- The space is currently running v0.4 of https://huggingface.co/philipp-zettl/chessPT
- New updates will come this week.
- Training runs will be logged under https://wandb.ai/philipp-zettl/chessPT/

**Note**: The model is still not performing on a level that I want it to. It predicts too frequently invalid moves (according to the game state). In addition to that the post-processing step is a little faulty, so it might be possible that you end up in a state where the model didn't provide a next move.


When huggingface patches this, I'm going to be really sad, but in the meantime, here you go:

When AutoTrain creates a new space to train your model, it does so via the huggingface API. If you modify the code so that it includes a premade README.md file, you can add these two lines:

```md
---
app_port: 8080 # or any integer besides 7860 that's greater than 2 ** 10
startup_duration_timeout: 350m
---
```

This will tell huggingface to listen for the iframe on your port, instead of the one autotrain is actually hosting on, and because startup time isn't charged, you get the product for free. (you can take this even further by switching compute type to A100 or something)

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Seems that someone is spoofing your servers by replicating profiles on his and possibly gleaning peoples logins and passwords..

https://huggingface.co/spaces/Lubub-Cruzeiro

I was there to use https://huggingface.co/spaces/Lubub-Cruzeiro/train-dreambooth-lora-sdxl

But it said I didn't have login access.. Then I noticed it wasn't the hugginface.co server domain..


📢 This year I made decent amout of experiments on LLM reasoning capabilities in author opinion extraction.
However, they did not go further with:
↗ annoation of other sources of opinion causes: entities, out-of-context object (None).
📏 evaluation of factual statements that support the extracted sentiment.

To address these limitations, so far we launch 🚀 RuOpinionNE-2024 competition on the Codalab platform:
📊 https://codalab.lisn.upsaclay.fr/competitions/20244

 The competition is aimed at extraction of opinion tuples (see attached images) from texts written in Russian.
It proceeds the past RuSentNE-2023 codalab competition findings:
🔎 Past year competition: https://www.dialog-21.ru/media/5896/golubevaplusetal118.pdf
🔎 LLM reasoning 🧠: https://arxiv.org/abs/2404.12342

For those who interested to adopt Generative AI, the complete information about competition is below:
📊 RuOpinionNE-2024: https://codalab.lisn.upsaclay.fr/competitions/20244
🗒 Task description: https://codalab.lisn.upsaclay.fr/competitions/20244#learn_the_details-overview
🔔 To follow updates: https://t.me/RuOpinionNE2024
⏰ Stages Deadlines (might be extended)
📦 Submission details (bottom of the competition page)

🙋 For questions you can contact @nicolay-r: https://nicolay-r.github.io/
🧪  Most recent findings on LLM application: https://github.com/nicolay-r/RuSentNE-LLM-Benchmark


🙋🏻♂hey there folks ,

did you know that  https://huggingface.co/lmms-lab released a new version of 🌋🌋Llava on thursday ? Now it has 🎥video understanding !
check it out 👇🏻

collection : https://huggingface.co/collections/lmms-lab/llava-video-661e86f5e8dabc3ff793c944
demo : https://huggingface.co/spaces/Tonic/Llava-Video


Hello guys,

I am trying to use the space "The Stringer" by Ilaria RVS.

When I all used my credits, the say to sign up on hugging face. So i'v signed and paid the pro version.

But I stay enable to generate some new voices...

What I did wrong ??


pretty much all of the values in the llama training post are placeholders, so if you dont get a desireable result tweak and tweak and tweak. it took months to get smallama to do anything


What are we thinking about MovieGen from Meta? Are the researchers on Hugging Face to be able to ask them questions?

The paper is here: https://ai.meta.com/static-resource/movie-gen-research-paper


📢 The fast application of named entity recognition (NER) model towards vast amout of texts usually serves two major pitfalls:
🔴 Limitation of the input window size
🔴 Drastically slows down the downstream pipeline of the whole application

⭐ https://github.com/nicolay-r/bulk-ner

To address these problems, bulk-ner represent a no-string framework with the handy wrapping over any dynamically linked NER-ml model by providing:
☑ Native long-input contexts handling.
☑ Native support of batching (assuming that ML-model engine has the related support too)

To quick start, sharing the wrapper over DeepPavlov NER models.
With the application of such models you can play and bulk your data here:
📙 https://colab.research.google.com/github/nicolay-r/ner-service/blob/main/NER_annotation_service.ipynb
(You have to have your data in CSV / JSONL format)

Lastly, it is powered by AREkit pipelines, and therefore could be a part of the relation extraction and complex information retrieval systems:
💻 https://github.com/nicolay-r/AREkit
📄 https://openreview.net/forum?id=nRybAsJMUt


Very few people realize that most of the successful AI startups got successful because they were focused on open science and open-source for at least their first few years. To name but a few, OpenAI (GPT, GPT2 was open-source), Runway & Stability (stable diffusion), Cohere, Mistral and of course Hugging Face!

The reasons are not just altruistic, it's also because sharing your science and your models pushes you to build AI faster (which is key in a fast-moving domain like AI), attracts the best scientists & engineers and generates much more visibility, usage and community contributions than if you were 100% closed-source. The same applies to big tech companies as we're seeing with Meta and Google!

More startups and companies should release research & open-source AI, it's not just good for the world but also increases their probability of success!


Version 0.2a of ChessPT is currently training.

I decided to wait with the actual v1.0 until I have a better understanding where I want to go and successfully trained the first fine tune.

I'm playing around with a loss that is highly influenced by the idea of reinforcement.

Basically I'm punishing the model for generating invalid PGN strings.
The current approach sets on simplicity

```
-2: wrong characters in output
-1: invalid PGN string, but valid charset
0: valid PGN string, incl. valid moves
```

GPT-4o helped me with the implementation. I'm expecting some errors in the implementation.

The training should finish in somewhat 14h, I will upload the new weights then.
But I still need to run extensive tests on this loss before I can happily call it v0.2 ✌

BTW, I'm also building a space for the model which will be published tonight after adding descriptions and a nice interface. ♟

https://huggingface.co/philipp-zettl/chessPT
https://huggingface.co/spaces/philipp-zettl/ChessPT


New hobby: creating AI research paper arts lol, using pymupdf to extract text and add background then animate with runway:) code coming soon…

FelipeMahlow AIAJAY John6666 todeschini dinesh-001 Jour tezuesh nslaughter AtAndDev IzzyR kaanhho jairo PriyankSisodia


still sending all your info to a black-box api when using an llm? you know it's bad. try this instead: you can run dozens of models right in your browser from Google, Microsoft, Mistral, Meta, Qwen, and Smollm. private and secure. you'll thank me later.

https://huggingface.co/spaces/cfahlgren1/webllm-playground


🙋🏻♂ Hey there folks ,

🦎Salamandra release by @mvillegas and team
@BSC_CNS https://huggingface.co/BSC-LT is absolutely impressive so far !

perhaps the largest single training dataset of high quality text to date of 7.8 trillion tokens in 35 European languages and code.

the best part : the data was correctly licenced so it's actually future-proof!

the completions model is really creative and instruct fine tuned version is very good also.

now you can use such models for multi-lingual enterprise applications with further finetunes , long response generation, structured outputs (coding) also works.

check out 👇🏻
the collection : https://huggingface.co/collections/BSC-LT/salamandra-66fc171485944df79469043a
the repo : https://github.com/langtech-bsc/salamandra
7B-Instruct demo : https://huggingface.co/spaces/Tonic/Salamandra-7B

John6666 monsoon-nlp malteos D1rtyB1rd jayebaku LuisVasquezBSC ibaucells


📜 𝐎𝐥𝐝-𝐬𝐜𝐡𝐨𝐨𝐥 𝐑𝐍𝐍𝐬 𝐜𝐚𝐧 𝐚𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐫𝐢𝐯𝐚𝐥 𝐟𝐚𝐧𝐜𝐲 𝐭𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐞𝐫𝐬!

Researchers from Mila and Borealis AI just have shown that simplified versions of good old Recurrent Neural Networks (RNNs) can match the performance of today's transformers.

They took a fresh look at LSTMs (from 1997!) and GRUs (from 2014). They stripped these models down to their bare essentials, creating "minLSTM" and "minGRU". The key changes:
❶ Removed dependencies on previous hidden states in the gates
❷ Dropped the tanh that had been added to restrict output range in order to avoid vanishing gradients
❸ Ensured outputs are time-independent in scale (not sure I understood that well either, don't worry)

⚡ As a result, you can use a “parallel scan” algorithm to train these new, minimal RNNs, in parallel, taking 88% more memory but also making them 200x faster than their traditional counterparts for long sequences

🔥 The results are mind-blowing! Performance-wise, they go toe-to-toe with Transformers or Mamba.

And for Language Modeling, they need 2.5x fewer training steps than Transformers to reach the same performance! 🚀

🤔 Why does this matter?

By showing there are simpler models with similar performance to transformers, this challenges the narrative that we need advanced architectures for better performance!

💬 François Chollet wrote in a tweet about this paper:

“The fact that there are many recent architectures coming from different directions that roughly match Transformers is proof that architectures aren't fundamentally important in the curve-fitting paradigm (aka deep learning)”

“Curve-fitting is about embedding a dataset on a curve. The critical factor is the dataset, not the specific hard-coded bells and whistles that constrain the curve's shape.”

It’s the Bitter lesson by Rich Sutton striking again: don’t need fancy thinking architectures, just scale up your model and data!

Read the paper 👉 https://huggingface.co/papers/2410.01201


🚀 New Model Release: zamal/Molmo-7B-GPTQ-4bit 🚀

Hello lovely community,

https://huggingface.co/zamal/Molmo-7B-GPTQ-4bit model is now available for all! This model has been highly quantized, reducing its size by almost six times. It now occupies significantly less space and vRAM, making it perfect for deployment on resource-constrained devices without compromising performance.

Now we get:
Efficient Performance: Maintains high accuracy while being highly quantized.
Reduced Size: The model size is reduced by nearly six times, optimizing storage and memory usage.
Versatile Application: Ideal for integrating a powerful visual language model into various projects particularly multi rag chains.
Check it out!


Hyperdimensional Computing + Neural Network, tell your friends. To my knowledge, this is a completely novel implementation of HDC+Neural Networks. It would be a direct competitor to Transformers. It is off the charts more computationally efficient than Transformers could ever hope to be (which is why I tested it in the first place). It is far more similar to biological processes. My testing so far shows that it works surprisingly well. One surprise so far from my testing, adding an Attention Mechanism to the model does nothing at all. Weirdest thing. Like 1% performance increase. I guess Attention Is Not All You Need?

I made a Github repository for my Hyperdimensional Computing Neural Network: https://github.com/RichardAragon/HyperDimensionalComputingNeuralNetwork

I made a YouTube video showcasing the model and some of my experiments with it: https://youtu.be/Eg51o519zVM


NEW releases for today:

- We've brought our new Esper 2 model to Llama 3.2! The DevOps-first Esper finetunes use our newest open source datasets. Get the new Esper: https://huggingface.co/ValiantLabs/Llama3.2-3B-Esper2
- Some new merged models, combining Shining Valiant 2 with the other Build Tools:
  - https://huggingface.co/sequelbox/Llama3.1-8B-PlumCode
  - https://huggingface.co/sequelbox/Llama3.1-8B-PlumChat
  - https://huggingface.co/sequelbox/Llama3.1-8B-PlumMath

more to come soon :)


My biggest release of the year: a series of 7 specialized embedding models for information retrieval within tax documents, is now available for free on Hugging Face 🤗

These new models aim to offer an open source alternative for in-domain semantic search from large text corpora and will improve RAG systems and context addition for large language models.

Trained on more than 43 million tax tokens derived from semi-synthetic and raw-synthetic data, enriched by various methods (in particular MSFT's evol-instruct by @intfloat), and corrected by humans, this project is the fruit of hundreds of hours of work and is the culmination of a global effort to open up legal technologies that has only just begun.

A big thank you to Microsoft for Startups for giving me access to state-of-the-art infrastructure to train these models, and to @julien-c, @clem  🤗, @thomwolf and the whole HF team for the inference endpoint API and the generous provision of Meta LLama-3.1-70B. Special thanks also to @tomaarsen for his invaluable advice on training embedding models and Loss functions ❤

Models are available on my personal HF page, into the Lemone-embed collection: https://huggingface.co/collections/louisbrulenaudet/lemone-embed-66fdc24000df732b395df29b

tomaarsen clem yashisfine sensei-ml John6666 a9i Tonic julien-c


🇨🇳⛵ 出海: Chinese AI is expanding globally

Fact: Chinese LLMs are heavily underrated, for instance recently the excellent Deepseek-v2.5 or Qwen models.

Luckily for us, @AdinaY just wrote an excellent blog post explaining the Chinese AI ecosystem!

My key takeaways:

Since Google, OpenAI and Anthropic models are not available in China, local companies are fighting for the market. A really good market - AI has much higher penetration there than in the rest of the world, both with companies and individual users!

💰 But since Deepseek heavily cut prices in May 24, this spiraled into a price war that created a cut-throat environment with unsustainably low prices.

📋 On top of this, the local regulation is stringent: models must undergo licensing from a local censor (the Cyberspace Administration of China), that for instance requires models to refuse answering certain questions on the CCP. Although this is certainly simpler to implement than certain condition of the European AI Act.

💸 If this wasn't enough, VC investment in AI is drying out: By mid-2024, Chinese AI startups raised approximately $4.4 billion, vs $55B for US startups just in Q2 24.

📱 To get profitability companies have shifted from foundational models to model + application, for instance PopAI from [01.AI](http://01.ai/) with millions of users and high profitability.

⛏ They also try to drill down specific industries: but these niches are also getting crowded.

➡ Since their home market is becoming both too crowded and unhospitable, Chinese companies are now going for international market, "Sailing abroad" following the expression consacred for Zheng He's legendary journey in 1500.

There, they'll have to adapt to different infrastructures and regulations, but they have bright prospects for growth!

Read her post 👉 https://huggingface.co/blog/AdinaY/chinese-ai-global-expansion


ColPali is an exciting new approach to multimodal document retrieval, but some doubt its practical use with existing vector DBs.

It turns out it's super easy to use Qdrant to index and search ColPali embeddings efficiently.

Blog post here: https://danielvanstrien.xyz/posts/post-with-code/colpali-qdrant/2024-10-02_using_colpali_with_qdrant.html

Very silly demo: https://huggingface.co/spaces/davanstrien/ufo-ColPali-Search


Updated my 📺RTV🖼 - Real Time Video AI app this morning.
URL: https://huggingface.co/spaces/awacke1/stable-video-diffusion

It uses Stable Diffusion to dynamically create videos from images in input directory or uploaded using A10 GPU on Huggingface.

Samples below.

I may transition this to Zero GPU if I can.  During Christmas when I revised this I had my highest billing from HF yet due to GPU usage.  It is still the best turn key GPU out and Image2Video is a killer app.  Thanks HF for the possibilities!


Triton-accelerated nanoGPT🤕
The WHY behind this ordeal - After practicing triton for about 2 weeks now, I challenged myself into implementing custom triton kernels for Karpathy's nanoGPT and quite an ordeal it was but somehow got something working, not perfect but getting there:), contributions are welcomed.

Code: https://github.com/Jaykef/Triton-nanoGPT