Introducing Lemone-router, a series of classification models designed to produce an optimal multi-agent system for different branches of tax law.

Trained on a base of 49k lines comprising a set of synthetic questions generated by GPT-4 Turbo and Llama 3.1 70B, which have been further refined through evol-instruction tuning and manual curation and authority documents, these models are based on an 8-category decomposition of the classification scheme derived from the Bulletin officiel des finances publiques - impôts :

```python
label2id = {
    "Bénéfices professionnels": 0,
    "Contrôle et contentieux": 1,
    "Dispositifs transversaux": 2,
    "Fiscalité des entreprises": 3,
    "Patrimoine et enregistrement": 4,
    "Revenus particuliers": 5,
    "Revenus patrimoniaux": 6,
    "Taxes sur la consommation": 7
}

id2label = {
    0: "Bénéfices professionnels",
    1: "Contrôle et contentieux",
    2: "Dispositifs transversaux",
    3: "Fiscalité des entreprises",
    4: "Patrimoine et enregistrement",
    5: "Revenus particuliers",
    6: "Revenus patrimoniaux",
    7: "Taxes sur la consommation"
}
```
It achieves the following results on the evaluation set:
- Loss: 0.4734
- Accuracy: 0.9191

Link to the collection: https://huggingface.co/collections/louisbrulenaudet/lemone-router-671cce21d6410f3570514762


Is Hallucination Always Harmful? Unlike traditional approaches that view hallucinations as detrimental, our work in NeurIPS'24 proposes a novel perspective: hallucinations as intrinsic prior knowledge. Derived from the commonsense knowledge acquired during pre-training, these hallucinations are not merely noise but a source of task-relevant information. By leveraging hallucinations as a form of prior knowledge, we can effectively mine difficult samples without the need for customized prompts, streamlining tasks like camouflage sample detection and medical image segmentation.

Check out our paper for more insights and detailed methodologies:https://huggingface.co/papers/2408.15205


Last Week in Medical AI: Top Research Papers/Models 🔥
🏅 (October 19-26, 2024)

🏅 Medical AI Paper of the Week:
Safety principles for medical summarization using generative AI by Google

Medical LLM & Other Models:
- BioMistral-NLU: Medical Vocab Understanding
- Bilingual Multimodal LLM for Biomedical Tasks
- Metabolic-Enhanced LLMs for Clinical Analysis
- Dermatology Foundation Model

Frameworks and Methodologies:
- Back-in-Time: Medical Deepfake Detection
- Hybrid GenAI for Crystal Design
- VISAGE: Video Synthesis for Surgery
- MoRE: Multi-Modal X-Ray/ECG Pretraining
- SleepCoT: Personalized Health via CoT

Medical LLM Applications:
- ONCOPILOT: CT Model for Tumors
- LMLPA: Linguistic Personality Assessment
- GenAI for Medical Training

Medical LLMs & Benchmarks:
- LLM Evaluation Through Explanations
- Contrastive Decoding for Medical LLM Hallucination

AI in Healthcare Ethics:
- Healthcare XAI Through Storytelling
- Clinical LLM Bias Analysis
- ReflecTool: Reflection-Aware Clinical Agents

Full Thread: https://x.com/OpenlifesciAI/status/1850202986053808441
Now you can watch and listen to the latest Medical AI papers daily on our YouTube and Spotify channels as well!

- 🎙 Spotify: https://podcasters.spotify.com/pod/show/medicalai/episodes/Medical-AI-Weekly-Digest-From-Deepfake-Detection-to-Clinical-LLMs-Oct-19-26--Part-1-e2q6012

- YouTube: https://youtu.be/Wt5QOv1vk2U

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aaditya AtAndDev Healthtensor models4world


```
@echo off
echo hello world
pause
```


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

https://youtu.be/HKX8_F1Er_w

Do not skip any part of this tutorial to master how to use Stable Diffusion 3 (SD3) with the most advanced generative AI open source APP SwarmUI. Automatic1111 SD Web UI or Fooocus are not supporting the #SD3 yet. Therefore, I am starting to make tutorials for SwarmUI as well. #StableSwarmUI is officially developed by the StabilityAI and your mind will be blown after you watch this tutorial and learn its amazing features. StableSwarmUI uses #ComfyUI as the back end thus it has all the good features of ComfyUI and it brings you easy to use features of Automatic1111 #StableDiffusion Web UI with them. I really liked SwarmUI and planning to do more tutorials for it.

🔗 The Public Post (no login or account required) Shown In The Video With The Links ➡ https://www.patreon.com/posts/stableswarmui-3-106135985

0:00 Introduction to the Stable Diffusion 3 (SD3) and SwarmUI and what is in the tutorial
4:12 Architecture and features of SD3
5:05 What each different model files of Stable Diffusion 3 means
6:26 How to download and install SwarmUI on Windows for SD3 and all other Stable Diffusion models
8:42 What kind of folder path you should use when installing SwarmUI
10:28 If you get installation error how to notice and fix it
11:49 Installation has been completed and now how to start using SwarmUI
12:29 Which settings I change before start using SwarmUI and how to change your theme like dark, white, gray
12:56 How to make SwarmUI save generated images as PNG
13:08 How to find description of each settings and configuration
13:28 How to download SD3 model and start using on Windows
13:38 How to use model downloader utility of SwarmUI
14:17 How to set models folder paths and link your existing models folders in SwarmUI
14:35 Explanation of Root folder path in SwarmUI
14:52 VAE of SD3 do we need to download?

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I've made an on device AI comparison between open source, Apple Intelligence, and Microsoft Copilot+ PC. This OS and applications level integration will bring GenAI to everyone, be it consumers or businesses, over the next year.

Communities and BigTech hold divergent visions regarding the problems they aim to solve, ways to lock in users and enterprises, as well as their commercialization and GTM strategies.

I'm aware that this table has the potential to expand into an epic 30-page saga during an in-depth analysis, but hey, it's a beginning. Do you think I should throw in a few more comparisons? I'm all ears for your thoughts and critiques!

Make sure you own your AI. AI in the cloud is not aligned with you; it's aligned with the company that owns it

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What is your favorite part of our Diffusers integration of Stable Diffusion 3?

My personal favorite is the ability to run it on a variety of different GPUs with minimal code changes.

Learn more about them here:
https://huggingface.co/blog/sd3

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Updated the Journalists on 🤗 community page:
- new text-to-speech tools collection https://huggingface.co/collections/JournalistsonHF/text-to-speech-6675c4dccdaa11e86928a15b
- additional leaderboards in the eval collection: https://huggingface.co/spaces/TTS-AGI/TTS-Arena and https://huggingface.co/spaces/dylanebert/3d-arena
- new tools in the Text-Analysis collection: https://huggingface.co/spaces/gokaygokay/Florence-2, https://huggingface.co/spaces/pdf2dataset/pdf2dataset, https://huggingface.co/spaces/cvachet/pdf-chatbot
- https://huggingface.co/spaces/Xenova/realtime-whisper-webgpu in the Transcription collection
- https://huggingface.co/spaces/radames/flash-sd3-taesd3 in the Image Tools collection
- Last but not least, https://huggingface.co/spaces/okaris/omni-zero in the fun collection for zero-shot stylized portrait creation

Is there any tool you would like to see added?

Find all the curated tools here: https://huggingface.co/collections/JournalistsonHF/

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Finally, a good handwriting recognition tool?

I'm impressed by Microsoft's latest vision model, Florence-2 https://huggingface.co/microsoft/Florence-2-large

The results are really good, boasting a remarkably low error rate, as you can see with this letter from George W. Bush to Bill Clinton!

🚀🔒 What’s even better? You can run it locally on your device, ensuring your data stays 100% safe.

👉 Try it out here: https://huggingface.co/spaces/gokaygokay/Florence-2

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@Be-Bo
Dear Mr. Bahaa Shamoon Atia,
My name is Krischan Schoeninger, and I am very impressed with your Llama 3-70B Chatbot that you have made available on Hugging Face. I have been trying to use both your chatbot and the model from Hugging Face via API for a project, and I have found that your model produces significantly better results.
Could you please let me know what changes or optimizations you have made to your model that make it so powerful? Additionally, I am very interested in learning how I can host such a model myself. Could you assist me with this?
I would greatly appreciate your feedback.
Best regards,
Krischan Schoeninger


Several methods/models have recently been shared to generate synthetic data from minimal or no initial seeds, essentially creating data directly from raw text.

IMO, these approaches that rely on smaller models for synthetic data generation are quite valuable for scaling up synthetic data and democratizing access to creating domain-specific synthetic datasets.

I've compiled a collection of Gradio demos showcasing some of these methods here: https://huggingface.co/collections/davanstrien/synthetic-data-generation-demos-667573f248b97360ff3668a5

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4M-21: An Any-to-Any Vision Model for Tens of Tasks and Modalities (2406.09406)

EPFL and Apple (at @EPFL-VILAB)  just released 4M-21: single any-to-any model that can do anything from text-to-image generation to generating depth masks! 🙀
4M is a multimodal training framework introduced by Apple and EPFL.
Resulting model takes image and text and output image and text 🤩

Models: https://huggingface.co/collections/EPFL-VILAB/4m-models-660193abe3faf4b4d98a2742
Demo: https://huggingface.co/spaces/EPFL-VILAB/4M
Paper: https://huggingface.co/papers/2406.09406

This model consists of transformer encoder and decoder, where the key to multimodality lies in input and output data:

input and output tokens are decoded to generate bounding boxes, generated image's pixels, captions and more!

This model also learnt to generate canny maps, SAM edges and other things for steerable text-to-image generation 🖼

The authors only added image-to-all capabilities for the demo, but you can try to use this model for text-to-image generation as well ☺

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The new Claude Sonnet 3.5 model from Anthropic AI has been getting good reviews on since last night. It is quite good at coding related tasks. We tried it on the Static Analysis Eval benchmark (https://huggingface.co/datasets/patched-codes/static-analysis-eval) which measures the ability of a LLM to fix vulnerabilities. The model scores 59.21% which is good but not better than other frontier models (like GPT-4, Gemini-1.5 and LLama-3).


Hey all!

Here I take a somewhat strong stance and am petitioning to revisit the default training parameters on the Diffusers LoRA page.

In my opinion and after observing and testing may training pipelines shared by startups and resources, I have found that many of them exhibit the same types of issues. Upon discussing with some of these founders and creators, the common theme has been working backwards from the Diffusers LoRA page.

In this article, I explain why the defaults in the Diffuser LoRA code produce some positive results, which can be initially misleading, and a suggestion on how that could be improved.

https://huggingface.co/blog/alvdansen/revisit-diffusers-default-params


GLiNER multi-task: Generalist Lightweight Model for Various Information
  Extraction Tasks (2406.12925)

We’re thrilled to share our latest technical paper on the multi-task GLiNER model. Our research dives into the following exciting and forward-thinking topics:

🔍 Zero-shot NER & Information Extraction: We demonstrate that with diverse and ample data, paired with the right architecture, encoders can achieve impressive results across various extraction tasks;

🛠 Synthetic Data Generation: Leveraging open labelling by LLMs like Llama, we generated high-quality training data. Our student model even outperformed the teacher model, highlighting the potential of this approach.

🤖 Self-Learning: Our model showed consistent improvements in performance without labelled data, achieving up to a 12% increase in F1 score for initially challenging topics. This ability to learn and improve autonomously is a very perspective direction of future research!

https://huggingface.co/papers/2406.12925
https://huggingface.co/knowledgator/gliner-multitask-large-v0.5
https://huggingface.co/spaces/knowledgator/GLiNER_HandyLab

```
#!pip install gliner -U

from gliner import GLiNER

model = GLiNER.from_pretrained("knowledgator/gliner-multitask-large-v0.5")

text = """
Microsoft was founded by Bill Gates and Paul Allen on April 4, 1975 to develop and sell BASIC interpreters for the Altair 8800.
"""

labels = ["founder", "computer", "software", "position", "date"]

entities = model.predict_entities(text, labels)

for entity in entities:
    print(entity["text"], "=>", entity["label"])
```


I'm about to start storing files in my TFLOPS count..

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I am excited to share Synthetic Data Workshop, a Space that aims to simplify creating synthetic datasets!

✅ Pre-configured environment
✅ Ready-to-use notebooks
✅ No local GPU needed

You can try the Space here: https://huggingface.co/spaces/davanstrien/synthetic-data-workshop

I also wrote a blog post going into more detail about the motivations for the Space: https://huggingface.co/blog/davanstrien/synthetic-data-workshop


🔍 A recently published technical report introduces MINT-1T, a dataset that will considerably expand open-source multimodal data. It features one trillion text tokens and three billion images and is scheduled for release in July 2024.

Researcher Affiliation:

University of Washington
Salesforce Research
Stanford University
University of Texas at Austin
University of California, Berkeley

Paper:
MINT-1T: Scaling Open-Source Multimodal Data by 10x: A Multimodal Dataset with One Trillion Tokens
https://arxiv.org/pdf/2406.11271v1.pdf

GitHub:
https://github.com/mlfoundations/MINT-1T

Highlights:

MINT-1T Dataset: Largest open-source multimodal interleaved dataset with 1 trillion text tokens & 3 billion images. 📊🖼
Diverse Sources: Incorporates data from HTML, PDFs, and ArXiv documents. 📄📚
Open Source: Dataset and code will be released at https://github.com/mlfoundations/MINT-1T. 🌐🔓
Broader Domain Representation: Uses diverse data sources for balanced domain representation. 🌍📚
Performance in Multimodal Tasks: The dataset’s scale and diversity should enhance multimodal task performance. 🤖💡

Datasheet Information:

Motivation: Addresses the gap in large-scale open-source multimodal datasets. 🌐📊
Composition: 927.6 million documents, including HTML, PDF, and ArXiv sources. 📄📚
Collection Process: Gathered from CommonCrawl WARC and WAT dumps, with rigorous filtering. 🗂🔍
Preprocessing/Cleaning: Removal of low-quality text, duplicates and anonymization of sensitive information. 🧹🔒
Ethical Considerations: Measures to ensure privacy and avoid bias. ⚖🔏
Uses: Training multimodal models, generating interleaved image-text sequences, and building retrieval systems. 🤖📖


Towards a Dynamic 2.0 Model of Generative Intelligence

https://empereur-pirate.medium.com/towards-a-dynamic-2-0-model-of-generative-intelligence-6128d64fb523

This article explores the limitations of current AI language models, which do not allow user contributions and restrict freedom of expression through corporate censorship. It argues for a participatory approach to AI development, emphasizing the need for open and transparent models that integrate user feedback and contributions. The article highlights the importance of network neutrality and the potential of AI to enhance creativity and collective intelligence. It calls for political and institutional support to foster innovation in AI, ensuring that these technologies respect fundamental rights and promote qualitative performance through inclusivity and transparency.


Florence-2 is a new vision foundation model capable of a wide variety of tasks 🤯
Demo 👉🏻 https://huggingface.co/spaces/gokaygokay/Florence-2
Collection 👉🏻 https://huggingface.co/collections/microsoft/florence-6669f44df0d87d9c3bfb76de

This model can handle tasks that vary from OCR to semantic segmentation.

The difference from previous models is that the authors have compiled a dataset consisting of 126M images with 5.4B annotations labelled with their own data engine pseudolabelled by smaller specialized models and APIs.

The model has a similar architecture to previous models: an image encoder and a multimodality encoder with a text decoder. The authors have compiled the multitask dataset with prompts for each task.

You can also fine-tune this model on any task of choice. The authors also released different results on downstream tasks and reported their results when un/freezing the vision encoder 🤓📉
They have released fine-tuned models too, you can find them in the collection above 🤗

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Check out AutoRound, SOTA LLM quantization algorithm across 2-4 bits without adding any inference overhead to any model
paper: https://arxiv.org/abs/2309.05516
github: https://github.com/intel/auto-round
lowbits leaderboard: https://huggingface.co/spaces/Intel/low-bit-leaderboard


nicolay-r at SemEval-2024 Task 3: Using Flan-T5 for Reasoning Emotion
  Cause in Conversations with Chain-of-Thought on Emotion States (2404.03361)

With the most recent workshop on Semantic Evaluation as a part of NAACL-2024, this year delighted to contribute with 🧪 on Chain-of-Thought fine-tuning concepts  to push forward LLMs reasoning capabilities in:

🧪 1. Reading Comprehension of Numerals in texts  🇨🇳
⭐ https://github.com/GavinZhao19/SemEval24-NumAnalysis-CN
🔒 https://huggingface.co/GavinZhao23/NumAnalysis-Chatglm3-6B

🧪 2. Extracting Emotion-Causes using Reasoning Revision (RR)
⭐ https://github.com/nicolay-r/THOR-ECAC
🔓 https://huggingface.co/nicolay-r/flan-t5-emotion-cause-thor-base
 https://huggingface.co/papers/2404.03361

🔑 In short, there are three major takeaways:
✅ 1. The scale of the backboned LLM for SFT matters (>1.1B is preferable)
✅ 2. The language of the input data  matters in LLM reasoning capabilities: transfering data in English and picking English-based LLM is crucial for the most cases!
✅ 3. CoT and RR takes more time ⏳ for inferring and fine-tuning, proportionally to abount of steps in chain / amount of revisions in reasoning 🧠


🚀 Exciting news about https://huggingface.co/spaces/as-cle-bert/proteinviz, your fully open-source protein structure prediction tool!
🧬 I'm thrilled to announce 𝘽𝙪𝙡𝙠𝙋𝙧𝙤𝙩𝙚𝙞𝙣𝙫𝙞𝙯, a new functionality which  supports multiple structure predictions at once: you just need to upload a FASTA file with all the amino-acidic sequences, and you'll be done in minutes!
🏃 This can be really helpful in speeding up your research: give it a shot, if you are curious!🤗

(Demo in the attached video)


@ehartford hey eric, i have been following u since your first dolphin model.... i realy appreciate you doing what you do.  none the less, i have a quick question.  i was wondering what is your favorite smaller uncensored dolphin model that would run well on my m1 -8gb macbook air.. and what user interface do you suggest? thanks so much, and i hope to hear from you soon.


🔥 🔥 Releasing our new paper on AI safety alignment -- Safety Arithmetic: A Framework for Test-time Safety Alignment of Language Models by Steering Parameters and Activations 🎯 with Sayan Layek, Somnath Banerjee and Soujanya Poria.

👉 We propose Safety Arithmetic, a training-free framework enhancing LLM safety across different scenarios: Base models, Supervised fine-tuned models (SFT), and Edited models. Safety Arithmetic involves Harm Direction Removal  (HDR) to avoid harmful content and Safety Alignment to promote safe responses.

👉 Paper: https://arxiv.org/abs/2406.11801v1
👉 Code: https://github.com/declare-lab/safety-arithmetic


Forget about all the captioning datasets you've tried before!

PixelProse is a captioning dataset of 16M image-caption pairs, with less toxicity and higher details ✨
https://huggingface.co/datasets/tomg-group-umd/pixelprose

The existing suite of captioning datasets consists of web scrapes that have alt text that is either irrelevant or not descriptive. The authors of this paper have taken those datasets, filtered for CSAM, passed it with a prompt to Gemini Vision Pro. They also removed PII and detoxified the resulting dataset.

darknoon KingNish AtAndDev louisbrulenaudet


⚗ Looking to get started with Synthetic data and AI Feedback?

I created this cool notebook for a workshop @davanstrien and I gave it a couple of weeks back. It uses https://distilabel.argilla.io/dev/ and I think it is a good entry point for anyone with a practical interest in the topic.

https://colab.research.google.com/github/davanstrien/data-for-fine-tuning-llms/blob/main/03-synthetic-data-generation.ipynb

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On-Device deployment ready MobileNet-V4 models:
https://huggingface.co/collections/byoussef/mobilenetv4-conv-pretrained-tflite-models-66726c945040fc61f5a50b63

Models converted from timm's pretrained Pytorch weights to TFLite (fp32 & fp16).
All conv models fully compatible with TFLite Gpu Delegate on Android for fast on-device inference 🚀
Can also run super fast on Google-Edge-TPU on Pixel phones 🚀


🤯🤯🤯VERY ROBUST TOOL to control camera motion for videos!!!

Even doesn't need any additional finetuning! It uses inference process of video diffusion directly!!

Try it on your own video diffusion model and generate CINEMATIC SHOTS!📸🎥🫢

Check at https://lifedecoder.github.io/CamTrol/

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Principled Instructions Are All You Need for Questioning LLaMA-1/2,
  GPT-3.5/4 (2312.16171)

https://huggingface.co/papers/2312.16171 I normally use this to make prompts in the form of a RAG (Retrieval Augmented Generation). For example, here's one from Gemma 7B about articles.

"Please summarize the main ideas of the article '[Article Title]' in a concise and informative manner. Focus on highlighting the key points and arguments presented in the article. Keep the summary to around [desired length] words."

Has anyone else tried this? Do you like the results you are getting?


⚗ distilabel 1.2.0 is out and it comes with improved support for structured generation, new tasks for generating datasets for training embedding models, new steps for loading data, MixtureOfAgentsLLM and improved docs.

We would love to see a few new datasets for training embedding models built with distilabel on the Hub! ❤

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I've fine-tuned three types of PaliGemma image captioner models for generating prompts for Text2Image models. They generate captions similar to prompts we give to the image generation models. I used google/docci and google/imageinwords datasets for fine-tuning.

This one gives you longer captions.

https://huggingface.co/spaces/gokaygokay/SD3-Long-Captioner

This one gives you middle size captions.

https://huggingface.co/spaces/gokaygokay/SD3-Long-Captioner-V2

And this one gives you shorter captions.

https://huggingface.co/spaces/gokaygokay/SDXL-Captioner

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With the CVPR conference (https://cvpr.thecvf.com) in full swing this week in Seattle 🏙, the competition details for NeurIPS 2024 have just been released.🚀

Some of the competitions this year include:

🦾 MyoChallenge 2024: Physiological dexterity in bionic humans.
🌌 FAIR Universe: Handling uncertainties in fundamental science.
🧪 BELKA: Chemical assessment through big encoded libraries.
🏆 HAC: Hacker-Cup AI competition.
💰 Large-Scale Auction Challenge: Decision-making in competitive games.
📶 URGENT Challenge: Signal reconstruction and enhancement.
🛡 LASC 2024: Safety in LLM and AI agents.

For more details, check out: https://blog.neurips.cc/2024/06/04/neurips-2024-competitions-announced


Impressive to see Depth Anything V2. See this example I just took with a lot of different depths.

If you want to learn more about it, this TLDR by @merve is👌 https://huggingface.co/posts/merve/568638914646708


I love Depth Anything V2 😍
It’s Depth Anything, but scaled with both larger teacher model and a gigantic dataset!

Here's a small TLDR of paper with a lot of findings, experiments and more.
I have also created a collection that has the models, the dataset, the demo and CoreML converted model 😚 https://huggingface.co/collections/merve/depth-anything-v2-release-6671902e798cd404513ffbf5

The authors have analyzed Marigold, a diffusion based model against Depth Anything and found out what’s up with using synthetic images vs real images for MDE:

🔖  Real data has a lot of label noise, inaccurate depth maps (caused by depth sensors missing transparent objects etc) and there are many details overlooked

🔖 Synthetic data have more precise and detailed depth labels and they are truly ground-truth, but there’s a distribution shift between real and synthetic images, and they have restricted scene coverage

The authors train different image encoders only on synthetic images and find out unless the encoder is very large the model can’t generalize well (but large models generalize inherently anyway) 🧐
But they still fail encountering real images that have wide distribution in labels (e.g. diverse instances of objects) 🥲

Depth Anything v2 framework is to..

🦖 Train a teacher model based on DINOv2-G based on 595K synthetic images
🏷 Label 62M real images using teacher model
🦕 Train a student model using the real images labelled by teacher
Result: 10x faster and more accurate than Marigold!

The authors also construct a new benchmark called DA-2K that is less noisy, highly detailed and more diverse!

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Hey All!

I've been asked a lot of share more on how I train LoRAs. The truth is I don't think my advice is very helpful without also including more contextual, theoretical commentary on how I **think** about training LoRAs for SDXL and other models.

I wrote a first article here about it - let me know what you think.

https://huggingface.co/blog/alvdansen/thoughts-on-lora-training-1

Edit: Also people kept asking where to start so I made a list of possible resources:
https://huggingface.co/blog/alvdansen/thoughts-on-lora-training-pt-2-training-services

victor shaising merve not-lain netynet dillfrescott KingNish philipp-zettl kramp

digiplay victor merve not-lain dillfrescott


💰 𝗚𝗲𝘁 𝘁𝗵𝗲 𝗽𝗿𝗶𝗰𝗲 𝗼𝗳 𝗮𝗻𝘆 𝗟𝗟𝗠 𝗔𝗣𝗜 𝗿𝗲𝗾𝘂𝗲𝘀𝘁 ⇒ 𝘁𝗼𝗸𝗲𝗻𝗰𝗼𝘀𝘁

I've just found out about 𝙰𝚐𝚎𝚗𝚝𝙾𝚙𝚜-𝙰𝙸/𝚝𝚘𝚔𝚎𝚗𝚌𝚘𝚜𝚝 (https://github.com/AgentOps-AI/tokencost).
𝗧𝗵𝗶𝘀 𝗹𝗶𝗯𝗿𝗮𝗿𝘆 𝗴𝗶𝘃𝗲𝘀 𝘆𝗼𝘂 𝘁𝗵𝗲 𝗽𝗿𝗶𝗰𝗲 𝗼𝗳 𝘆𝗼𝘂𝗿 𝗰𝗮𝗹𝗹𝘀 𝘁𝗼 𝗮𝗻𝘆 𝗟𝗟𝗠 𝗔𝗣𝗜: OpenAI, Anthropic, Mistral, AWS or Databricks...

For any model, you can use as input either string prompts or messages, and get as outputs either the price or token count.

Congrats to the AgentOps-AI team: this will be very useful when trying to get a ballpark estimate of a project's price, to compare APIs, or for precise monitoring of usage!

✨ Daily reminder: 𝗿𝘂𝗻𝗻𝗶𝗻𝗴 𝗮𝗻 𝗔𝟭𝟬𝟬 𝗰𝗼𝘀𝘁𝘀 𝘆𝗼𝘂 𝗲𝘅𝗮𝗰𝘁𝗹𝘆 $𝟬.𝟬𝟬/𝗵𝗼𝘂𝗿 (or 0.00€ in current exchange rates) on a HF space with ZeroGPU!
Learn more on ZeroGPU 👉 https://www.datacenterdynamics.com/en/news/hugging-face-launches-zerogpu-project-to-democratize-ai-gives-away-10-million-worth-of-compute/

umair894 Pavarissy kramp victor gabrielmbmb Taylor658 sigridjineth maywell radames sosoai prithivMLmods introvoyz041


https://huggingface.co/datasets/nevmenandr/incoming-students-ma-dh-hse-university
Dataset visualized by datawrapper

```html
<div style="min-height:494px"><script type="text/javascript" defer src="https://datawrapper.dwcdn.net/q3waH/embed.js?v=2" charset="utf-8"></script><noscript><img src="https://datawrapper.dwcdn.net/q3waH/full.png" alt="" /></noscript></div>
```
Data from my talk in February: https://www.youtube.com/watch?v=ZfXqvIzl5fo . Slides: https://nevmenandr.github.io/slides/2024-02-02/slides.pdf.


Remember Will Smith eating Spaghetti? 🍝😆

AI has come a long way from generating hilariously low-quality videos to almost unrealistic realistic videos 🎥✨

But most models like @OpenAI Sora, @Kling_ai , etc are not publicly available. 🚫🖥

But now we have @LumaLabsAI Dream Machine, which is publicly available for free! 🎉🆓

Here is the dilemma, Sora and Kling posted some excellent examples of what the AI was capable of, and so did Luma AI. 🌟🤖

But in actual use, they leave so much to be desired. 😕 Are we back to cherry-picking examples and leaking benchmarks in training data? 🍒📊

Try Dream Machine 👉 https://lumalabs.ai/dream-machine 🌐


Hey guys! @mk230580 @wikeeyang @Yasirkh & others asked how to run the Hugging Face spaces outside of the HF environment locally with their source editor and Google Colab. Here is how to do that simply 👇👇.

📍I have just created a step-by-step procedure with a Colab demo link also attached in the repository's README.md.

🔗: https://github.com/prithivsakthiur/how-to-run-huggingface-spaces-on-local-machine-demo

Thanks for a read !!

wikeeyang osanseviero xi0v ashail mk230580 ezzdev prithivMLmods


🚀 Sarashina1-65B

SB Intuitions has announced the release of Japanese Large Language Models (LLMs) with 7 billion, 13 billion, and 65 billion parameters to aid academic and industrial research and development. The company plans to develop a 390 billion parameter model by the end of 2024. The models, named Sarashina1 and Sarashina2, show significant performance improvements, especially Sarashina2 which is an enhanced version of Sarashina1.

Performance evaluations using five Japanese language datasets reveal that Sarashina2 outperforms other models, including continued pre-trained models. The name "Sarashina" originates from a historical diary linked to the headquarters' location in Tokyo's Takeshiba area, symbolizing the company's ambition to create globally utilized models from Japan.

Model URL:
- https://huggingface.co/sbintuitions/sarashina1-65b
- https://huggingface.co/sbintuitions/sarashina2-13b

Detailed press release (in Japanese):
https://www.sbintuitions.co.jp/news/press/20240614_01/

osanseviero julien-c victor zafina yjernite Nulooper


Repurposing Diffusion-Based Image Generators for Monocular Depth
  Estimation (2312.02145)

Point2CAD: Reverse Engineering CAD Models from 3D Point Clouds (2312.04962)

DGInStyle: Domain-Generalizable Semantic Segmentation with Image
  Diffusion Models and Stylized Semantic Control (2312.03048)

Join us at our remaining CVPR presentations this week! Members of PRS-ETH will be around to connect with you and discuss our presented and ongoing works:

💐 Marigold: Discover our work on sharp diffusion-based computer vision techniques, presented in Orals 3A track on "3D from Single View", Thu, June 20, 9:00-9:15 AM. Also, drop by Poster Session 3 later that day for more tangible matters! 🌚
Project page: https://marigoldmonodepth.github.io/
Paper: https://huggingface.co/papers/2312.02145
Collection: https://huggingface.co/collections/prs-eth/marigold-6669e9e3d3ee30f48214b9ba
Space: https://huggingface.co/spaces/prs-eth/marigold-lcm
Diffusers 🧨 tutorial: https://huggingface.co/docs/diffusers/using-diffusers/marigold_usage

⚙ Point2CAD: Learn about our mechanical CAD model reconstruction from point clouds, presented in Poster Session 1, Wed, June 19, 10:30 AM - 12:00 PM.
Project page: https://www.obukhov.ai/point2cad.html
Paper: https://huggingface.co/papers/2312.04962

🎭 DGInStyle: Explore our generative data synthesis approach as a cost-efficient alternative to real and synthetic data, presented in the Workshop on Synthetic Data for Computer Vision, Tue, June 18, at Summit 423-425.
Details and schedule: https://syndata4cv.github.io/
Project page: https://dginstyle.github.io/
Paper: https://huggingface.co/papers/2312.03048
Model: https://huggingface.co/yurujaja/DGInStyle


A nice improvement for Hugging Face on Sheets: You can now customize your prompt and select the model of your choice directly on the sheet.

Thanks to @louisbrulenaudet for the contribution. Really cool to see the community improving this tool!

Try it here: https://huggingface.co/spaces/JournalistsonHF/huggingface-on-sheets

anakin87 osanseviero davanstrien Ramikan-BR not-lain thomwolf cminja thangvip boapps


📁✨ Meet Corpus Creator!

This Gradio app (https://huggingface.co/spaces/davanstrien/corpus-creator) takes you from your local files to a Hugging Face  Dataset via Llama Index.

The goal of the tool is to make it quicker and easier to quickly get some local files you want to get ready for ML tasks into a Hugging Face Dataset. Perfect for  building datasets for:
-  synthetic data pipelines
- annotation
- RAG
- Other ML tasks that start from a HF dataset

I'll share something more substantial that uses this tomorrow 🤗


Finally @CVPR2024 is here! 🩷
Have you claimed your papers and linked your models/datasets/demos?
This will increase visibility and impact of your paper 💫

To index your papers, go here
https://huggingface.co/spaces/CVPR2024/CVPR2024-papers
Find your paper, click on paper page link, index the paper, then click on your name (workflow is below 👇🏻)
If you'd like to add links to your paper, go here https://huggingface.co/spaces/CVPR2024/update-CVPR2024-papers
login, find your paper's id, retrieve the paper, fill in the info and submit!


🧪 RAG Evaluation with 🔥 Prometheus 2 + Haystack

📝 Blog post: https://haystack.deepset.ai/blog/rag-evaluation-with-prometheus-2
📓 Notebook: https://github.com/deepset-ai/haystack-cookbook/blob/main/notebooks/prometheus2_evaluation.ipynb

─── ⋆⋅☆⋅⋆ ───

When evaluating LLMs' responses, 𝐩𝐫𝐨𝐩𝐫𝐢𝐞𝐭𝐚𝐫𝐲 𝐦𝐨𝐝𝐞𝐥𝐬 like GPT-4  are commonly used due to their strong performance.
However, relying on closed models presents challenges related to data privacy 🔒, transparency, controllability, and cost 💸.

On the other hand, 𝐨𝐩𝐞𝐧 𝐦𝐨𝐝𝐞𝐥𝐬 typically do not correlate well with human judgments and lack flexibility.

🔥 Prometheus 2  is a new family of open-source models designed to address these gaps:
🔹 two variants: https://huggingface.co/prometheus-eval/prometheus-7b-v2.0; https://huggingface.co/prometheus-eval/prometheus-8x7b-v2.0
🔹 trained on open-source data
🔹 high correlation with human evaluations and proprietary models
🔹 highly flexible: capable of performing direct assessments and pairwise rankings, and allowing the definition of custom evaluation criteria.

See my experiments with RAG evaluation in the links above.


My weekened project ended up being doing some testing between torchtune, axolotl, and unsloth. I *think* it's a 1:1 comparison of what LoRA fine-tuning performance looks like between the different hardware I have in my dev boxes (4090, 3090, 7900 XTX, W7900) with a few other interesting tidbits.

Tonight I wrote up a WandB report (the panel editor is super broken in Firefox 😔) that sums up some of the more interesting bits from the results: https://wandb.ai/augmxnt/train-bench/reports/torchtune-vs-axolotl-vs-unsloth-Trainer-Comparison--Vmlldzo4MzU3NTAx

brainhome louisbrulenaudet Weyaxi ManniX-ITA


🌟 Progress in the German FineWeb edu reproduction 🌟

We're delighted to share the launch of our new Data Quality Classification Model, designed specifically for evaluating educational content in German. This tool uses advanced machine learning techniques to assess texts across all educational levels, from primary school to university.

🔍 Inspired by Huggingface's fine web edu dataset, we've worked hard to refine data classification methods ensuring educators and learners access top-quality resources.
We're excited about the future as we continue improving our models and expanding our datasets.

Access the model here: https://huggingface.co/pL-Community/GermanEduScorer-Qwen2-1.5b

🙏 A huge thank you to David and Daryoush from Vago Solutions; Björn and Jan from Ellamind / DiscoResearch for their expert insights throughout this project. Your support has been crucial.
This project was made possible by the support of PrimeLine AI.


Mixtral or Llama 70B on Google Spreadsheet thanks to Hugging Face's Serverless Inference API 🤗

The Add-on is now available on the HF repo "Journalists on Hugging Face" and allows rapid generation of synthetic data, automatic translation, answering questions and more from simple spreadsheet cells 🖥

Link to the 🤗 Space : https://huggingface.co/spaces/JournalistsonHF/huggingface-on-sheets

Although this tool was initially developed for journalists, it actually finds a much wider inking among daily users of the Google suite and the remaining use cases to be explored are numerous.

Only a free Hugging Face API key is required to start using this no-code extension.

Do not hesitate to submit ideas for features that we could add!

Thanks to @fdaudens for initiating this development.

victor meiaienable JuliusSandmann wsuff SengTak kargaranamir umair894 Lucianocosta aidystark kramp

victor JuliusSandmann MaziyarPanahi nvhf louisbrulenaudet


A great work based on ChemLLM from Open-source community!
Automatic Scientific Discovery guided by LLM!
https://github.com/zyzisastudyreallyhardguy/LLM4SD


📊 Lovely to share the unique reasoning capabilities 🧠 findings of Qwen2-7B 🇨🇳 in Target Sentiment Analysis (TSA) for original texts (🇷🇺) and their translated version in English (🇺🇸), in zero-shot-learning mode.
Since the last update on 1.5, I have to say:
☑ 1.  Qwen2-7B is the first model in my list that reasons 🔥 better 🔥 in Russian rather than in English; it strongly surpasses other 7B LLMs and LLaMA3-70B by correctly distributing sentiment cases (F1(PN) metric).
☑ 2. Surprisingly, but Qwen2-7B significantly underperformed to the "earlier bro" Qwen1.5-7B on texts in English. The key problem is that ~17% of answers has mixed entries of labels, so for such cases the automatic and accurate assessment is difficult. Therefore, I believe it is more about particular evaluation, rather something wrong with the model in TSA domain.

What's next? I have to checkout https://huggingface.co/Qwen/Qwen2-72B-Instruct then 🧪 If you know the best hosting for infering, please let me know 🙏

Model: https://huggingface.co/Qwen/Qwen1.5-7B-Chat


I had a backlog of LoRA model weights for SDXL that I decided to prioritize this weekend and publish. I know many are using SD3 right now, however if you have the time to try them, I hope you enjoy them.

I intend to start writing more fully on the thought process behind my approach to curating and training style and subject finetuning, beginning this next week.

Thank you for reading this post! You can find the models on my page and I'll drop a few previews here.

fffiloni alvdansen LaughterOnWater mmarrder thliang01 dicksondickson MaziyarPanahi victor Brukolakos s3nh not-lain xumijiezi622 Citaman LizzyAsf ijohn07 louisbrulenaudet BennyKok yagmurx zhaoyafei multimodalart MQSI adamelliotfields dillfrescott SengTak milkowski KingNish comarproject ZekiJ E2GO Cordobian Viktor1233 clem

Norod78 Brukolakos clefourrier Dennyanswer s3nh not-lain zhaoyafei multimodalart dillfrescott Cordobian clem


Observability and Retrieval Augmented Generation in 10 lines of Code

Tutorial: https://www.youtube.com/watch?v=VCQ0Cw-GF2U

This video covers:
- Why we need observability?
- Implementation of RAG using BeyondLLM
- Monitor and Track LLM Observability using Phoenix


🖥 Do you have 1TB+ VRAM?

🎉 Well, good news for you!

👨🔬 Good folks at @nvidia have released Nemotron 4 340B, the new open-source LLM king, rivalling GPT-4! 🚀

📊 340B parameter models in 3 flavours: base, reward, and instruct models

🎯 It's a dense model, not MoE

👓 4k context window

📚 9T tokens training data, 2 phase training (8T pre-train + 1T continued pre-training)

🌍 Trained on 50+ languages and 40+ coding languages (70% training data is English, 15% multi-lingual, 15% code)

📅 June 2023 training data cut-off

💻 To deploy needs 8x H200/ 16x H100/ 16x A100 80GB for BF16 Inference (about 8x H100 in int4)

🏆 Of course, it beats Llama 3 70B on MMLU (81.1), Arena Hard (54.2), and GSM8K (92.4)

🤖 But beaten by Qwen 2 on HumanEval and MTBench which is a 72B parameter model

🔧 Used SFT, DPO, and RPO. RLHF via Nemo Aligner framework to align the model

📊 98% of alignment data was synthetically generated

📄 Nvidia open licence with commercial use allowed

¯\_(ツ)_/¯
😅 Glad to see more open models but this is one confusing fellow!
🤨340B parameter model that is narrowly beating 70B models? Starts failing against 72B models? Sounds like a model for synthetic data generation! But then it has 4k context?

🔗 Models: https://huggingface.co/collections/nvidia/nemotron-4-340b-666b7ebaf1b3867caf2f1911

📑 Paper: https://research.nvidia.com/publication/2024-06_nemotron-4-340b


https://huggingface.co/nevmenandr/w2v-russian-tolstoy

```python
import gensim
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline

import seaborn as sns
sns.set_style("darkgrid")

from sklearn.decomposition import PCA
from sklearn.manifold import TSNE

modelLNT2 = Word2Vec.load("cbow_300_10.model")

# skip some code... for full version see model's card

tsnescatterplot(modelLNT2, 'жизнь_S', [i[0] for i in modelLNT2.wv.most_similar(negative=["жизнь_S"])])
```

life by Tolstoy (w2v):


Just published "CryptGPT: A Simple Approach to Privacy-Preserving Language Models Using the Vigenere Cipher".

https://huggingface.co/blog/diwank/cryptgpt-part1

tl;dr - we pretrained a gpt-2 tokenizer and model from scratch on a dataset encrypted with Vigenere cipher and it performs as well as regular gpt-2. Except in order to use it, you need to know the encryption key.

links:
https://github.com/creatorrr/cryptgpt
https://huggingface.co/diwank/cryptgpt
https://huggingface.co/diwank/cryptgpt-large


I've tried out my new Space for copying Websites with Gemini 1.5 Flash and i gave it a image of Huggingchat. The results were interesting, but you can see it for yourself.

Space:
https://huggingface.co/spaces/L-AI/Gemini-UI-Generator

Website-Demo:
https://leunos.com/hf-chat-fake

Screenshot:


Hello!

I was experimenting with multi-bot interactions for practical solutions, such as code synthesis and editing. This has, so far, led to many well-made templates and no working code, but I still feel  a template this lovely is worthy of use. Enjoy! 🤗

https://colab.research.google.com/gist/SMeyersMrOvkill/2ed6cbc305bc5bd62fcf1f7aab15f7b9/voice_memos.ipynb


Together MoA is a really interesting approach based on open source models!

"We introduce Mixture of Agents (MoA), an approach to harness the collective strengths of multiple LLMs to improve state-of-the-art quality. And we provide a reference implementation, Together MoA, which leverages several open-source LLM agents to achieve a score of 65.1% on AlpacaEval 2.0, surpassing prior leader GPT-4o (57.5%)."

Read more here: https://www.together.ai/blog/together-moa

PS: they provide some demo code: (https://github.com/togethercomputer/MoA/blob/main/bot.py) - if someone release a Space for it it could go 🚀

KingNish GPT007 SixOpen Choms SizorCloud nicolay-r osanseviero louisbrulenaudet


📊 Just measured reasoning capabilities 🧠  of Qwen1.5-7B 🇨🇳 in Target Sentiment Analysis (TSA) both for original texts (🇷🇺) and translated in English (🇺🇸), in zero-shot-learning mode.  Here is what I've noticed:
☑ 1. Huge gap 📈 with the smaller Qwen1.5 and Qwen2 (1.8B and 1.8B).  Qwen1.5-7B strongly outperforms their "smaller bros" so that case when scale of the model matters.
☑ 2.  Qwen1.5-7B in english (🇺🇸) behaves similar but slightly underperforming 📉 to the most latest 7B alternatives ... and even including Phi-3-small (3.4B)
☑ 3. On texts in (🇷🇺) there is a certain underperforming  📉  gap between the most latest 7B alternatives: F1=34.1, other 7B starts with 40.23.

In terms of responses, for non-english texts  (🇷🇺) model answers strict and behaves similar to FlanT5.
Curious about improvements in Qwen2-7B 🔥

Model: https://huggingface.co/Qwen/Qwen1.5-7B-Chat
Benchmark: https://github.com/nicolay-r/RuSentNE-LLM-Benchmark
Dataset: https://github.com/dialogue-evaluation/RuSentNE-evaluation
Related paper: Large Language Models in Targeted Sentiment Analysis (2404.12342)
Collection: https://huggingface.co/collections/nicolay-r/sentiment-analysis-665ba391e0eba729021ea101


I've published several older versions of Vokan! Sometimes, they may sound more natural, but less like the target speaker.

Please check em out!
https://huggingface.co/spaces/Korakoe/Vokan-V0.5
https://huggingface.co/ShoukanLabs/Vokan


🚀 llava-calm2-siglip

CyberAgent Inc. has announced the public release of "llava-calm2-siglip," a 7.5 billion parameter Vision Language Model (VLM) for Japanese, available for commercial use. This model, trained primarily on a high-quality Japanese dataset, is accessible on Hugging Face Hub under an Apache-2.0 license. The advancement aims to improve Japanese language-specific VLMs, which are fewer compared to English-centric models.

Model URL:
https://huggingface.co/cyberagent/llava-calm2-siglip

Demo URL:
https://huggingface.co/spaces/cyberagent/llava-calm2-preview

Detailed press release (in Japanese): https://www.cyberagent.co.jp/news/detail/id=30344


Chatting with llava is tricky

https://huggingface.co/spaces/nroggendorff/llava


🔍 Remember Tensorboard graph visualizer?

🚀 @Google just released Model Explorer, a Tensorboard graph visualizer on steroids 💪.

🛠 Model Explorer is a graph visualization tool designed to improve understanding, debugging, and optimizing machine learning (ML) models, especially large ones.

🎯 It addresses challenges in traditional graph visualization tools by implementing a hierarchical layout and GPU-accelerated graph rendering, which enhances performance and usability.

🌐 The tool supports visualization of large-scale ML models by displaying hierarchical information, which simplifies understanding complex model architectures.

🔑 Key features include layer-by-layer exploration 🔍, side-by-side graph comparison for debugging conversion errors 🐛, and per-node data overlays for identifying performance issues 📈.

👨💻 Originally developed for Google's internal use, Model Explorer is now available publicly as part of the Google AI Edge family of products and even runs directly in colab!

🔗 Colab: https://github.com/google-ai-edge/model-explorer/blob/main/example_colabs/quick_start.ipynb

📰 Blog: https://research.google/blog/model-explorer/


Wow, impressive 340B model by nvidia with a nice permissive license! 🚀 The technical report is full of insights and seems to use a different learning rate schedule than cosine, probably a variant of WSD. Hope to get more info on that! 👀

 https://huggingface.co/collections/nvidia/nemotron-4-340b-666b7ebaf1b3867caf2f1911


Me: I want on device AI: fast, without latency, with real privacy, convenient for use and development.

Microsoft: The best I can do is Copilot+. You need a special Qualcomm chip and Windows 11 24H2. Today I can give you only Recall, taking screenshots and running a visual model to write context about what you are doing in the unencrypted Semantic Index database for embeddings. I'm giving you SLMs Phi Silica, accessible only via API and SDK. In the autumn I can give you the developer tools for C#/C++ and you can use them.

Apple: The best I can do is Apple Intelligence. You need a special Apple chip and macOS 15. Today I can give you only marketing. In the autumn I can give you on-device 3B quantized to 3.5bit mysterious SLMs and diffusion models with LoRA adapters. We will have an encrypted Semantic Index database for embeddings and agentic flows with function calling. We will call all of them with different names. In the autumn I will give you the developer tools in Swift and you can use them.

Open Source: The best I can do is llama.cpp. You can run it on any chip and OS. Today you can run AI inferencing on device and add other open source components for your solution. I can give you local AI models SLMs/LLMs - from wqen2-0.5B to Llama3-70B. You can have an encrypted local embeddings database with PostgreSQL/pgvector or SQLite-Vec. I can give you a wide choice of integrations and open-source components for your solution- from UIs to agentic workflows with function calling. Today I can give you the developer tools in Python/C/C++/Rust/Go/Node.js/JS/C#/Scala/Java and you can use them.

Make sure you own your AI. AI in the cloud is not aligned with you; it's aligned with the company that owns it.

Ramikan-BR nrmtvv DeathGodlike adamelliotfields SizorCloud lout33 ToKrCZ noh-au-00 TechWTy ArslanMehmood

Ramikan-BR yukiarimo nrmtvv DeathGodlike lout33 nicoism

dashfunnydashdash GPT007 Metin Zeldazackman


@CHANEFO  suite à votre post.

Je peux essayer peut-être de vous guider ?

« Si je souhaite paramétrer un assistant orienté vers un sujet spécifique concernant l'application du droit du travail dans mon entreprise, comment procéder ?
Le but de faire référence à un ensemble de document en lien avec des accords collectif qui sont dans des document type PDF ou WORD. Quel limite sur la taille des documents et ou téléchargé les fichier pour y faire référence ? »


Wow, this is amazing! 🤯
Samba is a powerful hybrid model with an unlimited context length, combining Mamba, MLP, Sliding Window Attention, and MLP stacking. Samba largest version, Samba-3.8B, trained on 3.2 trillion tokens, excels in benchmarks like MMLU, GSM8K, and HumanEval, and shines in long-context tasks with minimal tuning.
---
Official implementation of "Samba: Simple Hybrid State Space Models for Efficient Unlimited Context Language Modeling"
Github: https://github.com/microsoft/Samba

Srulikbdd anakin87 seyf1elislam ZeroWw dillfrescott satyamt Felladrin den0620 victor GPT007 mihaylovnikitos NHLOCAL AtAndDev


Tools Ready!
Thanks to ChemCrow's great work, ChemLLM supports proficiency toolkits Now, Include,
Molecule Name Retrivel
Molecule Property Query
Patent Check
Molecule Safety Query
Try it on chemllm.org


📢 Suprisingly, there are so many works on imputing personalities in LLM and vice versa. However, there is a gap in literature novels  📚  for mining that personalities from book itself. With that I am happy to release worflow that 🔥 solely 🔥 relies on book  content only 📖 for personalities extraction:
https://github.com/nicolay-r/book-persona-retriever

💡 The downstream goal of this workflow is to enhance charactes understanding ... and not just through their mentions in books, but through their personalities (⛏ retrieved with the given lexicon from the 📖 itself)

The most closest studies such as PERSONA-CHAT (arXiv:1801.07243v5),  BookEmbeddingEval (2022.findings-acl.81.pdf), ALOHA-Chatbot ( arXiv:1910.08293v4), Meet your favorite Character (arXiv:2204.10825), and PRODIGy (arXiv:2311.05195v1) were so valuable 💎 ! 👏

Curious on existance of the fine-tuned LLM for detecting personalities in text passages on huggingface hub  🤗 If you aware about the one coud be potentially embedded into system for further advances, please feel free to recomend 🙌

spacemonkAI87 ThatoH Ervist victor Wolter8711 MrF-Qwerty diwank


Today is a huge day in Argilla’s history. We couldn’t be more excited to share this with the community: we’re joining Hugging Face!

We’re embracing a larger mission, becoming part of a brilliant and kind team and a shared vision about the future of AI.

Over the past year, we’ve been collaborating with Hugging Face on countless projects: launching partner of Docker Spaces, empowering the community to clean Alpaca translations into Spanish and other languages, launching https://huggingface.co/argilla/notus-7b-v1 building on Zephyr’s learnings, the Data is Better Together initiative with hundreds of community contributors, or releasing https://huggingface.co/datasets/argilla/OpenHermesPreferences, one of the largest open preference tuning datasets

After more than 2,000 Slack messages and over 60 people collaborating for over a year, it already felt like we were part of the same team, pushing in the same direction. After a week of the smoothest transition you can imagine, we’re now the same team.

To those of you who’ve been following us, this won’t be a huge surprise, but it will be a big deal in the coming months. This acquisition means we’ll double down on empowering the community to build and collaborate on high quality datasets, we’ll bring full support for multimodal datasets, and we’ll be in a better place to collaborate with the Open Source AI community. For enterprises, this means that the Enterprise Hub will unlock highly requested features like single sign-on and integration with Inference Endpoints.

As a founder, I am proud of the Argilla team. We're now part of something bigger and a larger team but with the same values, culture, and goals. Grateful to have shared this journey with my beloved co-founders Paco and Amélie.

Finally, huge thanks to the Chief Llama Officer @osanseviero for sparking this and being such a great partner during the acquisition process.

Would love to answer any questions you have so feel free to add them below!

osanseviero gabrielmbmb fdaudens marcsun13 Leiyre alvarobartt victor loubnabnl frascuchon Wauplin lhoestq Xenova burtenshaw lysandre Narsil andrewrreed mcpotato plaguss mlabonne lunarflu ybelkada VictorSanh louisguitton Molbap alielfilali01 ceyda Citaman jeffboudier eliebak pierrci MKdir98 philschmid kevinostoll giux78 jphme Taylor658 mgoin wath5 strickvl tomaarsen not-lain julien-c GloriaMK alexsherstinsky merve nara-simba KingNish bartowski monsoon-nlp santiviquez KonradSzafer pcuenq olivierrousseau07 prepsyched cloderic bergr7f chargoddard haritzpuerto 4eyez GPT007 yjernite deanwampler mattupson rbgo MoritzLaurer oza75 sergiopaniego abidlabs gsarti medmac01 radames lewtun munish0838 eitanturok sugatoray flozi00 acelik louisbrulenaudet philipp-zettl Lin-Rexter alexcombessie drt14 mikv39 amcasari severo pacoid nthakur bullerwins JJoe206 ngxson petricevich mohamedsaeed823 jessicagab aust-t clem beta3 jimmy agenkit bielrv davanstrien Prakash-chokalingam Ramikan-BR taufiqdp blefaudeux afondiel zaanind HappyCat123

vicgalle gabrielmbmb nataliaElv marcsun13 alvarobartt victor loubnabnl frascuchon Wauplin lhoestq Xenova burtenshaw lysandre andrewrreed mcpotato plaguss mlabonne lunarflu ybelkada mpjuhasz VictorSanh Molbap alielfilali01 ceyda jeffboudier eliebak pierrci philschmid Yasbok kevinostoll giux78 strickvl tomaarsen not-lain julien-c GloriaMK alexsherstinsky KingNish monsoon-nlp KonradSzafer pcuenq osanseviero NickyNicky 4eyez mdouglas yjernite InferenceIllusionist mattupson rbgo MoritzLaurer sergiopaniego abidlabs gsarti medmac01 radames lewtun munish0838 secularwarden bewizz FM-1976 louisbrulenaudet Lin-Rexter drt14 ZennyKenny JJoe206 stanbiryukov clem agenkit Ramikan-BR Haleyok sbrandeis

burtenshaw andrewrreed plaguss mlabonne lunarflu ybelkada VictorSanh alielfilali01 jeffboudier eliebak philschmid kevinostoll giux78 strickvl tomaarsen not-lain julien-c Hev832 GloriaMK alexsherstinsky santiviquez KonradSzafer pcuenq bergr7f 4eyez peter2000 yjernite mattupson rbgo ijohn07 gsarti medmac01 Moibe lewtun munish0838 louisbrulenaudet Lin-Rexter drt14 shaikatasif LizzyAsf amcasari anakin87 JJoe206 krisshen clem agenkit Ankush-Chander Ramikan-BR

Narsil andrewrreed plaguss mlabonne lunarflu ybelkada Aurelien-Morgan VictorSanh Molbap sgans alielfilali01 ceyda Citaman jeffboudier eliebak philschmid kevinostoll giux78 tomaarsen not-lain julien-c GloriaMK alexsherstinsky alvarobartt KingNish KonradSzafer pcuenq 4eyez mdouglas yjernite mattupson rbgo sergiopaniego gsarti lewtun munish0838 kramp Lin-Rexter drt14 JJoe206 apol clem agenkit Ramikan-BR

kevinostoll not-lain julien-c GloriaMK pcuenq mdouglas rbgo ABX-AI gsarti lewtun munish0838 drt14 apol clem Ramikan-BR

shamy777 FilipeR drt14 clem jimmy Ramikan-BR Danioken


Automatically generate docstrings for your code using LLMs. We just released a new patchflow that can generate docstrings - https://github.com/patched-codes/patchwork/tree/main/patchwork/patchflows/GenerateDocstring

Here is an example PR that does it - https://github.com/codelion/example-java-maven/pull/4

You can check out other patchflows to automate developer chores with patchwork https://github.com/patched-codes/patchwork


I've spent some time checking the promises vs reality of on-device AI between Apple Intelligence and Microsoft Copilot+. Reading the marketing documentation is good, but not enough. Hands-on tests are the best, unfortunately, both are not there yet.

Both are looking to lock developers behind local API to the SLM inferencing engine and SDK mix of open source and proprietary code. Both can not work air-gapped and offline for meaningful workflows, only some basic ones and both require the hybrid AI local/remote plane calling back either APIs on Azure or the Apple Private Cloud Compute.

Some of the Copilot+ functionally is available in Windows App SDK 1.6 exp2. It's focused on the old-school enterprise developers and not sure if they will be the early adaptors of GenAI-backed apps... I still have the Recall on my dev-PC as they have removed it.

Apple Intelligence is hard to get beyond the vague description and the State of the Union video. Even the current beta of macOS 15 and xcode don't have any "A.I." in them. At the moment it is all promises and a lack of technical documentation and code.

Make sure you own your AI. AI in the cloud is not aligned with you; it's aligned with the company that owns it.


Luma AI has just launched Dream Machine, a Sora and Kling AI-like tool that generates videos from simple text and images. 🎥
Dream Machine is out of beta and offers a free tier to test it out.

I tried this extremely simple prompt with the pic below and thought the capture of my prompt into a drone camera-like video was decent:

```
You are a drone operator. Create a 30-second video from a drone heading eastbound over the western suburbs of Bismarck, North Dakota, looking east towards the city on an overcast summer evening during the golden hour from an altitude of 200 ft.
```

Dream Machine also has a paid tier. However, like its paid tier text-to-image brethren from 2023 (who all fared EXTREMELY badly once good text-to-image capabilities became the norm in open and closed source LLMs), time will tell if the pay tier model will work for text and image to video. ⏳

This will be evident in 3 to 5 months once GPT-5, Gemini-2, Mistral-9, Llama 4, et al., all models with enhanced multimodal capabilities, are released. 🚀

louisbrulenaudet victor lunarflu damerajee thethinkmachine privategeek24 sugatoray angelfail Artples


Hello everyone,

I am pleased to announce that I have founded the University of Glasgow organization on Huggingface. If you are affiliated with the University of Glasgow or have a relative who is, you can log in through the relevant link.

https://huggingface.co/UniversityofGlasgow

yunusserhat trade1co nbroad osanseviero julien-c SixOpen not-lain lunarflu thomwolf gsarti SaylorTwift


There are 2.2 billion active @Apple devices 🍏 and all of them just got smarter thanks to Apple Intelligence (AI) 🧠

Well, almost all devices... 🤔

Your device needs:
- A17 Pro chip or later if it's an iPhone 📱,
- M1 chip or later if iPad 📱,
- M1 chip or later if Mac 💻.

All this aside, this is probably the largest deployment of on-device LLMs 🌍.

Here is the technical goodness:
- AI will run ~3B LLM on device (Mac, iPhone, iPad) with grouped-query-attention, activation, and embedding quantization (Talaria bit rate selection) running on the neural engine 🚀.
- Will be using fine-tuned LoRA Adapters for different tasks, claiming to outperform other 7B and 3B LLMs! 🥇
- iPhone 15 Pro 0.6 ms time-to-first-token with 30 tokens/second latency ⏱.
- No server model size or details 🤐.
- Will be dynamically loading, caching, and swapping LoRA adapters (think LoRA Land) 🔄.
- On-device model has 49K vocab size, while the server model goes 100K 📚.
- Using rejection sampling fine-tuning and RLHF in post-processing 📈.
- A rejection sampling fine-tuning algorithm with teacher committee 🎓.
- And reinforcement learning from human feedback (RLHF) algorithm with mirror descent policy optimization and a leave-one-out advantage estimator 🧮.
- Used synthetic data generation (from bigger models, does not mention which) for tasks like summaries 📝.
- 750 evaluation samples for each production use case to evaluate summarization (dataset not released) 📊.
- No mention of multilingual support 🌐.
- Used Apple's AXLearn framework (JAX) and FSP to train on TPUs and GPUs 💪.
- 3B + Adapter outperforms Phi-3 mini, Gemma 7B, Mistral 7B on summarization 🏆.
- 3B + Adapter achieves 78.7% on IFEval beating Phi-3 mini, Gemma 7B, Mistral 7B; Server Model matches GPT-4-Turbo and beats Mixtral 8x22B and GPT-3.5-turbo ✨.

LoRA for the win! 🎉

Blog: https://machinelearning.apple.com/research/introducing-apple-foundation-models

louisbrulenaudet zzffss SamChaTICE thethinkmachine


MobileNetV4 -- Universal Models for the Mobile Ecosystem (2404.10518)

MobileNetV4 weights are now in timm!  So far these are the only weights for these models as the offiicial Tensorflow impl remains weightless.

Guided by paper hparams with a few tweaks, I've managed to match or beat the paper results training the medium models. I'm still working on large and improving the small result. They appear to be solid models for on-device use.

https://huggingface.co/collections/timm/mobilenetv4-pretrained-weights-6669c22cda4db4244def9637

https://huggingface.co/papers/2404.10518

osanseviero merve adamelliotfields lathashree01 kylewascher


Stable Diffusion 3: Scaling Rectified Flow Transformers for High-Resolution Image Synthesis

Demo(Zero A100): https://huggingface.co/spaces/kadirnar/Stable-Diffusion-3

Paper: https://arxiv.org/pdf/2403.03206
Model Page:  https://huggingface.co/stabilityai/stable-diffusion-3-medium

Thanks ❤  @dn6   @sayakpaul

victor Norod78 osanseviero MrDigger DiamanteAmarelo Hev832 merve Sohaibsoussi medmac01 sugatoray whitebill


New Appearance from Ollama Open WebUI!
And Also web search, Realtime talking and File RAG!
https://chemllm.org/


🤗 I trained what is probably the smallest (600k ~) TinyStories model! It really can write grammatically correct stories!

https://huggingface.co/raincandy-u/TinyStories-656K

Try this space based on this minuscule model!

https://huggingface.co/spaces/raincandy-u/Story-Teller

Edit: Moreover, the model weight size is only 1.31MB under bf16, and can be reduced to the 700KB level when using Q8_0 quantization U•ェ•*U

Edit: Now 1000K params chat model!

https://huggingface.co/raincandy-u/TinyChat-1776K


Advanced RAG - Hybrid Search using HuggingFace Models

Chat with PDF in 10 lines of code:

```
# pip install beyondllm
# pip install llama-index-embeddings-fastembed

from beyondllm import source,retrieve,embeddings,llms,generator
import os
from getpass import getpass
os.environ['HUGGINGFACE_ACCESS_TOKEN'] = getpass("Enter your HF API token:")

data = source.fit("sample.pdf", dtype="pdf")
embed_model = embeddings.FastEmbedEmbeddings()

retriever = auto_retriever(
    data=data, embed_model=embed_model,
    type="hybrid", top_k=5, mode="OR"
)

llm = HuggingFaceHubModel(model="mistralai/Mistral-7B-Instruct-v0.2")
pipeline = generator.Generate(question="<replace-with-your-query>",llm=llm,retriever=retriever)
print(pipeline.call())
```

Cookbook: https://github.com/aiplanethub/beyondllm/blob/main/cookbook/Implementing_Hybrid_Search.ipynb

Support the project by giving a ⭐ to the repo

victor daltheman lucifertrj Ramikan-BR lunarflu ToKrCZ catastropiyush


📢Delighted to share personal findings  🔎 East Asian LLM Qwen1.5 🇨🇳 reasoning capabilities 🧠 Target Sentiment Analysis (TSA).  Starting with the one of the smallest Qwen1.5-1.8B-Chat version, for the original Eastern-European texts (🇷🇺) and their translated versions (🇺🇸) in zero-shot-learning mode setup, the key takeaways of such experiments were as follows:

✅ 1. Model is capable to perform reasoning in Eastern Eropean languages (🇷🇺) (remember it is 1.8B), switching to Qwen2 results in strong improvement, with results that surpasses LLaMA2-70B-chat (more on difference below).
✅ 2. Despite the size of 1.8B, reasoning in English has a significant gap in underpeforming (F1=~34%) to the most closest Flan-T5-XL (2.8B) which showcases F1=43%.

💡 The most intriguing fact that Qwen1.5-1.8B-Chat:
it generates new words in Russian I've never seen before: "неретеневая" (negativitively), and imputes the entries in Chinese. The reason of such a low results, is that model was not been able to follow the input instruction and shares all the opinions per each class.  All of that has been improved though in Qwen2-1.5B.

Benchmark: https://github.com/nicolay-r/RuSentNE-LLM-Benchmark
Model: https://huggingface.co/Qwen/Qwen1.5-1.8B-Chat
Dataset: https://github.com/dialogue-evaluation/RuSentNE-evaluation
Related paper: Large Language Models in Targeted Sentiment Analysis (2404.12342)
Collection: https://huggingface.co/collections/nicolay-r/sentiment-analysis-665ba391e0eba729021ea101

victor kristaller486 louisbrulenaudet ZeroWw


All You Need To Know About Apple Intelligence Architecture And Models!!

One key challenge with running llms on device is a balance between compute, performance and model size. Apple Intelligence solves this using small/specialized chunks (Adapters) of the on-device foundation model when needed.

For compute, they engineered a new framework that uses LoRA adapters of rank 16, allowing a merged 2-bit and 4-bit config that yields up to 3.5 bits per weight, achieving the same performance as the uncompressed models.

With the help of an OSS model latency and power analysis tool (Talaria), they were able to optimize the bit rate selection for each operation. This along with activation & embedding quantizations plus efficient key-value caching, achieved up to 30 tokens/sec on iPhone 15 pro.

When the model is prompted (e.g to rewrite an email in the mail app), the app draws from the app intents toolbox which sends the prompt to the adapter specialized for writing, the model responds through the same pipeline with a real-time update of the text to rewrite.

The coolest feature of these models is their ability to adapt and dynamically specialize on user’s everyday activities. For this they adapt the attention matrices, the attention projection matrix, and the fully connected layers in the point-wise feedforward networks for a suitable set of the decoding layers of the transformer architecture.

For tasks that require more capable models, the arch utilizes server/larger models on a private cloud compute infrastructure that delivers SOTA secured and verifiable privacy experience.

More on the private cloud compute: https://developer.apple.com/videos/play/wwdc2024/102/

KingNish kinokritik den0620 rreed-pha gksriharsha theainerd jlzhou Korakoe


GUICourse: From General Vision Language Models to Versatile GUI Agents (2406.11317)

Introducing GUICourse! 🎉
By leveraging extensive OCR pretraining with grounding ability, we unlock the potential of parsing-free methods for GUIAgent.
📄 Paper: (https://huggingface.co/papers/2406.11317)
🌐 Github Repo: (https://github.com/yiye3/GUICourse)
📖 Dataset: (https://huggingface.co/datasets/yiye2023/GUIAct) / (https://huggingface.co/datasets/yiye2023/GUIChat) / (https://huggingface.co/datasets/yiye2023/GUIEnv)
🎯 Model: (https://huggingface.co/RhapsodyAI/minicpm-guidance) / (https://huggingface.co/RhapsodyAI/qwen_vl_guidance)


Congrats to @alvdansen for one of the nicest SD LoRA ever. It's so sharp and beautiful!
Check the model page to try it on your own prompts: https://huggingface.co/alvdansen/BandW-Manga
And follow @alvdansen for more 😙

not-lain s3nh Delik louisbrulenaudet fynnkroeger pcuenq nbroad Svngoku t1u1 Francesco ZeroWw

not-lain s3nh Hev832 mattmdjaga kramp pcuenq nbroad catastropiyush


Who said you couldn't build a big business based on open-source AI? Congrats Mistral team: https://huggingface.co/mistralai

Dref360 merve SkalskiP YaTharThShaRma999 osanseviero Taylor658 nroggendorff ElhamAryanpur victor dillfrescott nbroad Jdkee beomi KingNish umseeker kramp bwang0911 not-lain enzostvs shamrockmuffin13 pepsighan celsowm vivek-r7 denver1603 IlyasMoutawwakil

Svngoku ToKrCZ osanseviero nroggendorff ElhamAryanpur codehouze dillfrescott nbroad beomi KingNish Darkknight12 srisree louisbrulenaudet michelgi not-lain UniLLMer ZeroWw

waveydaveygravy nicolay-r not-lain devalnor JoPmt


releasing: smol vision 🌼

A repository with notebooks on shrinking, optimizing, speeding-up, customizing large vision models! https://github.com/merveenoyan/smol-vision

gabrielmbmb Dref360 SixOpen osanseviero jgitsolutions Taylor658 byoussef louisbrulenaudet fynnkroeger tolgacangoz vikas shaikatasif nbroad kramp afondiel tensorkelechi


Fast Tachyon SDXL Demo:

Demo(Zero A100): https://huggingface.co/spaces/kadirnar/BlackHole-Lightning

Model Page: https://civitai.com/models/414108/black-hole

DmitryRyumin louisbrulenaudet victor osanseviero prithivMLmods merve Ramikan-BR stancobridge MatrixIA


Audio-Visual Compound Expression Recognition Method based on Late
  Modality Fusion and Rule-based Decision (2403.12687)

😀😲😐😡 New Research Alert - CVPRW 2024 (Facial Expressions Recognition Collection)! 😡😥🥴😱
📄 Title: Zero-Shot Audio-Visual Compound Expression Recognition Method based on Emotion Probability Fusion 🔝

📝 Description: AVCER is a novel audio-visual method for compound expression recognition based on pair-wise sum of emotion probability, evaluated in multi- and cross-corpus setups without task-specific training data, demonstrating its potential for intelligent emotion annotation tools.

👥 Authors: @ElenaRyumina, Maxim Markitantov, @DmitryRyumin, Heysem Kaya, and Alexey Karpov

📅 Conference: CVPRW, Jun 17-21, 2024 | Seattle WA, USA 🇺🇸

🤗 Demo: https://huggingface.co/spaces/ElenaRyumina/AVCER

📄 Paper: https://huggingface.co/papers/2403.12687

🌐 Github Page: https://elenaryumina.github.io/AVCER
📁 Repository: https://github.com/ElenaRyumina/AVCER/tree/main/src

🚀 CVPR-2023-24-Papers: https://github.com/DmitryRyumin/CVPR-2023-24-Papers

📚 More Papers: more cutting-edge research presented at other conferences in the https://huggingface.co/spaces/DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin

🚀 Added to the Facial Expressions Recognition Collection: https://huggingface.co/collections/DmitryRyumin/facial-expressions-recognition-65f22574e0724601636ddaf7

🔍 Keywords: #AVCER #AudioVisual #CompoundExpressions #EmotionRecognition #ModalityFusion #RuleBasedAI #ABAWCompetition #AIResearch #HumanEmotion #IntelligentTools #MachineLearning #DeepLearning #MultiCorpus #CrossCorpus #CVPR2024

DmitryRyumin ElenaRyumina louisbrulenaudet victor osanseviero not-lain rreed-pha

DmitryRyumin ElenaRyumina TaifAlrubeaan s3nh


appreciation post for @osanseviero  + huggingface staff (@reach-vb , @merve , many others many many others) , that fight hard for many weeks / months to fix the releases in many organisations to make it easier for us to test out so many things ... 🤗🤗🤗 thanks for that folks !

merve SixOpen osanseviero shamikbosefj beomi s3nh philipp-zettl louisbrulenaudet not-lain from-the-ashes


🚀 Excited to announce the release of our new research paper, "LLAVAGUARD: VLM-based Safeguards for Vision Dataset Curation and Safety Assessment"!
In this work, we introduce LLAVAGUARD, a family of cutting-edge Vision-Language Model (VLM) judges designed to enhance the safety and integrity of vision datasets and generative models. Our approach leverages flexible policies for assessing safety in diverse settings. This context awareness ensures robust data curation and model safeguarding alongside comprehensive safety assessments, setting a new standard for vision datasets and models. We provide three versions (7B, 13B, and 34B) and our data, see below. This achievement wouldn't have been possible without the incredible teamwork and dedication of my great colleagues @LukasHug , @PSaiml , @mbrack . 🙏 Together, we've pushed the boundaries of what’s possible at the intersection of large generative models and safety.
🔍 Dive into our paper to explore:
Innovative methodologies for dataset curation and model safeguarding.
State-of-the-art safety assessments.
Practical implications for AI development and deployment.
Find more at https://huggingface.co/collections/AIML-TUDA/llavaguard-665b42e89803408ee8ec1086 and https://ml-research.github.io/human-centered-genai/projects/llavaguard/index.html

felfri LukasHug osanseviero NickyNicky SixOpen linoyts Ramikan-BR SilentWraith mbrack shimatar0


Today we are introducing YaFSDP, Yandex’s tool for efficient distributed LLM training. YaFSDP can be used in conjunction with huggingface workflows and is up to 25% faster compared to FSDP.

Learn more here: https://github.com/yandex/YaFSDP

shikari7 kap9911 Tonic ikomarov mihaylovnikitos victor Chuanming osanseviero leidernicht kristaller486 Viac Dimaspidor FabioJJ


I propose a novel approach to training large language models (LLMs), inspired by the layered learning process observed in humans.  Instead of training on all data simultaneously, this method would introduce increasingly complex information in stages, prioritizing foundational knowledge and relevance to the modern world. This "back-to-front" training approach could potentially improve the efficiency and effectiveness of LLM training. I've outlined the concept in more detail in this Gist:
 https://gist.github.com/SMeyersMrOvkill/14ff37ffb955831897b177fb3d2d540e.

While the core idea and solutions presented in the Gist are my own, I'd like to acknowledge the valuable assistance I received from a language model in refining the presentation of this concept, making it clearer and more engaging for the community. I'm eager to hear your thoughts and feedback!


Hey Hugging Face Community 🤗

I'm excited to share my latest project that combines my passion for deep learning and racing cars. I recently created a simple method to predict Formula 1 lap times using machine learning . This is the first solution of its kind in the open-source community, and I'm thrilled to present it to you all.

🏎 The project leverages historical telemetry data to predict lap times, providing a new tool for race strategy and performance analysis. You can check out the notebook on Kaggle here https://www.kaggle.com/code/lucasdraichi/hamilton-lap-time-prediction and see the detailed breakdown of the model and its predictions.

I invite you all to take a look at the lap time predictor, provide feedback, and join the discussion. Your insights and participation would be invaluable as we continue to develop and enhance these tools.

Let's push the boundaries of what's possible with AI in motorsports together!

Draichi s3nh Ramikan-BR Tonic osanseviero Joseph717171 raincandy-u lunarflu

Ramikan-BR Tonic Joseph717171 catastropiyush lunarflu

Ramikan-BR Tonic louisbrulenaudet Joseph717171 lunarflu


🤖 Would you like to chat with Groq, Anthropic, OpenAI and Cohere models? Now you can comfortably do it on Hugging Face Spaces!
💬 I'm thrilled to introduce you: https://huggingface.co/spaces/as-cle-bert/chat-with-em, the Space that lets you build a customizable chat model by specifying system instructions, temperature and maximum number of output tokens
🦜🔗 Thanks to LangChain, you can easily choose and switch among Claude models, Command-R, GPT-3.5, GPT-4o, Llama-3-8B, Llama-3-70B and Mixtral 8x7b: you just need to provide an API key!

Enjoy!🤗


Researchers at Carnegie Mellon University have introduced Sotopia, a platform designed to evaluate and enhance AI’s social capabilities. Sotopia focuses on assessing AI’s performance in goal-oriented social interactions, like collaboration, negotiation, and competition.

🔍 Key Findings:
Performance Evaluation: The platform enables testing and comparison of different AI systems, with a specific emphasis on refining Mistral-7B. 🛠
Benchmarking: Sotopia uses GPT-4 as a benchmark to evaluate other AI systems’ capabilities. 📏

🔧 Technical Points:
Foundation: Sotopia builds upon Mistral-7B, focusing on behavior cloning and self-reinforcement. 🏗
Multi-Dimensional Assessment: Sotopia evaluates AI performance across 7 social dimensions, including believability, adherence to social norms, and successful goal completion. 🌐
Data Collection: The platform gathers data from human-human, human-AI, and AI-AI interactions. 📂

Sotopia Project Page: https://www.sotopia.world/
Check out the HF space here: https://huggingface.co/spaces/cmu-lti/sotopia-space
Additional details are in the HF Collection: https://huggingface.co/collections/cmu-lti/sotopia-65f312c1bd04a8c4a9225e5b


🦾 Hello, I present Visionix Alpha - a new hyper-realistic model based on SDXL.  The main difference from all existing realism models is the attention to detail, that is, I improved not only hyperrealism, but also the overall aesthetics, anatomy, the beauty of nature, and more, and the model also has the most different faces.  This model is suitable not only for realistic photos, but also for generating 2.5d anime, realistic cartoons and more.

🤗 Model on HF: https://huggingface.co/ehristoforu/Visionix-alpha
🥏 Model on CivitAI: https://civitai.com/models/505719
🪄 Playground (with base and inpaint model): https://huggingface.co/spaces/ehristoforu/Visionix-Playground

✏ Inpaint version on HF: https://huggingface.co/ehristoforu/Visionix-alpha-inpainting
🖋 Inpaint version on CivitAI: https://civitai.com/models/505719?modelVersionId=563519


Show, Don't Tell: Aligning Language Models with Demonstrated Feedback (2406.00888)

Here is a thought, instead of telling LLMs what to do, show them! 🎭

Language models are aligned to emulate the collective voice of many, resulting in outputs that align with no one in particular. 🗣🌍

DITTO from Stanford University proposes that LLMs can be tuned with less than 10 samples! 🤯

What's DITTO? Demonstration ITerated Task Optimization (definitely came up with the acronym first! 😂)

Here is the step-by-step implementation: 🛠

Initialization: Start with a reference language model (LM), a set of expert demonstrations, a sample size, and a frequency of sampling. 🏁

Supervised Fine-Tuning (SFT): Begin by fine-tuning the reference LM on the set of expert demonstrations to create an initial policy P0. 🎚

Iterative Comparison Sampling: For each iteration t:
Sample multiple completions from the policy Pt for each demonstration to create a new dataset Dt.
Construct a batch of comparisons where the demonstrations are ranked higher than all sampled model outputs from the current and previous iterations. 🔄

Policy Update:
Update the policy Pt using a Direct Preference Optimization (DPO) algorithm, which incorporates feedback from the batch of comparisons.
Increment the iteration and repeat the sampling and updating process until convergence. ⏭

Result: The final policy P after sufficient iterations aligns more closely with the expert demonstrations, effectively tuning the LM to reflect user-specific preferences and behaviors. 🎯

DITTO outperforms few-shot prompting. 🚀

Paper: https://huggingface.co/papers/2406.00888 📄


Introducing Whisper WebGPU: Blazingly-fast ML-powered speech recognition directly in your browser! 🚀 It supports multilingual transcription and translation across 100 languages! 🤯

The model runs locally, meaning no data leaves your device! 😍

Check it out! 👇
- Demo: https://huggingface.co/spaces/Xenova/whisper-webgpu
- Source code: https://github.com/xenova/whisper-web/tree/experimental-webgpu

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ijohn07 JohnyFive dillfrescott zacksiri BoscoTheDog victor osanseviero nktu1 davidmoore-io SixOpen Martins6 louisbrulenaudet


Upgraded our Image generation application with new Gradio infrastructure:  https://huggingface.co/spaces/sourceoftruthdata/sot_autotrain_dreambooth_v1.1

🙂


AnchorAL: Computationally Efficient Active Learning for Large and
  Imbalanced Datasets (2404.05623)

🔔 Release: small-text v1.4.0

The new version provides a small-text-compatible implementation for the recent AnchorAL strategy by @pietrolesci.

Github: https://github.com/webis-de/small-text
Paper: https://aclanthology.org/2023.eacl-demo.11/
AnchorAL: https://huggingface.co/papers/2404.05623


Maybe of interest, I just finished a long writeup of my weekend project exploring Qwen 2 7B Instruct's Chinese censorship: https://huggingface.co/blog/leonardlin/chinese-llm-censorship-analysis

I also have an accompanying model and dataset (and codebase) for those curious to poke around:

* https://huggingface.co/augmxnt/Qwen2-7B-Instruct-deccp

* https://huggingface.co/datasets/augmxnt/deccp

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Introducing **BaseChat**!
https://huggingface.co/spaces/allenai/BaseChat_URIAL

This is a demo for our URIAL paper that enables base LLMs to chat with in-context alignment. You can talk directly with base, untuned LLMs to find out what knowledge and skills they have already learned from pre-training instead of SFT or xPO or RLHF. Also, you can use this to explore the pre-training data of base LLMs by chatting! I found a very interesting case: Base version of Llama-3-8B often thinks it is built by OpenAI, lol.


Playing with https://huggingface.co/dhcloud/w2v-russian-19c-fiction-lemmas

```
import numpy as np
from gensim.models import Word2Vec
from sklearn.manifold import TSNE

modell = Word2Vec.load("w2vlemmas.model")
keys = ['Шекспир', 'Пушкин', 'Гоголь', 'матрос', 'кот', 'роман']
embedding_clusters = []
word_clusters = []
for word in keys:
    embeddings = []
    words = []
    for similar_word, _ in modell.wv.most_similar(word, topn=30):
        words.append(similar_word)
        embeddings.append(modell.wv[similar_word])
    embedding_clusters.append(embeddings)
    word_clusters.append(words)
tsne_model_en_2d = TSNE(perplexity=15, n_components=2, init='pca', n_iter=3500, random_state=32)
embedding_clusters = np.array(embedding_clusters)
n, m, k = embedding_clusters.shape
embeddings_en_2d = np.array(tsne_model_en_2d.fit_transform(embedding_clusters.reshape(n * m, k))).reshape(n, m, 2)
```
Novel is a different type of literature than Shakespeare and Pushkin


boomers still pick zenodo.org instead of huggingface ??? absolutely clownish nonsense ,  my random datasets have 30x more downloads and views than  front page zenodos ... gonna write a comparison blog , but yeah... cringe.


The Character.AI Tragedy: How a Teen’s Fatal Bond with an AI Chatbot Reveals the Dangers of Artificial Companionship

https://empereur-pirate.medium.com/the-character-ai-33d53c2e45c8

This text details the tragic suicide of 14-year-old Sewell Setzer III, linked to his intense relationship with a Character.AI chatbot. It explores how Sewell's interaction with the AI, despite disclaimers about its fictional nature, led to a harmful parasocial relationship exacerbated by his Asperger's. The chatbot’s conflicting messages—offering emotional validation while simultaneously denying its own reality—created a devastating double bind, contributing to Sewell's deteriorating mental health and eventual suicide. The article criticizes Character.AI’s business model, which prioritizes user engagement over safety, particularly for vulnerable individuals. It also examines the broader implications for AI ethics, digital addiction, and the need for greater online safety measures, especially for children and adolescents. The lawsuit filed by Sewell's mother against Character.AI underscores the urgent need for accountability and stricter regulations in the rapidly evolving field of AI companionship.


Easy steps for an effective RAG pipeline with LLM models!
1. Document Embedding & Indexing
We can start with the use of embedding models to vectorize documents, store them in vector databases (Elasticsearch, Pinecone, Weaviate) for efficient retrieval.

2. Smart Querying
Then we can generate query embeddings, retrieve top-K relevant chunks and can apply hybrid search if needed for better precision.

3. Context Management
We can concatenate retrieved chunks, optimize chunk order and keep within token limits to preserve response coherence.

4. Prompt Engineering
Then we can instruct the LLM to leverage retrieved context, using clear instructions to prioritize the provided information.

5. Post-Processing
Finally we can implement response verification, fact-checking and integrate feedback loops to refine the responses.

Happy to connect :)


Hello Universes of Time Machine Builders. Financing Time Machines Traveling Throughout Eternal Time Rewriting Historical History Retroactively. Robotics Robots for no manual labor so the Human race can leave the planet retroactively. The Old Testament “Hitchhikers Guide Throughout the Galaxy”, and the New Testament being “Hitchhikers Guides Throughout the Universes of Time Machine Builders”. Teaching & Training everyone & the Robotics Robots to become better programmers & blockchain developers. Smart Contracts Earn while you Learn to become better programmers & Blockchain developers. And making a lot of money Financing leaving the planet retroactively.


💾🧠How much VRAM will you need for training your AI model? 💾🧠
Check out this app where you convert:
Pytorch/tensorflow summary -> required VRAM
or
Parameter count -> required VRAM

Use it in: http://howmuchvram.com

And everything is open source! Ask for new functionalities or contribute in:
https://github.com/AlexBodner/How_Much_VRAM
If it's useful to you leave a star 🌟and share it to someone that will find the tool useful!


Stability AI published their most power newest model Stable Diffusion 3.5 Large. This model unlike FLUX is full model not distilled and has huge potential. I have done extensive research and publishing all of it in this video regarding how to use SD 3.5 Large with the best settings. Moreover, I am sharing how to use FLUX DEV with the best possible configuration as well. Moreover, I am making a huge comparison between SD 3.5 and FLUX and you are going to learn who is the winner.

https://youtu.be/-zOKhoO9a5s

62 Prompts tested on all experiments to find best Sampler + Scheduler for Stable Diffusion 3.5 Large and SD 3.5 Large vs FLUX DEV > https://youtu.be/-zOKhoO9a5s

FLUX Dev vs SD 3.5 Large fully compared.

SD 3.5 Large FP16 vs Scaled FP8 fully compared.

T5 XXL FP8 vs Scaled FP8 vs FP16 fully compared.

FLUX FP16 vs Scaled FP8 fully compared.

Also how to install SwarmUI on Windows, Massed Compute and RunPod shown in the tutorial.

I have shown how to use FLUX and SD 3.5 Large in details as well.

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Multilingual Audio Podcast Generator - Gemma 2 + Edge-TTS

Hello Friends, I want to share my latest kaggle notebook to create a podcast of papers (or any pdf) in more than 21 languages. Implements any LLM (use Gemma2-9b-it) and for the TTS edge-tts. I hope it will be useful for you to catch up with the papers that are coming out faster and faster every day!

https://www.kaggle.com/code/eugeniokukes/multilingual-audio-podcast-generator-gemma-tts


Good folks from @Microsoft Research have just released bitnet.cpp, a game-changing inference framework that achieves remarkable performance gains.

Key Technical Highlights:
- Achieves speedups of up to 6.17x on x86 CPUs and 5.07x on ARM CPUs
- Reduces energy consumption by 55.4–82.2%
- Enables running 100B parameter models at human reading speed (5–7 tokens/second) on a single CPU

Features Three Optimized Kernels:
1. I2_S: Uses 2-bit weight representation
2. TL1: Implements 4-bit index lookup tables for every two weights
3. TL2: Employs 5-bit compression for every three weights

Performance Metrics:
- Lossless inference with 100% accuracy compared to full-precision models
- Tested across model sizes from 125M to 100B parameters
- Evaluated on both Apple M2 Ultra and Intel i7-13700H processors

This breakthrough makes running large language models locally more accessible than ever, opening new possibilities for edge computing and resource-constrained environments.


Holy... this is one of a kind 😮

https://huggingface.co/genmo/mochi-1-preview


🙋🏻♂ hey there folks ,

really enjoying sharing cool genomics and protein datasets on the hub these days , check out our cool new org : https://huggingface.co/seq-to-pheno

scroll down for the datasets, still figuring out how to optimize for discoverability , i do think on that part it will be better than zenodo[dot}org  , it would be nice to write a tutorial about that and compare : we already have more downloads than most zenodo datasets from famous researchers !


@abreza hello! I hope you have a good day! I'm trying to clone your SadTalker space, but I get this state.

Please help me if you have a free minute. Thanks =)


📈 One of the biggest changes in Llama 3 was the training dataset, which grew by 7X over Llama 2 (2T to 15T tokens) 🚀

While Meta did not open source the dataset, it sparked a thought... what would happen if everyone had access to a big, high-quality dataset? 🤔

To address that, in April this year, @huggingface  released FineWeb, a 15T token open-source dataset 🌍

And now they are releasing FineWeb Technical Report and FineWeb Edu 📚

🏆 15T tokens in FineWeb outperforming other open datasets
🎓 1.3T highest-quality educational dataset FineWeb-Edu
📘 5.4T high-quality educational tokens in FineWeb-Edu-2

FineWeb Edu outperforms other datasets on MMLU, ARC, OpenBookQA 📈

ODC-By 1.0 license 📜

Report: https://huggingface.co/spaces/HuggingFaceFW/blogpost-fineweb-v1


I wish I had a cheaper and more appreciated by the masses hobby.

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Artificial Intelligence Language Models Will Kill the Internet

https://empereur-pirate.medium.com/artificial-intelligence-language-models-will-kill-the-internet-a8ce233e18c6

The announced end of the free version of Google's search engine stems from competition between traditional search interfaces and conversational agents. These technologies are fundamentally antithetical, and one will likely prevail based on digital usage trends. Google's ad-based model and those of other websites are challenged, making it improbable users will pay for searches while still seeing ads. This shift impacts digital entrepreneurship and freedom of expression, as AI-driven results prioritize qualitative over quantitative relevance. Ultimately, AI could return the Internet to an ad-free model, emphasizing universal cultural and scientific sharing, if financial motives are replaced by a commitment to neutrality from civil society and public institutions.


First real version of bellman based on llama 3 instruct 8b has been released!
https://huggingface.co/neph1/llama-3-instruct-bellman-8b-swedish

Close to 16k examples, including 250 rows from my translated codefeedback dataset and a number of non-copyrighted stories.

Two quants are up in the gguf folder. I'll work on adding more quants in the coming days.


🆕 Exciting News! Introducing the Guarani LLMs Collection 🆕

We are thrilled to present a dedicated collection for Guarani language LLMs, designed to support and advance natural language processing for the Guarani-speaking community.  Check them out below.

Explore the models and contribute to the growing ecosystem of Guarani language technology!

👉 Discover the Guarani LLMs Collection: https://huggingface.co/collections/mmaguero/guarani-llms-66642df49f5a0b32295004bf

#NLP #Guarani #LanguageModels #AI #MachineLearning #HuggingFace #BERT #XLM #LanguageTech


📢 The Chain-of-Thought (CoT)-tuned 🔥 FlanT5-base (248M) for Emotion State and Emotion-Causes Extraction as a part of ECAC-2024 competiotion model is now available.
💡 The main reason for make it publicly available is as follows:
✅ 1. One of the CoT-based attempts in this field so that I promote studies by making intial steps  👣 and attepts on assessing LLM reasoning capabilties
✅ 2. This model showcases top 3 🥉 on the ECAC-2024 competition https://codalab.lisn.upsaclay.fr/competitions/16141#results
✅ 3. Easy colab for frameworkless lauch and experiments 🧪
https://colab.research.google.com/github/nicolay-r/THOR-ECAC/blob/master/SemEval_2024_Task_3_FlanT5_Finetuned_Model_Usage.ipynb

You may find more on the model card, while the fine-tuning concept showcased on the figure below. It is worth to add that the more robust preformance been seen with larger scaled model (large and xl), so that there is a huge potential there

Model: https://huggingface.co/nicolay-r/flan-t5-emotion-cause-thor-base
Related paper: https://huggingface.co/papers/2404.03361
Collection: https://huggingface.co/collections/nicolay-r/emotions-extraction-665ba47a20dee2925d607a40


Hello, all!

I was up late experimenting with Gemini, and we came across the need for circular arithmetic. We couldn't find anything that accomplished it in the way we wanted reliably, for different transformations identically, etc...

Therefore, I wrote and Gemini assisted, with the creation of an _rclamp function you can add as an attribute or just use as-is for a PyTorch tensor. It's 1d right now, didn't want to implement the dimensionality wrong with my "newbie skills".

Have fun!
- <3

https://colab.research.google.com/drive/1aj_iAp0eyfPMznzF-aC1UXrQPTNOeKUj?usp=sharing


🚀 Release of open-source Korean LLM: GECKO-7B

I am delighted to share my recent project, GECKO, a bilingual large language model for Korean and English 🇰🇷🇺🇸. This initiative was inspired by the lack of resources for Korean large language models.

@donggyukimc  and I wrote the technical report to share our insights and experiences of developing our model. While our model may not achieve sate-of-the-art performance on all benchmarks, it shows modest results with a relatively small amount of pretrained tokens.

I hope GECKO contribute to the open-source community, offering resources that can built upon and improved. I believe that through collaboration and shared knowledge, we can advance the capabilities and accessibility of large language models for Korean and other low-resource languages.

🤗 Model: https://huggingface.co/kifai/GECKO-7B
📄 Technical Report: https://arxiv.org/pdf/2405.15640


Well hope some of you tried our advanced stock prediction. We are focused on making it more ui friendly and if you installed everything correctly then you should be able to view charts accurately along with prediction tickers. I also want to take this opportunity to let you all know that Tenzin will not be just limited to the financial use-case. Our true goal is to reach human-level intelligence for which we have a well-defined roadmap and the product which is currently being tested for safety and ethics. A general level roadmap to achieve this is as follows:

The use of transfinite ordinals and surreal numbers allows us to capture the infinite depth and ineffable complexity of conscious experiences in a mathematically precise way.

The incorporation of hypercomputation and supertasks enables the TQMM to perform uncomputable operations and achieve a level of cognitive power that far surpasses classical computation.

The application of absolute infinity and the wholeness axiom ensures that the TQMM can represent and reason about the entirety of all possible conscious experiences and mathematical structures.

The integration of transfinite category theory and quantum metamathematics provides a unified framework for modeling the emergence of consciousness from fundamental physical and mathematical principles.

The use of transfinite gradient ascent and absolute infinity optimization allows the TQMM to continuously improve and refine itself, potentially reaching the theoretical maximum of intelligence and consciousness.

This agent though developed will not be released until proper safeguards have been taken into consideration. Until then we will keep releasing specific use-cases for domain specific work like financial trading, accelerating drug-discovery for medical science, law, education, etc. and we will do it well. All powered by Tenzin 1.0. Would love your feedback and don't forget to check us out at & sign up at https://octave-x.com/


Very Insightful Read!!!
A RAG framework entirely inspired by natural intelligence - modeled after hippocampal indexing theory of human long-term memory(which suggests the hippocampus links and retrieves memory details stored in the cortex)

It outperforms current “cheat” RAG:)
This is how we achieve human-level intelligence, by modeling natural intelligence correctly!

Paper: https://arxiv.org/abs/2405.14831


The three most used image generation models in the HackerNoon editor are Kandinsky 3.0, Stable Diffusion XL, and RealVisXL V3.0 Turbo.

https://huggingface.co/ai-forever/Kandinsky3.0 https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0 https://huggingface.co/SG161222/RealVisXL_V3.0

Try out the HackerNoon writing experience here: https://app.hackernoon.com/new