HiDiffusion SDXL now supports Image-to-Image, so I've created an "Enhance This" version using the latest ControlNet Line Art model called MistoLine.  It's faster than DemoFusion

Demo: https://huggingface.co/spaces/radames/Enhance-This-HiDiffusion-SDXL

Older version based on DemoFusion https://huggingface.co/spaces/radames/Enhance-This-DemoFusion-SDXL

New Controlnet SDXL Controls Every Line https://huggingface.co/TheMistoAI/MistoLine

HiDiffusion is compatible with diffusers and support many SD models - https://github.com/megvii-research/HiDiffusion

osanseviero not-lain victor jaisanrobert


@Yasusan 理化学研究所AIPの関根聡と申します。AnswerCarefullyのデータについて確認いただきありがとうございます。HuggingFavceにて再配布公開いただいたこと確認しました。日本のLLMの発展に貢献したいという目標でこのデータを作成し、同じ目標で再公開いただいたと思うのですが、このデータは危険性を含んでおり、かつ、データを利用してハックされることを危惧しており、どなたが利用したかわかるよう、公開ホームページにある通り「再配布は禁止」しております。その点をご理解いただき、再配布を中止いただけますようお願いします。


What tools do you need to deconstruct bias in algorithms? (You know, this thing that is becoming increasingly prevalent in our lives)

Participate in the new discussion in the Journalists on Hugging Face community: https://huggingface.co/spaces/JournalistsonHF/README/discussions/4


🚀 Introducing RefuelLLM-2 and RefuelLLM-2-small, the next version of our large language models purpose built for data labeling, enrichment and cleaning.

RefuelLLM-2 (83.82%) outperforms all state-of-the-art LLMs, including GPT-4-Turbo (80.88%), Claude-3-Opus (79.19%) and Gemini-1.5-Pro (74.59%), across a benchmark of ~30 data labeling tasks.

RefuelLLM-2-small (79.67%), aka Llama-3-Refueled, outperforms all comparable LLMs including Claude3-Sonnet (70.99%), Haiku (69.23%) and GPT-3.5-Turbo (68.13%).

📖 Open sourcing the model weights: https://huggingface.co/refuelai/Llama-3-Refueled
📝 Detailed blog post: https://www.refuel.ai/blog-posts/announcing-refuel-llm-2
🧪 Try out the model here: https://labs.refuel.ai/playground

rajasbansal abhinavn sahilyadav rish93 nihitdesai dhruvabansal osanseviero victor DmitryRyumin Etherll Lin-Rexter thomwolf Tejasw1 lunarflu radames EveryPizza dillfrescott

abhinavn rajasbansal rish93 sahilyadav nihitdesai dhruvabansal osanseviero Lin-Rexter hiieu thomwolf lunarflu radames dillfrescott

rajasbansal abhinavn rish93 sahilyadav dhruvabansal osanseviero thomwolf lunarflu Felladrin dillfrescott

rajasbansal abhinavn rish93 dhruvabansal thomwolf lunarflu radames dillfrescott


xLSTM: Extended Long Short-Term Memory (2405.04517)

🔥🚀🌟 New Research Alert - xLSTM! 🌟🚀🔥
📄 Title: xLSTM: Extended Long Short-Term Memory 🔝

📝 Description: xLSTM is a scaled-up LSTM architecture with exponential gating and modified memory structures to mitigate known limitations. xLSTM blocks outperform SOTA transformers and state-space models in performance and scaling.

👥 Authors: Maximilian Beck et al.

📄 Paper: https://huggingface.co/papers/2405.04517

📁 Repository: https://github.com/NX-AI/xlstm

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

🔍 Keywords: #xLSTM #DeepLearning #Innovation #AI

DmitryRyumin taufiqdp mihaylovnikitos victor KvrParaskevi radames cfrancois7 AlekseiPravdin siyengfeng lunarflu osanseviero Taylor658 Lin-Rexter FreeHugsForRobots ucyang sayhan dillfrescott JoPmt


Get daily/weekly/monthly notifications about latest trending feature-extraction models compatible with transformers.js for semantic search! All open source built on GitHub Actions and ntfy.sh.

I'm also providing daily updated tables (filterable and sortable by onnx model size too!) if you want to have a look only once in a while. Download what suits you best: csv, xlsx, parquet, json, html.

Would you like to monitor other models/tags? Feel free to open a PR :)

GitHub: https://github.com/do-me/trending-huggingface-models
Ntfy.sh daily channel: https://ntfy.sh/feature_extraction_transformers_js_models_daily
Sortable table: https://do-me.github.io/trending-huggingface-models/

And the best part: all 145 models are integrated in SemanticFinder to play around with https://do-me.github.io/SemanticFinder/!


lunarflu radames Flashyflash vihangsharma faisalbsl21


Introducing illusion Diffusion Video
https://huggingface.co/spaces/KingNish/IllusionDiffusionVideo

It can Create high quality ULTRA HD illusion video.
If you find any bugs, please let me know😊


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

aaditya AtAndDev ravikiran777 fetong shetumohanto vtrubamacrocosmos Healthtensor models4world

aaditya John6666 AtAndDev kobonj vtrubamacrocosmos Healthtensor models4world

aaditya AtAndDev JoPmt Healthtensor models4world

aaditya AtAndDev Lowenzahn rhyliieee models4world

aaditya AtAndDev Healthtensor models4world


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


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.

MonsterMMORPG Svngoku fabiofernandes KillerShoaib adhisetiawan guru001 AtAndDev DeFactOfficial gregthompsonjr LeonardoSidney London12345

MonsterMMORPG adhisetiawan AtAndDev John6666 gregthompsonjr vtrubamacrocosmos

MonsterMMORPG ImranzamanML adhisetiawan AtAndDev gregthompsonjr

MonsterMMORPG adhisetiawan AtAndDev gregthompsonjr


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.


🔥 Prometheus 2 was recently released by Kaist AI as an alternative and closely mirroring both human and GPT-4 evaluation, and surpassing the former Prometheus!

https://huggingface.co/prometheus-eval/prometheus-7b-v2.0
https://huggingface.co/prometheus-eval/prometheus-8x7b-v2.0

🌬Fine-tuned on top of https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2 and https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1
🗂The datasets used for fine-tuning have been publicly released i.e. https://huggingface.co/datasets/prometheus-eval/Feedback-Collection and https://huggingface.co/datasets/prometheus-eval/Preference-Collection
🤝🏻Unified LM evaluator for absolute (a single prompt-completion pair) and relative (two completions for a given prompt) due to model merging
❌No longer needs a mandatory reference / golden answer, but can still be provided optionally
🔝Surpasses the former version of Prometheus, and has a high correlation with human, GPT-4, and Claude 3 Opus scores when evaluating LMs
📝Apache 2.0 license

Long-story short, an amazing job from Kaist AI bridging the gap with LLM evaluators other than proprietary and bigger models!

This week at Argilla, we decided to add a new task to use Prometheus 2 as an LLM evaluator using `distilabel`, so we implemented `PrometheusEval`.

😱 Using `PrometheusEval` running their 7B variant with vLLM in a single L40 on top of https://huggingface.co/datasets/HuggingFaceH4/instruction-dataset, we got the 327 existing prompt-completion pairs evaluated and pushed to the Hub in less than 2 minutes!

Find the generated dataset and the code at https://huggingface.co/datasets/distilabel-internal-testing/instruction-dataset-prometheus

matinla lunarflu davanstrien andrewrreed victor NickyNicky QuocKhanh osanseviero dhruva-sarma shamy777

matinla lunarflu andrewrreed QuocKhanh osanseviero anakin87


For those who love the Arabic language like me, This is a summary of my different models, datasets and spaces i made the last couple of months:

1. https://huggingface.co/MohamedRashad/Arabic-Orpo-Llama-3-8B-Instruct is a finetuned version of Meta-Llama-3-8B-Instruct using ORPO on https://huggingface.co/datasets/2A2I/argilla-dpo-mix-7k-arabic and the space to try it is here https://huggingface.co/spaces/MohamedRashad/Arabic-Chatbot-Arena.

2. https://huggingface.co/MohamedRashad/arabic-small-nougat is a finetuned version of https://huggingface.co/facebook/nougat-small on Arabic book pages to be a capable arabic-ocr and its space is also avialable here https://huggingface.co/spaces/MohamedRashad/Arabic-Small-Nougat.

3. There is https://huggingface.co/datasets/MohamedRashad/Arabic-CivitAi-Images dataset for text-to-image in the Arabic language (Hope someone utilize it to build something great).

4. https://huggingface.co/datasets/MohamedRashad/arabic-sts for those who want to train an Arabic Text Embedder model.

5. Finally, a small arabic dataset about translation from Fusha Arabic to English called https://huggingface.co/datasets/MohamedRashad/rasaif-translations (This dataset is very important in my opinion).


hey guys i am trying to build a High frequency trading bot on ninja trader if you are intrested in making this money printing machine hit me up and lets create a start up!


Working on a concept `GPT-2 (small)` that uses `KANs` instead of `MLPs`.
The ckpt and training code will be soon on the hub.

hanzla mrm8488 clem ucalyptus holdyourseed shengqiang takeraparterer wannaphong teshhh victor zhangliqun s3nh santyzenith taufiqdp Norod78 radames apol Etherll Korakoe QuocKhanh lunarflu osanseviero sagarduwal mathiasn1 Lin-Rexter awelker aaditya adarshxs aceeee johnlockejrr dillfrescott

shengqiang takeraparterer topgan zhangliqun Elserry radames QuocKhanh lunarflu Zmu Lin-Rexter kramp johnlockejrr dillfrescott

holdyourseed takeraparterer zhangliqun QuocKhanh lunarflu Lin-Rexter dillfrescott m18coppola Meggido erickfmm varl42

takeraparterer Karsh-CAI lunarflu dillfrescott

NickyNicky takeraparterer lunarflu dillfrescott


Question: Quantization through GPTQ

Hi Team, I’m trying to quantize a 13b model using the below configuration on A100. I tried the below options

quantization_config = GPTQConfig(
     bits=4,
     group_size=128,
     dataset="wikitext2",
     batch_size=16,
     desc_act=False

)

1. Enforce batch_size = 16  or batch_size = 2 at the quant configurations
2. Set tokenizer.pad_token_id = tokenizer.eos_token_id (which is 2)

I observed that even if we explicitly enforce the batch size and set the pad_token_id  value other than None. It is not being considered

 Can’t we set the batch_size and pad_token_id to some other value is this expected behavior with GPTQ . What is the reason behind this? Please suggest if there is any way to override the batch size config.

https://github.com/huggingface/optimum/blob/main/optimum/gptq/data.py#L51

Could you kindly suggest?  Appreciate your kind support.
Thanks


Introducing CosmoChat, a multiturn chat dataset based on Cosmopedia that I'm working on in the open on the Hub.

🎯 Goals:
💬 Create multi-turn chats seeded from Cosmopedia
🎓 Customize questions for different audience levels
🔍 Evaluate the model's ability to elaborate and clarify
🤓 (I want to learn more about creating valuable synthetic datasets, and I learn best by doing stuff rather than reading stuff).

Cosmochat is created using the excellent distilabel library.

🔗 Explore the current version of the dataset: https://huggingface.co/datasets/davanstrien/cosmochat
📝 Read more: https://huggingface.co/blog/davanstrien/cosmochat

andrewrreed clem Locutusque ucalyptus Severian alielfilali01


Question: HF model search not showing all results

I noticed that when I use the HF model search with these tags:
- feature-extraction
- transformers.js
it is not showing all models that are actually tagged.

Example: All Alibaba-NLP models (e.g. gte family) are correctly tagged but they don't show here
- https://huggingface.co/models?pipeline_tag=feature-extraction&library=transformers.js&sort=trending&search=gte
- correctly tagged model https://huggingface.co/Alibaba-NLP/gte-large-en-v1.5

Does anyone know why?

fyi @Xenova


Recently, the Hugging Face 🤗 datasets team met with the Language Technologies team led by Marta Villegas (@mvillegas) at Barcelona Supercomputing Center @BSC-LT. Eager to collaborate to promote AI across Catalan, Spanish, Basque, and Galician languages and share open-source datasets/models. 🤝 #AI #LanguageTech #OpenSource

severo lhoestq Tonic monsoon-nlp jeffboudier not-lain davanstrien VictorSanh


linxi IsmaelMousa EloyOn Benyamin369 adikur21 belyakoff AtAndDev faisalbsl21 dillfrescott Ramikan-BR

boarder Aaron2465 Noomam AtAndDev dillfrescott


Audio transcription is one of the most useful use cases for journalists (and many other professions!). @sergeipetrov, @reach-vb, @pcuenq, and @philschmid have created an optimized Whisper with Speaker Diarization for @huggingface Inference Endpoints—definitely worth a read!

Check out their blog post here: https://huggingface.co/blog/asr-diarization

You can find the notebook here: https://huggingface.co/sergeipetrov/asrdiarization-handler/blob/main/handler.py


Sparse Concept Bottleneck Models: Gumbel Tricks in Contrastive Learning (2404.03323)

Sparse Concept Bottleneck Models: Gumbel Tricks in Contrastive Learning

Paper: https://huggingface.co/papers/2404.03323

Authors propose a novel architecture and method of explainable classification with Concept Bottleneck Models (CBMs): they introduce a new type of layers known as Concept Bottleneck Layers (CBL), and present three methods for training them: with $\ell_1$-loss, contrastive loss and loss function based on Gumbel-Softmax distribution (Sparse-CBM), while final FC layer is still trained with Cross-Entropy. They show a significant increase in accuracy using sparse hidden layers in CLIP-based bottleneck models. Which means that sparse representation of concepts activation vector is meaningful in Concept Bottleneck Models.

Key concepts:
– Contrastive Gumbel-Softmax loss: the first contrastive variant of Gumbel-Softmax objective which achieves an inner sparse representation of the Concept Bottleneck Layer activations.
– Sparse $\ell_1$ regularization.
– Contrastive loss for inner layers of the model.

Methodology:
The approach consists of three main steps:
– Create a set of concepts based on the labels of the dataset.
– Supply a multi-modal encoder with CBL.
– Train this CBL with the picked objective function and train the classifiers head with Cross-Entropy.

Results and Analysis:
The methodology can be applied to the task of interpreted image classification. And the experimental results show the superiority of using sparse hidden representations of concepts.


🔬 Open LLM Progress Tracker 🔬

Inspired by the awesome work from @mlabonne, I created a Space to monitor the narrowing gap between open and proprietary LLMs as scored by the LMSYS Chatbot Arena ELO ratings 🤗

The goal is to have a continuously updated place to easily visualize these rapidly evolving industry trends 🚀

🔗 Open LLM Progress Tracker: https://huggingface.co/spaces/andrewrreed/closed-vs-open-arena-elo
🔗 Source of Inspiration: https://www.linkedin.com/posts/maxime-labonne_arena-elo-graph-updated-with-new-models-activity-7187062633735368705-u2jB/

mlabonne kristaller486 DDJN-data-science NePe m-ric victor rennokki radames nachoyawn ucyang clboetticher Optimisuke

pakkinlau m-ric victor DimaGM Optimisuke jeffboudier


@llmixer is doing g-d's work by orthogonalizing weakness out of free-thinking models, well-done @llmixer


What matters when building vision-language models? (2405.02246)

We release Idefics2-chatty, the chatbot-optimized version of Idefics2: https://huggingface.co/HuggingFaceM4/idefics2-8b-chatty

Idefics2-chatty is better at following instructions and following Chain-of-Thoughts reasoning.

Moreover, we also release a paper, containing a lot of findings on how to build an efficient and performant Vision-Language Model: https://huggingface.co/papers/2405.02246

How are you going to use the model, or what data are you going to fine-tune it on?

yhaj1017 zafar5783 0xgimic pedi VictorSanh DmitryRyumin Nazikkrs samusenps DongfuJiang victor ahmetozkann tit33 Eyel louisbrulenaudet aloobun osanseviero Lewdiculous muhtasham

yhaj1017 zafar5783 VictorSanh DmitryRyumin samusenps DongfuJiang victor ahmetozkann Lewdiculous muhtasham


💬🔥Releasing idefics2-8b-chatty, the chat-optimized version of Idefics2!

It is a very efficient (8B parameters) state-of-the-art VLM, has been red-teamed, and comes with a few surprises:
- 📖Paper dissecting a lot of the experimental insights we learned building Idefics2:
- 🏎TGI integration for blazing-fast inference (you can already run it locally with < 24GB GPU memory)
- 🏆 Ranking 2nd in its category (< 10B, open weights) in the awesome Open VLM Leaderboard, and now appearing in the incredible Vision Arena

Ressources:
⏯Playground: https://huggingface.co/spaces/HuggingFaceM4/idefics2_playground
📖Paper: https://huggingface.co/papers/2405.02246
🏋♂Model and red-teaming analysis: https://huggingface.co/HuggingFaceM4/idefics2-8b-chatty
👀Ressources to get started: https://huggingface.co/HuggingFaceM4/idefics2-8b-chatty#how-to-get-started
🏆Open VLM Leaderboard: https://huggingface.co/spaces/opencompass/open_vlm_leaderboard
🏟Vision arena: https://huggingface.co/spaces/WildVision/vision-arena

HugoLaurencon Srikanth492 FLOGFI yhaj1017 zafar5783 pedi Nazikkrs ShahabVFX mrslimbine Eyel osanseviero


NEW publishing hub for trending text model academic research papers broken down into open source technical blog posts ➡ https://textmodels.tech


This dataset is cool:
https://huggingface.co/datasets/MohamedRashad/midjourney-detailed-prompts

kelork niwram69 boksman Ndonda871 nivram189 kooshi FLOGFI pedi ahmedzx


I've just stumbled upon some excellent work on (🇫🇷 French) retrieval models by @antoinelouis. Kudos to him!

- French Embedding Models: https://huggingface.co/collections/antoinelouis/dense-single-vector-bi-encoders-651523c0c75a3d4c44fc864d
- French Reranker Models: https://huggingface.co/collections/antoinelouis/cross-encoder-rerankers-651523f16efa656d1788a239
- French Multi-vector Models: https://huggingface.co/collections/antoinelouis/dense-multi-vector-bi-encoders-6589a8ee6b17c06872e9f075
- Multilingual Models: https://huggingface.co/collections/antoinelouis/modular-retrievers-65d53d0db64b1d644aea620c

A lot of these models use the MS MARCO Hard Negatives dataset, which I'm currently reformatting to be more easily usable. Notably, they should work out of the box without any pre-processing for training embedding models in the upcoming Sentence Transformers v3.


@rubend18  Hello Ruben, I have been trying to manipulate my Andriod and other phones for some time. Friends always ask, its a hit or miss. May I have access to your repo on the jailbreaking please.

kelork Ndonda871 kooshi pedi ShahabVFX Dariabaraeva


3D Gaussian Blendshapes for Head Avatar Animation (2404.19398)

🚀🎭🌟 New Research Alert - SIGGRAPH 2024 (Avatars Collection)! 🌟🎭🚀
📄 Title: 3D Gaussian Blendshapes for Head Avatar Animation 🔝

📝 Description: 3D Gaussian Blendshapes for Head Avatar Animation is a novel method for modeling and animating photorealistic head avatars from monocular video input.

👥 Authors: Shengjie Ma, Yanlin Weng, Tianjia Shao, and Kun Zhou

📅 Conference: SIGGRAPH, 28 Jul – 1 Aug, 2024 | Denver CO, USA 🇺🇸

📄 Paper: https://huggingface.co/papers/2404.19398

🌐 Github Page: https://gapszju.github.io/GaussianBlendshape/

📚 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 Avatars Collection: https://huggingface.co/collections/DmitryRyumin/avatars-65df37cdf81fec13d4dbac36

🔍 Keywords: #3DAnimation #HeadAvatar #GaussianBlendshapes #FacialAnimation #RealTimeRendering #SIGGRAPH2024 #ComputerGraphics #DeepLearning #ComputerVision #Innovation

DmitryRyumin Ramayanti kelork boksman Ndonda871 pedi ShahabVFX ahmedzx afoam thomwolf danielus


🔥 Did you know that you can try out Play.HT 2.0 and OpenVoice V2 on the TTS Arena for free?

Enter text and vote on which model is superior!
https://huggingface.co/spaces/TTS-AGI/TTS-Arena


Chemllm.org Now transfered to ChemLLM-20B-DPO, Have a try now!🤗


mlx_micrograd - mlx port of Karpathy's micrograd- a tiny scalar-valued autograd engine with a small PyTorch-like neural network library on top.

https://github.com/Jaykef/mlx_micrograd
Installation
```bash
pip install mlx_micrograd
```
Example usage
Example showing a number of possible supported operations:
```python
from mlx_micrograd.engine import Value

a = Value(-4.0)
b = Value(2.0)
c = a + b
d = a * b + b**3
c += c + 1
c += 1 + c + (-a)
d += d * 2 + (b + a).relu()
d += 3 * d + (b - a).relu()
e = c - d
f = e**2
g = f / 2.0
g += 10.0 / f
print(f'{g.data}') # prints array(24.7041, dtype=float32), the outcome of this forward pass
g.backward()
print(f'{a.grad}') # prints array(138.834, dtype=float32), i.e. the numerical value of dg/da
print(f'{b.grad}') # prints array(645.577, dtype=float32), i.e. the numerical value of dg/db
```


Create and Train Your Own Expert LLM: Generating Synthetic, Fact-Based Datasets with LMStudio/Ollama and then fine-tuning with MLX and Unsloth

Hey everyone!

I know there are tons of videos and tutorials out there already but I've noticed a lot of questions popping up in community posts about using synthetic datasets for creative projects and how to transform personal content into more factual material. In my own work doing enterprise-level SFT and crafting my open-source models, I've enhanced a Python framework originally shared by the creator of the Tess models. This improved stack utilizes local language models and also integrates the Wikipedia dataset to ensure that the content generated is as accurate and reliable as possible.

I've been thinking of putting together a comprehensive, step-by-step course/guide on creating your own Expert Language Model. From dataset preparation and training to deployment on Hugging Face and even using something like AnythingLLM for user interaction. I'll walk you through each phase, clarifying complex concepts and troubleshooting common pitfalls.

Let me know if this interests you!

Most of the datasets and models I've made have been using these scripts and my approach

afkfatih algorithm MultiTrickFox raincandy-u victor nivram189 pedi LeroyDyer ahmedzx AtAndDev GaelicThunder

HeRksTAn algorithm victor nivram189 pedi AtAndDev Norod78


Self-healing Nodes with Adaptive Data-Sharding (2405.00004)

Enhancing Distributed Systems with Self-Healing Nodes and Adaptive Data Sharding

Paper: https://huggingface.co/papers/2405.00004

The paper introduces an innovative approach to improve distributed systems by integrating self-healing nodes with adaptive data sharding. This method leverages advanced concepts like self-replication, fractal regeneration, and predictive sharding to enhance scalability, performance, fault tolerance, and adaptability.

Key Concepts:
- Self-Replication: Nodes can create copies of themselves or their data to aid in recovery and load balancing.
- Fractal Regeneration: Nodes can reconfigure and restore their functionality after partial damage, inspired by natural fractals.
- Predictive Sharding: Nodes can anticipate future data trends and proactively adjust data distribution to optimize performance.

Methodology:
The approach consists of four main steps:
- Temporal data sharding based on data's temporal characteristics.
- Self-replicating nodes to enhance data availability and reliability.
- Fractal regeneration for robust recovery mechanisms.
- Predictive sharding using consistent hashing to anticipate and adapt to future data trends.

Results and Analysis:
Experimental evaluations show that this approach outperforms existing data sharding techniques in scalability, performance, fault tolerance, and adaptability. The use of synthetic data and workload generators created realistic scenarios for testing.

Applications:
The methodology can be applied to various domains such as distributed database systems, blockchain networks, IoT, and cloud computing, offering improvements in data distribution efficiency and system resilience.


# Thoughts on Neural Scaling Laws
When you take a zoomed-out perspective view on the success goals of neural networks, you see they all revolve around the Scaling Laws - empirical observations that performance improves with increased model size, dataset, and compute resources.

The specifics of how these laws apply, vary for different modalities and architectures. This is notable in the empirical equations used to measure these laws.

Yet they all heavily rely on three main factors - Data, Size and Computation. These factors themselves also have sub-dependencies - data size & quality, model size & architecture, num of GPUs & code for compute kernels respectively.

As research in these laws progresses, we begin to see new scaling laws emerge that may apply in much different ways than usual. This is typical in recent local LLMs (Phi-3, Gemma 2B, LLMs in a flash) which shows small sized models with small rich quality data beating large models

I look forward to the singularity moment - when these laws take a full round spin and meet at where it all began:)

References:
- Scaling Laws for Neural Language Models: https://arxiv.org/pdf/2001.08361
- Scaling Laws for Autoregressive Generative Modeling: https://arxiv.org/abs/2010.14701
- LLMs in a flash: https://arxiv.org/abs/2312.11514
- Phi-3 Technical Report: https://arxiv.org/abs/2404.14219
- Gemma 2B: https://arxiv.org/pdf/2403.08295


The IDM-VTON (Improving Diffusion Models for Authentic Virtual Try-on in the Wild) is so powerful that it can even transfer beard or hair as well.

I have prepared installer scripts and full tutorials for Windows (requires min 8 GB VRAM GPU), Massed Compute (I suggest this if you don’t have a strong GPU), RunPod and a free Kaggle account (works perfect as well but slow).

Windows Tutorial : https://youtu.be/m4pcIeAVQD0

Cloud (Massed Compute, RunPod & Kaggle) Tutorial : https://youtu.be/LeHfgq_lAXU


A new dataset for anyone interested in Satellite imagery: 3 million @Satellogic images of unique locations — 6 million images, including location revisits — from around the world under a Creative Commons CC-BY 4.0 license.

Interesting potential in journalism.

https://huggingface.co/datasets/satellogic/EarthView


Excited to bring our benchmarking leaderboard of >100 LLM API endpoints to HF!

Speed and price are often just as important as quality when building applications with LLMs. We bring together all the data you need to consider all three when you need to pick a model and API provider.

Coverage:
‣ Quality (Index of evals, MMLU, Chatbot Arena, HumanEval, MT-Bench)
‣ Throughput (tokens/s: median, P5, P25, P75, P95)
‣ Latency (TTFT: median, P5, P25, P75, P95)
‣ Context window
‣ OpenAI library compatibility

Link to Space: https://huggingface.co/spaces/ArtificialAnalysis/LLM-Performance-Leaderboard

Blog post: https://huggingface.co/blog/leaderboard-artificial-analysis

jeffboudier victor Kukedlc clefourrier Ijithad ajibawa-2023 ShahabVFX not-lain


Only 14 languages have DPO preference style datasets on the Hugging Face Hub (https://huggingface.co/spaces/DIBT/preference_data_by_language) Let's improve that! How?

The Cohere For AI Aya dataset https://huggingface.co/datasets/CohereForAI/aya_dataset has human-annotated prompt-completion pairs in 71 languages. We can use this to create DPO datasets for more languages!

Using Aya's prompt/response pairs as a starting point we can use an LLM to generate an additional response to each prompt. We then use an LLM Judge to rank each response.

✅ In some/many languages, human responses may be better than LLM ones but we may want to check that assumption for some languages.
🚀 We use Argilla's distilabel library to push data to Argilla for validation. This also allows us to determine if an LLM judge is effective for different languages.

As an example of what this pipeline produces:
- https://huggingface.co/datasets/DIBT/aya_dutch_dpo a DPO style dataset for Dutch using Llama 3 as a generator/judge LM.
- An annotation Space that anyone with a HF account can contribute to: https://dibt-demo-argilla-space.hf.space/dataset/924ef8a8-a447-4563-8806-0e2a668a5314/annotation-mode?page=1&status=pending

As part of Data is Better Together we want to build more DPO datasets. Join us here: https://github.com/huggingface/data-is-better-together#4-dpoorpo-datasets-for-more-languages 🤗

librarian-bot fdaudens ShahabVFX comarproject anakin87


🚀🚀🚀🚀 Introducing AutoTrain Configs! 🚀🚀🚀🚀
Now you can train models using yaml config files! 💥 These configs are easy to understand and are not at all overwhelming. So, even a person with almost zero knowledge of machine learning can train state of the art models without writing any code. Check out example configs in the config directory of autotrain-advanced github repo and feel free to share configs by creating a pull request 🤗
Github repo: https://github.com/huggingface/autotrain-advanced

not-lain Taylor658 derek-thomas radames imad40 Tonic


May the fourth be with you...  Ai yoda #starwarsday


A Primer on the Inner Workings of Transformer-based Language Models (2405.00208)

🔍 Today's (self-serving) pick in Interpretability & Analysis of LMs:

A Primer on the Inner Workings of Transformer-based Language Models
by @javifer @gsarti @arianna-bis and M. R. Costa-jussà
(@mt-upc, @GroNLP, @facebook)

This primer can serve as a comprehensive introduction to recent advances in interpretability for Transformer-based LMs for a technical audience, employing a unified notation to introduce network modules and present state-of-the-art interpretability methods.

Interpretability methods are presented with detailed formulations and categorized as either localizing the inputs or model components responsible for a particular prediction or decoding information stored in learned representations. Then, various insights on the role of specific model components are summarized alongside recent work using model internals to direct editing and mitigate hallucinations.

Finally, the paper provides a detailed picture of the open-source interpretability tools landscape, supporting the need for open-access models to advance interpretability research.

📄 Paper: https://huggingface.co/papers/2405.00208

🔍 All daily picks: https://huggingface.co/collections/gsarti/daily-picks-in-interpretability-and-analysis-ofc-lms-65ae3339949c5675d25de2f9


Capabilities of Gemini Models in Medicine (2404.18416)

The Open Medical-LLM Leaderboard is now up on HF Spaces. 🤗

https://huggingface.co/spaces/openlifescienceai/open_medical_llm_leaderboard

It will be interesting to add the results of the just announced Med-Gemini model to the Leaderboard to see how it compares and if its stated 91.1% MedQA benchmark is accurate.

https://huggingface.co/papers/2404.18416


I've added new collections to the Journalists on 🤗 community, focusing on Data Visualization, Optical Character Recognition, and Multimodal Models:

- TinyChart-3B: This model interprets data visualizations based on your prompts. It can generate the underlying data table from a chart or recreate the chart with Python code.
- PDF to OCR: Convert your PDFs to text—ideal for FOI records sent as images.
- Idefics-8b: A multimodal model that allows you to ask questions about images.

Explore these tools here: 👉 https://huggingface.co/JournalistsonHF

Taylor658 damerajee ucalyptus JuliusSandmann lunarflu ShahabVFX


Introducing llama-3-neural-chat-v2.2-8b! This powerful conversational AI model builds on Meta's Llama 3, fine-tuned by Locutusque for enhanced performance in coding, math & writing.

https://huggingface.co/Locutusque/llama-3-neural-chat-v2.2-8B

aloobun Tonic lunarflu jlinux ShahabVFX mohammadaeid

Taylor658 louisbrulenaudet Tonic lunarflu mohammadaeid


I just shared a blogpost on https://nateraw.com explaining the motivation + process of training https://huggingface.co/nateraw/musicgen-songstarter-v0.2 - including training details, WandB logs, hparams, and notes on previous experiments.

Check it out here ⤵
https://nateraw.com/posts/training_musicgen_songstarter.html

:) still kinda a WIP so if there's anything else you want to see, let me know.

rooben lunarflu akashicmarga allandclive ShahabVFX tvergho pbotsaris multimodalart aceeee

lunarflu aceeee Norod78 tvergho multimodalart


A list of my favorite articles I've written on Hugging Face (the others are kind of mid imo):
https://huggingface.co/blog/AmelieSchreiber/a-new-era-of-enzyme-engineering

https://huggingface.co/blog/AmelieSchreiber/protein-optimization-and-design

https://huggingface.co/blog/AmelieSchreiber/rfdiffusion-potentials

https://huggingface.co/blog/AmelieSchreiber/mutation-scoring

https://huggingface.co/blog/AmelieSchreiber/esmbind

https://huggingface.co/blog/AmelieSchreiber/esm2-ptm

https://huggingface.co/blog/AmelieSchreiber/directed-evolution-with-esm2

https://huggingface.co/blog/AmelieSchreiber/intrinsic-dimension-of-proteins

https://huggingface.co/blog/AmelieSchreiber/plm-persistent-homology-msa-replacement

https://huggingface.co/blog/AmelieSchreiber/faster-pha

mmhamdy Taylor658 hsevi lunarflu victor vtiyyal1 radames samusenps ehottl AtAndDev kgourgou AglWings high-entropy-gio gabriel-charite raannakasturi

lunarflu vtiyyal1 radames samusenps AtAndDev kgourgou Vlansu wangjin2000 high-entropy-gio vchupakhin BeanXu lighttt9999 bloyal


Looking for someone with +10 years of experience training Deep Kolmogorov-Arnold Networks.

Any suggestions?


Hello!
The 8B/70B OG Llama-3 models made with the Orthogonal Activation Steering script as been pushed in private.

After multiple test with an empty prompt system, I can confirm it's not uncensored enough, but I wanted to try all the GGUF before (and it take time to do lmao)

If you want to try that yourself, here is the script : https://gist.github.com/wassname/42aba7168bb83e278fcfea87e70fa3af
And here is the same script that we modified to be able to use it on multiple GPU for 70B : https://files.catbox.moe/ya4rto.ipynb

Llama3-Unholy-8B-OAS don't have the problem as it was already trained to be less censored, but the OG one was really too much censored.

I will try to redo that soon, as it seems to HAVE WORKED for some prompt (as seen on the log, for exemple) but it's not enough.

32 entry of the dataset is clearly not enough, but it's okay, I really wanted to try that as it was something new.
I could take the Unholy way and retrain the 70B before using OAS but it should work without, that's not the goal.

llama-anon musicurgy Kearm Chief-Inspector Bakanayatsu ucalyptus rooben nschle wassname cosmojg ClayFace thomasgauthier avatar9692 Taylor658 kaz666 ngphuchoangnam vikarti-anatra


one thousand Miku ヾ( ˃ᴗ˂ )◞ • *✰
 https://huggingface.co/datasets/parsee-mizuhashi/miku
by @parsee-mizuhashi

antiven0m samusenps r3gm EloyOn Lewdiculous EveryPizza


🚀 We recently released datasets 2.19.0! 📦

🔥 What's New:
- Polars integration 🐻❄
- fsspec support for conversion to JSON, CSV, and Parquet
- Mode parameter for Image feature
- CLI function to convert script-datasets to Parquet
- Dataset.take and Dataset.skip

Plus, a bunch of general improvements & bug fixes!

Check out the release notes: https://github.com/huggingface/datasets/releases/tag/2.19.0

Upgrade now and power up your data workflows! 💥

mmhamdy lucabaggi Taylor658 valentimarco davanstrien SilentWraith not-lain akashicmarga louisbrulenaudet Molbap pedi ynakashima

mmhamdy ucalyptus 1aurent not-lain Molbap


New model and dataset! The Llama-3-IMPACTS-2x8B-64k-MLX (and upcoming GGUF) model is a cutting-edge large language model trained on the I.M.P.A.C.T.S dataset, which encompasses scenarios from biomimicry, climate change, and theoretical astrobiology.

https://huggingface.co/Severian/Llama-3-IMPACTS-2x8B-64k-MLXF
https://huggingface.co/datasets/Severian/IMPACTS


Sailor: Open Language Models for South-East Asia (2404.03608)

✨ Today, we're excited to share the full data processing script used in developing our Sailor models. The repo provides an end-to-end data processing pipeline for LLM training. 🚀

💻Code: https://github.com/sail-sg/sailcraft
🤗Model: https://huggingface.co/collections/sail/sailor-language-models-65e19a749f978976f1959825
📜Paper: https://huggingface.co/papers/2404.03608
🌐Homepage: https://sailorllm.github.io

# Overview 🔍

The pipeline consists of 4 stages🧹:
1⃣ Initial data cleaning
2⃣ Near deduplication
3⃣ Exact deduplication
4⃣ Second round of data cleaning

A special focus was given to the data cleaning part of South-East Asian (SEA) languages🌍

# Use Case ✨

With this codebase, you can clean your own dataset with:

✅ Get filtered data counts after each processing stage
✅ Easily configure language-specific cleaning rules (we support Arabic, Bengali, Catalan, Spanish, Basque, French, Hindi, Portuguese, Urdu, and optimize for English, Indonesian, Vietnamese, Chinese, Thai, Lao, Malay)
✅ Investigate what data was removed at each processing stage

# Acknowledgement 🙏

The main credit goes to @dreamerdeo , the first author of our Sailor paper ❤! He put in tremendous effort on the data processing pipeline, enabling the model's great performance. We believe the mini repo will be a valuable resource for researchers working on dataset curation for large language models. 🎉

Sharing the recipe openly aligns with our commitment to open language model development. 💪 And this repo would not have been possible without the contributions from the open community, including the BigScience data cleaning tool, the all-in-one deduplication tool by @chenghao , and the deduplication project from Google. 🧠

# What's Next 🚀

Share your thoughts or leave any comments on what you'd like the Sailor models to do! We also have some exciting news coming soon, and please stay tuned. 🚄

mmhamdy dreamerdeo ToluClassics chenghao


#Previous Version / Older
📂Huggingface for Android ➡
🪶Median ( Go Native ) Plugin :

version 0.0.1
🚀 https://huggingface.co/spaces/prithivMLmods/Huggingface-Android-App

jeanflop samusenps Taylor658 ucalyptus raincandy-u AlekseiPravdin dillfrescott pedi Ramikan-BR prithivMLmods Ngrthm


A Careful Examination of Large Language Model Performance on Grade
  School Arithmetic (2405.00332)

A Careful Examination of Large Language Model Performance on Grade School Arithmetic

https://huggingface.co/papers/2405.00332

Large language models (LLMs) have achieved impressive success on many benchmarks for mathematical reasoning. However, there is growing concern that some of this performance actually reflects dataset contamination, where data closely resembling benchmark questions leaks into the training data, instead of true reasoning ability. To investigate this claim rigorously, we commission Grade School Math 1000 (GSM1k). GSM1k is designed to mirror the style and complexity of the established GSM8k benchmark, the gold standard for measuring elementary mathematical reasoning. We ensure that the two benchmarks are comparable across important metrics such as human solve rates, number of steps in solution, answer magnitude, and more. When evaluating leading open- and closed-source LLMs on GSM1k, we observe accuracy drops of up to 13%, with several families of models (e.g., Phi and Mistral) showing evidence of systematic overfitting across almost all model sizes. At the same time, many models, especially those on the frontier, (e.g., Gemini/GPT/Claude) show minimal signs of overfitting. Further analysis suggests a positive relationship (Spearman's r^2=0.32) between a model's probability of generating an example from GSM8k and its performance gap between GSM8k and GSM1k, suggesting that many models may have partially memorized GSM8k.


🚀 Just released version 0.23.0 of the huggingface_hub Python library!

Exciting updates include:
📁 Seamless download to local dir!
💡 Grammar and Tools in InferenceClient!
🌐  Documentation full translated to Korean!
👥 User API: get likes, upvotes, nb of repos, etc.!
🧩 Better model cards and encoding for ModelHubMixin!

Check out the full release notes for more details:
https://huggingface.co/spaces/Wauplin/huggingface_hub/discussions/6
 👀

severo tomaarsen Lewdiculous julien-c albertvillanova mmhamdy radames davanstrien Molbap not-lain


@VanshGehlot could upgrade help Me with this


Full parameter fine-tuning of the LLaMA-3 8B model using a single GTX 3090 GPU with 24GB of graphics memory?

Please check out our tool for fine-tuning, inferencing, and evaluating GreenBitAI's low-bit LLMs:
https://github.com/GreenBitAI/green-bit-llm
Model Zoo:
https://huggingface.co/GreenBitAI


MotionLCM: Real-time Controllable Motion Generation via Latent
  Consistency Model (2404.19759)

🔥Motion Latent Consistency Model🔥

Introducing MotionLCM💃, controlling and generating a motion in milliseconds!

Huggingface Space:
https://huggingface.co/spaces/wxDai/MotionLCM
Huggingface Paper:
https://huggingface.co/papers/2404.19759

Project page: https://dai-wenxun.github.io/MotionLCM-page/
Paper: https://arxiv.org/pdf/2404.19759.pdf
Code: https://github.com/Dai-Wenxun/MotionLCM
video: https://www.youtube.com/watch?v=BhrGmJYaRE4

MotionLCM supports inference pipelines of 1-4 steps, with almost no difference in effectiveness between 1 and 4 steps. Generating approximately 200 frames of motion only takes about 30ms, which averages to approximately 6k fps per frame.

Our MotionLCM can achieve high-quality text-to-motion and precise motion control results (both sparse and dense conditions) in ∼30 ms.

We integrated a control module into the diffusion of the latent space, named Motion ControlNet, to achieve controllable motion generation. Our control algorithm is approximately 1,000 times faster than the best-performing baseline, with comparable quality.


Custom pipelines and components in Diffusers 🎸

Wanted to use customized pipelines and other components (schedulers, unets, text encoders, etc.) in Diffusers?

Found it inflexible?

Since the first dawn on earth, we have supported loading custom pipelines via a `custom_pipeline` argument 🌄

These pipelines are inference-only, i.e., the assumption is that we're leveraging an existing checkpoint (e.g., runwayml/stable-diffusion-v1-5) and ONLY modifying the pipeline implementation.

We have many cool pipelines, implemented that way. They all share the same benefits available to a `DiffusionPipeline`, no compromise there 🤗

Check them here:
https://github.com/huggingface/diffusers/tree/main/examples/community

Then we might have a requirement of everything customized i.e., custom components along with a custom pipeline. Sure, that's all possible.

All you have to do is keep the implementations of those custom components on the Hub repository you're loading your pipeline checkpoint from.

SDXL Japanese was implemented like this 🔥
https://huggingface.co/stabilityai/japanese-stable-diffusion-xl

Full guide is available here ⬇
https://huggingface.co/docs/diffusers/main/en/using-diffusers/custom_pipeline_overview

And, of course, these share all the benefits that come with `DiffusionPipeline`.


When I read the KAN paper, I see physicists casually making fun of the uncertainties in MLPs or Neural nets as a whole:

- "The philosophy here is close to the mindset of physicists, who often care more about typical cases rather than worst cases" lol this went hard on NNs

- "Finite grid size can approximate the function well with a residue rate independent of the dimension, hence beating curse of dimensionality!" haha.

- "Neural scaling laws are the phenomenon where test loss decreases with more model parameters"

- "Our approach, which assumes the existence of smooth Kolmogorov Arnold representations, decomposes the high-dimensional function into several 1D functions"

Key Differences With MLPs:
- Activation Functions: Unlike MLPs that use fixed activation functions at the nodes, KANs utilize learnable activation functions located on the edges between nodes.
- Weight Parameters: In KANs, traditional linear weight matrices are absent. Instead, each weight parameter is replaced by a learnable univariate function, specifically a spline.
- Summation Nodes: Nodes in KANs perform simple summation of incoming signals without applying non-linear transformations.

Advantages Over MLPs:
- Accuracy: achieve higher accuracy with smaller network sizes compared to larger MLPs in tasks like data fitting and solving partial differential equations (PDEs).
- Interpretability: Due to their unique structure, KANs are more interpretable than MLPs.

Technical Innovations:
- Learnable Edges: learnable functions on network edges presents a novel approach to network design, providing greater flexibility in modeling complex relationships in data.
- No Linear Weights: elimination of linear weights reduces the parameters, and potentially simplifies the learning process, focusing on the optimization of univariate function representations.

Applications and Practical Use:
- Scientific Collaboration: KANs have been applied in scientific settings as tools to help discover or rediscover math


TGI v2.0.2 is out!
- New models (idefics2, phi3)
- Cleaner VLM support in the openai layer
- Upgraded to pytorch 2.3.0

https://github.com/huggingface/text-generation-inference/releases/tag/v2.0.2

Kudos @Narsil @olivierdehaene @drbh and so many contributors!

sosoai Narsil VictorSanh tomaarsen radames Alagib dawengmerak18 clboetticher


KAN: Kolmogorov-Arnold Networks (2404.19756)

Okay, first pass over KAN: Kolmogorov–Arnold Networks, it looks very interesting!

Interpretability of KAN model:
May be considered mostly as a safety issue these days, but it can also be used as a form of interaction between the user and a model, as this paper argues and I think they make a valid point here. With MLP, we only interact with the outputs, but KAN is an entirely different paradigm and I find it compelling.

Scalability:
KAN shows better parameter efficiency than MLP. This likely translates also to needing less data. We're already at the point with the frontier LLMs where all the data available from the internet is used + more is made synthetically...so we kind of need something better.

Continual learning:
KAN can handle new input information w/o catastrophic forgetting, which helps to keep a model up to date without relying on some database or retraining.

Sequential data:
This is probably what most people are curious about right now, and KANs are not shown to work with sequential data yet and it's unclear what the best approach might be to make it work well both in training and regarding the interpretability aspect. That said, there's a rich long history of achieving sequential data in variety of ways, so I don't think getting the ball rolling here would be too challenging.

Mostly, I just love a new paradigm and I want to see more!

https://huggingface.co/papers/2404.19756

valeriylo victor mmhamdy radames turiabu jorostuff KillerShoaib jgitsolutions dillfrescott Roozbehr72 Adien123 makiisthebes fendy07

Jaward sergeodesico rennokki sseres Noomam dillfrescott Pwlot nebiyu29


🪽 New FluentlyXL V4!

🤗 Finally the model came out, it turned out great, we worked on contrast, realism and anatomy.

👨💻 Model page:  https://huggingface.co/fluently/Fluently-XL-v4

🦾 You can try it here: https://huggingface.co/spaces/fluently/Fluently-Playground

ehristoforu dreamdrop-art lunarflu victor radames DmitryRyumin Delik frankmao lailailailailai

victor ehristoforu radames dreamdrop-art


Can you create domain-specific synthetic datasets in under 20 minutes?

@burtenshaw recently launched the Domain Specific Dataset Project as part of Data is Better Together. As part of this, Ben created a Space that you can use to define some key perspectives and concepts from a domain. This seed dataset can then be used to generate a synthetic dataset for a particular domain.

In less than 30 minutes this afternoon, I created a domain-specific dataset focused on data-centric machine learning using these tools: https://huggingface.co/datasets/davanstrien/data-centric-ml-sft.

You can create your own domain specific datasets using this approach. Find the steps to follow here: https://github.com/huggingface/data-is-better-together/blob/main/domain-specific-datasets/README.md

alvarobartt lunarflu ZennyKenny fdaudens burtenshaw tomaarsen Taylor658 ucyang


If you are a debate fan or did this as an extracurricular activity as a kid, you might have fun with this demo - debate bot. Debate against AI/RAG:

https://huggingface.co/spaces/vectara/debate-bot


New conversation in our Journalists on Hugging Face community: Exploring auto-tagging articles for taxonomy.

I've shared insights from my previous experience with fine-tuning a model for a classification task. But has anyone built a similar use case? Or are you seeking a solution for this task too? Join the discussion here: https://huggingface.co/spaces/JournalistsonHF/README/discussions/2#6631b9a250c203b5a925d2e1


Octopus v4: Graph of language models (2404.19296)

Octopus v4

Graph of language models

https://huggingface.co/papers/2404.19296

Language models have been effective in a wide range of applications, yet the most sophisticated models are often proprietary. For example, GPT-4 by OpenAI and various models by Anthropic are expensive and consume substantial energy. In contrast, the open-source community has produced competitive models, like Llama3. Furthermore, niche-specific smaller language models, such as those tailored for legal, medical or financial tasks, have outperformed their proprietary counterparts. This paper introduces a novel approach that employs functional tokens to integrate multiple open-source models, each optimized for particular tasks. Our newly developed Octopus v4 model leverages functional tokens to intelligently direct user queries to the most appropriate vertical model and reformat the query to achieve the best performance. Octopus v4, an evolution of the Octopus v1, v2, and v3 models, excels in selection and parameter understanding and reformatting. Additionally, we explore the use of graph as a versatile data structure that effectively coordinates multiple open-source models by harnessing the capabilities of the Octopus model and functional tokens.


Hello Community,
Would you like to see Illusion Diffusion in Video format. https://huggingface.co/spaces/AP123/IllusionDiffusion

Let me Know.

nijatzeynalov ijohn07 victor Nick088 SvCy NHLOCAL fffiloni Aldyytzyy maxvirusvj


Chunte not-lain dillfrescott Joseph717171


Dear all,

We are happy to share that we have just open-sourced over 200 low-bit LLMs. For the MLX community, we have prepared 2-4 bit versions of mainstream LLMs. You can visit the following collection to access them: https://huggingface.co/collections/GreenBitAI/greenbitai-mlx-llm-6614eb6ceb8da657c2b4ed58.

These low-bit models can be conveniently used through our open-source tool at https://github.com/GreenBitAI/gbx-lm.

Compared to other open-source quantization algorithms, these models provide better accuracy retention. We have provided some model evaluation results here:
https://github.com/GreenBitAI/green-bit-llm/blob/main/green_bit_llm/evaluation/README.md.

You can also evaluate the models yourself using the evaluation script we provided.

YaTharThShaRma999 victor yanghaojin alobnayis NicoNico KingNish adhisetiawan clem


two new VLM benchmarks! 🤩

BLINK: evaluates tasks that humans can solve within a blink 👀 https://huggingface.co/datasets/BLINK-Benchmark/BLINK

SEED-2-Plus: multichoice questions on charts, maps, webs 😍 https://huggingface.co/datasets/AILab-CVC/SEED-Bench-2-plus


text-generation-inference 2.0.2 is out.

- Native support for Idefics2, with much better efficiency than llava 1.6 (next) !

Phi3, Increase VLM support in the openai layer.

Release notes https://github.com/huggingface/text-generation-inference/releases/tag/v2.0.2


EMOPortraits: Emotion-enhanced Multimodal One-shot Head Avatars (2404.19110)

🚀🎭🌟 New Research Alert - CVPR 2024 (Avatars Collection)! 🌟🎭🚀
📄 Title: EMOPortraits: Emotion-enhanced Multimodal One-shot Head Avatars 🔝

📝 Description: EMOPortraits is an enhanced multimodal one-shot head avatar model that achieves SOTA performance in emotion transfer and audio-driven facial animation tasks by improving the training pipeline and architecture to better handle intense and asymmetric facial expressions, while also proposing a novel multiview video dataset containing a wide range of such expressions.

👥 Authors: Nikita Drobyshev, Antoni Bigata Casademunt, Konstantinos Vougioukas, Zoe Landgraf, Stavros Petridis, and Maja Pantic

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

📄 Paper: https://huggingface.co/papers/2404.19110

🌐 GitHub Page: https://neeek2303.github.io/EMOPortraits

📚 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 Avatars Collection: https://huggingface.co/collections/DmitryRyumin/avatars-65df37cdf81fec13d4dbac36

🔍 Keywords: #EMOPortraits #EmotionalTransfer #FacialAnimation #HeadAvatar #MultimodalLearning #OneShotLearning #AsymmetricFacialExpressions #IntenseFacialExpressions #NovelDataset #CVPR2024 #DeepLearning #ComputerVision #Innovation

DmitryRyumin KingNish victor Delik jeffboudier RedSparkie AlekseiPravdin nroggendorff


Really excited to read about Kolmogorov Arnold Networks as a novel alternatives to Multi Layer Perceptrons.

Excerpt:
> Kolmogorov-Arnold Networks (KANs) are promising alternatives of Multi-Layer Perceptrons (MLPs). KANs have strong mathematical foundations just like MLPs: MLPs are based on the universal approximation theorem, while KANs are based on Kolmogorov-Arnold representation theorem. KANs and MLPs are dual: KANs have activation functions on edges, while MLPs have activation functions on nodes. This simple change makes KANs better (sometimes much better!) than MLPs in terms of both model accuracy and interpretability.

https://github.com/KindXiaoming/pykan


⌚ Visiting the past with Time Machine GPT!

We are all familiar with the concept of a suite of models being a series of variants of a certain model that differ mainly in size. For example, Llama-2 7B, Llama-2 13B, Llama-2 70B

But this is not always the case. Researchers from The University of Oxford, The Alan Turing Institute, and The University of Manchester introduced TimeMachineGPT (TiMaGPT), a suite of language models that were pretrained on data constrained by a certain period in time. Instead of various sizes of the model, you get the same model but trained on different data coming from different times.

Using a GPT-2 model architecture with 117 million parameters, they trained 12 different models on Wikipedia and WMT News from 2011 to 2022 with each year represented by a model. For example, TiMaGPT-2011, TiMaGPT-2012, ..., TiMaGPT-2022.

🤔 But how could these models be useful?

They can be very useful. For example:

1⃣ Most language models are static in the sense that they are trapped in the time bubble of their pretraining data, their knowledge is limited by the cut-off date of their training dataset. In order to update their knowledge, Temporal Adaptation can be performed, which means further training on newer data. The TiMaGPT series of models can be used to study the limitations of Temporal Adaptation of language models.

2⃣ Word meaning can change not only with its context but also with its time of use and there is a large amount of research that focuses on understanding how embeddings shift through time. TiMaGPT will be very helpful in studying this phenomenon.

3⃣ One more use case in the context of Time-series forecasting and event prediction is "backtesting". Which is using historical data to evaluate new models for forecasting the future. Models like TiMaGPT (each living in its own time without any knowledge of the future/present) will be great for such a use case.

🤗 All models and datasets are on the hub: https://huggingface.co/Ti-Ma

samusenps monsoon-nlp maywell JackCloudman adamelliotfields


🚀 just reached 3K+ readers on this blog post about RAG using only HF🤗 related tools in just a little over 1 week from publishing.

📃the most interesting thing about it is that you can use the FAISS index in the datasets library to retrieve your most similar documents.

🔗https://huggingface.co/blog/not-lain/rag-chatbot-using-llama3

Happy reading everyone ✨


𝗔𝗯𝗼𝘂𝘁 𝘁𝗵𝗲 𝗧𝗧𝗦 𝗔𝗿𝗲𝗻𝗮

𝗔𝗯𝗼𝘂𝘁 𝘁𝗵𝗲 𝗧𝗧𝗦 𝗔𝗿𝗲𝗻𝗮

𝗢𝗽𝗲𝗻𝗩𝗼𝗶𝗰𝗲 𝗩𝟮

𝗢𝗽𝗲𝗻𝗩𝗼𝗶𝗰𝗲 𝗩𝟮

𝗖𝗼𝗺𝗽𝗮𝗿𝗲 𝘁𝗵𝗲 𝗺𝗼𝗱𝗲𝗹𝘀 𝗼𝗻 𝘁𝗵𝗲 𝗧𝗧𝗦 𝗔𝗿𝗲𝗻𝗮:

𝗖𝗼𝗺𝗽𝗮𝗿𝗲 𝘁𝗵𝗲 𝗺𝗼𝗱𝗲𝗹𝘀 𝗼𝗻 𝘁𝗵𝗲 𝗧𝗧𝗦 𝗔𝗿𝗲𝗻𝗮:

Excited to launch two new SOTA text-to-speech models on the TTS Arena:

- OpenVoice V2
- Play.HT 2.0

𝗔𝗯𝗼𝘂𝘁 𝘁𝗵𝗲 𝗧𝗧𝗦 𝗔𝗿𝗲𝗻𝗮

The TTS Arena is an open sourced Arena where you can enter a prompt, have two models generate speech, and vote on which one is superior.

We compile the results from the votes into a automatically updated leaderboard to allow developers to select the best model.

We've already included models such as ElevenLabs, XTTS, StyleTTS 2, and MetaVoice. The more votes we collect, the sooner we'll be able to show these new models on the leaderboard and compare them!

𝗢𝗽𝗲𝗻𝗩𝗼𝗶𝗰𝗲 𝗩𝟮

OpenVoice V2 is an open-sourced speech synthesis model created by MyShell AI that supports instant zero-shot voice cloning. It's the next generation of OpenVoice, and is fully open-sourced under the MIT license.
https://github.com/myshell-ai/OpenVoice

𝗣𝗹𝗮𝘆.𝗛𝗧 𝟮.𝟬

Play․HT 2.0 is a high-quality proprietary text-to-speech engine. Accessible through their API, this model supports zero-shot voice cloning.

𝗖𝗼𝗺𝗽𝗮𝗿𝗲 𝘁𝗵𝗲 𝗺𝗼𝗱𝗲𝗹𝘀 𝗼𝗻 𝘁𝗵𝗲 𝗧𝗧𝗦 𝗔𝗿𝗲𝗻𝗮:

https://huggingface.co/spaces/TTS-AGI/TTS-Arena


Replacing Judges with Juries: Evaluating LLM Generations with a Panel of
  Diverse Models (2404.18796)

As part of the Data is Better Together MPEP project, we are now at the point where some translation efforts have successfully translated 500 highly ranked prompts into a new target language (amazing work from @Rijgersberg et al!)

Our next step is to use these translated prompts to evaluate the performance of LLMs for non English languages.

Does LLM, as a judge, work outside of English?

Ideally, it would be compelling to leverage LLMs to judge models for non-English since this significantly lowers the barrier to evaluating models (although it doesn't remove this barrier altogether).

What we want to know is:
- does auto/LLM eval work in general for a particular language
- which model(s) works best as a judge
- do LLMs' judgments of non-English models match human preferences?

We're starting to think about how to approach this. If you have any ideas of possible approaches feel free to comment or join the discussion here: https://github.com/huggingface/data-is-better-together/issues/61

Other ideas...

Could an approach like https://huggingface.co/papers/2404.18796 with the SOA models for a particular language work? i.e., choose 4 of the best open LLMs for Arabic and use those at the pool of raters rather than relying on one powerful judge LLM?

NePe kristaller486 mmhamdy Rijgersberg alvarobartt


It’s exciting to see Apple’s commitment to opensource AI research lately. From a new awesome machine learning framework (mlx) to a family of purely open models (openELM) and incredibly visionary papers (LLMs in a flash, MM1) not mention the vibrant OSS community behind mlx - All alpha signs of something huge dropping in this year’s #AppleEvent & #WWDC

victor onandon adorkin julien-c alielfilali01 luancloud


HyperSD released the 8-steps CFG-preserved LoRA just now, may be the first acceleration plugin that preserves the original CFG for both SDXL and SD15, you can adjust your negative prompts now!!!
Hyper-SDXL-8steps-CFG-LoRA: https://huggingface.co/ByteDance/Hyper-SD/blob/main/Hyper-SDXL-8steps-CFG-lora.safetensors
Hyper-SD15-8steps-CFG-LoRA: https://huggingface.co/ByteDance/Hyper-SD/blob/main/Hyper-SD15-8steps-CFG-lora.safetensors

YaTharThShaRma999 victor radames OzzyGT multimodalart


OpenELM in Core ML

Apple recently released a set of efficient LLMs in sizes varying between 270M and 3B parameters. Their quality, according to benchmarks, is similar to OLMo models of comparable size, but they required half the pre-training tokens because they use layer-wise scaling, where the number of attention heads increases in deeper layers.

I converted these models to Core ML, for use on Apple Silicon, using this script: https://gist.github.com/pcuenca/23cd08443460bc90854e2a6f0f575084. The converted models were uploaded to this community in the Hub for anyone that wants to integrate inside their apps: https://huggingface.co/collections/corenet-community/openelm-core-ml-6630c6b19268a5d878cfd194

The conversion was done with the following parameters:
- Precision: float32.
- Sequence length: fixed to 128.

With swift-transformers (https://github.com/huggingface/swift-transformers), I'm getting about 56 tok/s with the 270M on my M1 Max, and 6.5 with the largest 3B model. These speeds could be improved by converting to `float16`. However, there's some precision loss somewhere and generation doesn't work in `float16` mode yet. I'm looking into this and will keep you posted! Or take a look at this issue if you'd like to help: https://github.com/huggingface/swift-transformers/issues/95

I'm also looking at optimizing inference using an experimental kv cache in swift-transformers. It's a bit tricky because the layers have varying number of attention heads, but I'm curious to see how much this feature can accelerate performance in this model family :)

Regarding the instruct fine-tuned models, I don't know the chat template that was used. The models use the Llama 2 tokenizer, but the Llama 2 chat template, or the default Alignment Handbook one that was used to train, are not recognized. Any ideas on this welcome!

ZachNagengast brightbit reach-vb victor radames JimRWallace NickyNicky 1duo HelloCephalopod not-lain julien-c MathisDevFP maxrubin629 michellenicole Rainierraoul anthonymikinka toddmath irotem98 velyan

reach-vb JimRWallace SuperL3D HelloCephalopod not-lain azhka MathisDevFP toddmath velyan

reach-vb HelloCephalopod not-lain MathisDevFP Norod78 0xjorgev


Model                                             / Avg. (pass@3)

Model                                             / Avg. (pass@3)

Claude 3 Haiku                          / 27.67

Claude 3 Haiku                          / 27.67

PECC: Problem Extraction and Coding Challenges (2404.18766)

How Robust Is Your Model in Complex Code Generation Tasks? 🤔

We've launched the PECC benchmark to challenge chat models in code generation, drawing from the Advent of Code for programming tasks and the Euler Project for math-heavy challenges. This new task tests models with problems presented in both detailed prose and concise "leet code" styles, evaluating their ability to understand and solve complex coding issues and math problem in chat-based interactions.

It seems that the Claude 3 models outperforme ChatGPT:
Model                                             / Avg. (pass@3)
Claude 3 Haiku                          / 27.67
GPT-3.5-Turbo                            / 23.75
Mixtral-8x22B-Instruct-v0.1  / 8.35

Read our Preprint📃: https://huggingface.co/papers/2404.18766
Look at the dataset🔎: https://huggingface.co/datasets/PatrickHaller/pecc

We also got accepted at LREC-COLING '24  🎉


Introducing JARVIS Tony's voice assistant for You.

JARVIS responds to all your questions in audio format.
Must TRY -> https://huggingface.co/spaces/KingNish/JARVIS

Jarvis is currently equipped to accept text input and provide audio output.
In the future, it may also support audio input.

DEMO Video:

victor sebastianking SvCy DamarJati Ezi Marc-Gloaguen julien-c


I've built a custom component that integrates Rerun web viewer with Gradio, making it easier to share your demos as Gradio apps.

Basic snippet
```
# pip install gradio_rerun gradio
import gradio as gr
from gradio_rerun import Rerun

gr.Interface(
    inputs=gr.File(file_count="multiple", type="filepath"),
    outputs=Rerun(height=900),
    fn=lambda file_path: file_path,
).launch()
```
More details here https://huggingface.co/spaces/radames/gradio_rerun
Source https://github.com/radames/gradio-rerun-viewer

Follow Rerun here https://huggingface.co/rerun

louisbrulenaudet AlekseiPravdin andreasnaoum Tyfo87


💰❌ 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡 𝐟𝐨𝐫 𝐭𝐡𝐞 𝐯𝐞𝐫𝐲 𝐆𝐏𝐔 𝐏𝐨𝐨𝐫 - 𝐒𝐜𝐚𝐥𝐢𝐧𝐠 𝐥𝐚𝐰𝐬 𝐫𝐞𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧

🎆 Good news: 𝘆𝗼𝘂 𝗰𝗮𝗻 𝗱𝗼 𝗰𝘂𝘁𝘁𝗶𝗻𝗴-𝗲𝗱𝗴𝗲 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝘄𝗶𝘁𝗵 𝗮 𝗰𝗮𝗹𝗰𝘂𝗹𝗮𝘁𝗼𝗿 𝗮𝗻𝗱 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗣𝗮𝗶𝗻𝘁 𝟮𝟬𝟬𝟲!

The Chinchilla experiments (by Google DeepMind) ran hundreds of pre-trainings with models >1B parameters (I do not want to imagine how much that cost) to 𝗳𝗶𝗻𝗱 𝘁𝗵𝗲 𝗼𝗽𝘁𝗶𝗺𝗮𝗹 𝗿𝗮𝘁𝗶𝗼 𝗼𝗳 𝗺𝗼𝗱𝗲𝗹 𝘀𝗶𝘇𝗲 𝘃𝘀 𝘁𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝘁𝗼𝗸𝗲𝗻𝘀. Why is this question so important?
Well, you only ever have access to a fixed compute, counted in FLOPs (floating point operations). So if your model is bigger, you will have less compute to train on many tokens, and if you want to train on more tokens, your model will be smaller. When model trainings cost million, you absolutely need to get this right.

The new paper "Chinchilla Scaling: A replication attempt" by Epoch AI sets on on the ambitious goal of reproducing this.

But since the authors do not have infinite money, they decided to directly run their computations from DeepMind's own experiments! They took the figure from the last experiment (cf slide below), measured point positions, picked color codes, and ended up reconstructing the underlying data.

💥 They then just fit the scaling laws proposed by the Chinchilla Authors, but arrived at wildly different results! They find that as a rough rule of thumb, you should use 20 training tokens for each parameter in your model, instead of the 70 obtained in the original paper. They also point out inconsistencies in the paper, and unrealistically narrow confidence intervals.

➡ This only contradicts the results from the last (out of 3) experiments in the Chinchilla paper. And the model trained at the end of the Chinchilla paper still seems properly scaled.

✅ But it does show that a tiny bit more theoretical work can go a long way, especially given the huge financial costs that such an error can have!

chansung hllj maywell radames victor cstr

maywell radames louisbrulenaudet KingNish NotSiDDH


Adding a long prompt can help you fight LLM hallucinations. However, if you know exactly how you want your LLM output constrained, there are much better strategies! 💪

Did you know you can force your LLM to ALWAYS generate a valid JSON file? Or to follow a well-defined answer template? You can do that and more with the 🤗 transformers-compatible `outlines` library.

It doesn't only allow you to master your LLM -- your text generation application will also become faster! 🔥 The more constrained your text generation is, the bigger speedups you'll see!

Follow @remi and other `outlines` folks to stay on top of the constrained generation game 🧠


The hype is real: a mysterious gpt2-chatbot model has appeared on the LLM Arena Leaderboard 👀.
It seems to be at least on par with the top performing models (closed and open).

To try it out: https://chat.lmsys.org/ -> then click on the Direct Chat tab and select gpt2-chatbot.

 Take your bet, what do you think it is?

KingNish FreeHugsForRobots radames mvaloatto AlekseiPravdin alfredplpl Lin-Rexter icpro adumred not-lain

KingNish radames xiaotianhan louisbrulenaudet AlekseiPravdin PatrickHaller not-lain


Should media organizations strike deals with big tech companies? Here are two colliding news stories about licensing:

1. The Financial Times has secured a licensing agreement with OpenAI to license its material both for training and queries on ChatGPT. It is the fifth such deal, following similar agreements with Associated Press, Axel Springer, Le Monde and Prisa Media. "Financial terms were not disclosed."

"Apart from the benefits to the FT, there are broader implications for the industry. It’s right, of course, that AI platforms pay publishers for the use of their material. OpenAI understands the importance of transparency, attribution, and compensation – all essential for us."

2. Meanwhile, French media outlet Mediapart is refusing to cash in money from Google, which it is entitled to under so-called "neighbouring rights" for the right to display their news content online.

Why? Due to issues with disclosing financial terms: "The confidentiality clauses imposed by Google today prevent us from publicizing to our readers not only the total amount paid, but also the amount Mediapart is entitled to receive."

"In our view, financial dependence on platforms is incompatible with our public service mission, which is to make the powerful face up to their responsibilities. It also seems extremely dangerous economically."

Two positions at opposite sides of the spectrum.

- The Financial Times and OpenAI strike content licensing deal
https://www.ft.com/content/33328743-ba3b-470f-a2e3-f41c3a366613

- Droits voisins : Mediapart lance la bataille de la transparence contre Google (in French) https


Am I the only one who think command-r-+ is a better daily Assistant than ChatGPT-4? (and it's not even close :D)

KingNish jgitsolutions fdaudens radames not-lain WaveCut Alagib Rybens DamarJati Sachiii84


#ICLR 2024 is almost there 🔥🔥🔥 counting the days to be again in the beautiful city of Vienna participating in the The Twelfth International Conference on Learning Representations, hope to see many of the Hugging Face comunity there!

I would like to contribute 🎁 by releasing the second Knowledge Vault, with 100 lectures visualized from the last 10 years of ICLR from 2014 to 2023, including knowledge graphs for all the Invited Lectures and some extras, with almost 3000 topics represented. (Of course using several AI tools including Llama3)

You can explore it here:
🌏 https://theendofknowledge.com/Vaults/2/ICLR2014-2023.html

And you can learn more about the Vaults here:
📝https://www.linkedin.com/pulse/knowledge-vaults-david-vivancos-lbjef/

Hope you like the Knowledge Vault!

victor notoookay AlekseiPravdin damerajee


If you are into video generation you should check this great Space by @KingNish - generate videos in ~10 seconds!

https://huggingface.co/spaces/KingNish/Instant-Video


https://arxiv.org/pdf/2402.06852
Technical report for ChemLLM 1.5 released.🤗


🦢 The SWIM-IR dataset contains 29 million text-retrieval training pairs across 27 diverse languages. It is one of the largest synthetic multilingual datasets generated using PaLM 2 on Wikipedia! 🔥🔥

SWIM-IR dataset contains three subsets :
- Cross-lingual:`nthakur/swim-ir-cross-lingual`
- Monolingual: `nthakur/swim-ir-monolingual`
- Indic Cross-lingual: `nthakur/indic-swim-ir-cross-lingual`

Check it out:
https://huggingface.co/collections/nthakur/swim-ir-dataset-662ddaecfc20896bf14dd9b7


🔥 Transfer model's Chat feature, Context length and Knowledge to another under 1 minute without any train.

Imagine being able to create chat models, expand context, and transfer domain-specific knowledge to models, all within a matter of minutes. Our innovative approach, based on a combination of diff-based techniques and sigmoid ratio calculations, makes this possible.

By considering the diffs between the desired information model (long context or chat) and the base model, as well as the diffs between the base model and the target model, we can efficiently transfer features and expand context without the need for extensive training or resources.

Our method minimizes model degradation and ensures that only the desired information is captured, resulting in high-quality models that can be created with just a single click. Whether you need a chat model, expanded context, or domain-specific knowledge transfer, our approach offers a rapid and effective solution.

In blog post below, we will dive into the details of our method, provide code examples, and showcase the impressive results achieved using our approach. Get ready to revolutionize your model creation process and unlock new possibilities with this powerful technique.

Blog - https://huggingface.co/blog/maywell/llm-feature-transfer

maywell IHaBiS lunarflu Unggi beomi victor julien-c FengJ DeathGodlike hivaze kaki-paper hiauiarau

IHaBiS lunarflu beomi celarlin scoop777 sappho192 kaki-paper


Soon new releases on NeverSleep 👀
8B/70B Llama3 RP fine-tune in the work!

IkariDev Ainonake saishf xxx777xxxASD bluuwhale Andrewwwwww joujiboi lunarflu Nacholmo antiven0m EveryPizza Blobbinski ZootZootTesla beberik victor WesPro kleberbaum den0620 TeoR4eg Tillx85 HailJebus ABX-AI

ggnick Ainonake Andrewwwwww Th3Nomad lunarflu s0lu Meggido ZootZootTesla bharathchalla MrHillsss den0620 Kenshiro-28 ABX-AI


I created a Twitter account a while back. I finally decided to make it public SebastianG74019. For those of you following @Locutusque on Twitter, that is not me! 😂


VTuber Logo Generator❤🪄⭐ by @gojiteji
https://huggingface.co/spaces/gojiteji/VTuberLogoGenerator
How this works:
- mistralai/Mixtral-8x7B-Instruct-v0.1 for Japanese transliteration.
- Stable Diffusion 3 for logo generation.
- simple k-means for color selection.

KvrParaskevi dillfrescott lunarflu grx96 victor julien-c willowill5 pabloce not-lain

coderjuzi dillfrescott lunarflu not-lain


New update to mlx-rag-gguf:
- mlx supported phi-3-mini-4k gguf  weight.
- support for other gguf weights (llama arch) 4 & 8 bits quantized.
repo: https://github.com/Jaykef/mlx-rag-gguf
model https://huggingface.co/Jaward/phi-3-mini-4k-instruct.Q4_0.gguf


zaursamedov1 dgallitelli ameerazam08 gojiteji svjack lunarflu victor Yasmanyel3men2 not-lain danielus


LayerSkip: Enabling Early Exit Inference and Self-Speculative Decoding (2404.16710)

Layer Skip

Enabling Early Exit Inference and Self-Speculative Decoding

https://huggingface.co/papers/2404.16710

We present LayerSkip, an end-to-end solution to speed-up inference of large language models (LLMs). First, during training we apply layer dropout, with low dropout rates for earlier layers and higher dropout rates for later layers, and an early exit loss where all transformer layers share the same exit. Second, during inference, we show that this training recipe increases the accuracy of early exit at earlier layers, without adding any auxiliary layers or modules to the model. Third, we present a novel self-speculative decoding solution where we exit at early layers and verify and correct with remaining layers of the model. Our proposed self-speculative decoding approach has less memory footprint than other speculative decoding approaches and benefits from shared compute and activations of the draft and verification stages. We run experiments on different Llama model sizes on different types of training: pretraining from scratch, continual pretraining, finetuning on specific data domain, and finetuning on specific task. We implement our inference solution and show speedups of up to 2.16x on summarization for CNN/DM documents, 1.82x on coding, and 2.0x on TOPv2 semantic parsing task.

Squadgroup1234 Chunte gojiteji samadpls yizhilll travisking QuocKhanh vincentmichael089


Wow i can post on HF now!
Love HF so much 🤗❤

Chunte gojiteji Moibe julien-c not-lain Tonic SvCy DavidGF


Explore the Latest Top Papers with Papers Leaderboard!
We are excited to introduce a new way to explore the most impactful research papers: Papers Leaderboard! This feature allows you to easily find the most talked-about papers across a variety of fields.
Hf-demo : https://huggingface.co/spaces/ameerazam08/Paper-LeaderBoard
Happy weekends!


Yay we got 500K+ monthly HF downloads on our Unsloth HF repo! :) Super appreciate everyone in the OSS community - and thanks for using Unsloth!!

clem giux78 lunarflu nbroad alielfilali01 Kukedlc Oscar007 Dlbk Chunte catastropiyush AtAndDev julien-c duxx damerajee feeltheAGI beomi

clem lunarflu fredericmenezes samusenps nbroad alielfilali01 Kukedlc Chunte AtAndDev f0ster julien-c louisbrulenaudet damerajee aloobun beomi

Kukedlc Oscar007 AtAndDev damerajee Ji-Xiang beomi


clem lunarflu samusenps Dlbk julien-c not-lain zaursamedov1 GPT007


Glad to see Idefics2 making its way into the awesome OpenVLM Leaderboard which ranks VLMs. 🏆
2nd in its category (<10B parameters and open weights)!

While InternLM-XComposer2 uses proprietary data, Idefics2 is built solely using openly available data.

Leaderboard: https://huggingface.co/spaces/opencompass/open_vlm_leaderboard
Model: https://huggingface.co/HuggingFaceM4/idefics2-8b

julien-c yjernite lunarflu not-lain Cuiunbo Dlbk fdaudens lewtun Narsil London12345 kaifahmad orrzohar


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 !


Why is the Adam Optimizer so good? Simple, because it will never find the absolute most optimal solution. That is a design feature, not a flaw. This is why no other optimizer comes close in terms of generalizable use. Want to learn more about this entire process and exactly what I am talking about? I break all of this down in very simple terms in this video! https://youtu.be/B9lMONNngGM

https://youtu.be/B9lMONNngGM


Microsoft released a groundbreaking model that can be used for web automation, with MIT license 🔥 https://huggingface.co/microsoft/OmniParser

Interesting highlight for me was Mind2Web (a benchmark for web navigation) capabilities of the model, which unlocks agentic behavior for RPA agents.

no need for hefty web automation pipelines that get broken when the website/app design changes! Amazing work.

Lastly, the authors also fine-tune this model on open-set detection for interactable regions and see if they can use it as a plug-in for VLMs and it actually outperforms off-the-shelf open-set detectors like GroundingDINO. 👏

OmniParser is a state-of-the-art UI parsing/understanding model that outperforms GPT4V in parsing.

adorkin YaTharThShaRma999 cstr theospeak ucsahin adamelliotfields AtAndDev acidtib DivaVivy FredLaplagne ofirst

prithivMLmods YaTharThShaRma999 John6666 ucsahin Felladrin AtAndDev Csplk


Parents in the 1990: Teach the kids to code
Parents now: Teach the kids to fix the code when it starts walking around 🤖✨

luk158 not-lain yangstech julien-c theospeak nazimali xpgx1 sajjad112233 maywell Lewdiculous kaki-paper lopezjm96 super-cinnamon RoversX Chief-Inspector Ailokis Chehong

John6666 not-lain julien-c super-cinnamon den0620 OmbelineM

CaioXapelaum julien-c sajjad112233 Chehong


Unleashing Reasoning Capability of LLMs via Scalable Question Synthesis
  from Scratch (2410.18693)

📊 We present ScaleQuest-Math-1M, a mathematical reasoning dataset of 1 million high-quality question-answer pairs.
🔥 We propose ScaleQuest, a scalable and novel data synthesis method that utilizes small-size open-source models to generate questions from scratch.

Project Page: https://scalequest.github.io/
Dataset: https://huggingface.co/datasets/dyyyyyyyy/ScaleQuest-Math
Paper: https://huggingface.co/papers/2410.18693
HF Collection: https://huggingface.co/collections/dyyyyyyyy/scalequest-670a7dc2623c91990f28913b


🤯 Plot twist: Size isn't everything in AI! A lean 32B parameter model just showed up to the party and outperformed a 70B one. Efficiency > Scale? The AI world just got more interesting...

Cohere For AI released Aya Expanse, a new family of multilingual models (8B and 32B) spanning 23 popular languages.

Models: https://huggingface.co/collections/CohereForAI/c4ai-aya-expanse-671a83d6b2c07c692beab3c3
Blog post: https://huggingface.co/blog/aya-expanse
Demo: https://huggingface.co/spaces/CohereForAI/aya_expanse

clem John6666 teckytim juliantorr prithivMLmods thomwolf luk158 sugatoray alielfilali01 manred1997 Tonic not-lain theospeak


This is no Woodstock AI but will be fun nonetheless haha. I’ll be hosting a live workshop with team members next week about the Enterprise Hugging Face hub.

1,000 spots available first-come first serve with some surprises during the stream!

You can register and add to your calendar here: https://streamyard.com/watch/JS2jHsUP3NDM

nbroad adrienduf jeffboudier Julianvega2 alvarobartt MoritzLaurer lysandre Wauplin Violette kramp prithivMLmods Sylvestre brunatrevelin alielfilali01 fdaudens nicoism andrewrreed julien-c edwixx vtrubamacrocosmos pierrci

jeffboudier Julianvega2 lysandre Wauplin brunatrevelin andrewrreed julien-c nbroad

jeffboudier Julianvega2 lysandre Aurelien-Morgan julien-c


If you have ~300+ GB of V-RAM, you can run Mochi from @genmo

A SOTA model that dramatically closes the gap between closed and open video generation models.

Mochi 1 introduces revolutionary architecture featuring joint reasoning over 44,520 video tokens with full 3D attention. The model implements extended learnable rotary positional embeddings (RoPE) in three dimensions, with network-learned mixing frequencies for space and time axes.

The model incorporates cutting-edge improvements, including:
- SwiGLU feedforward layers
- Query-key normalization for enhanced stability
- Sandwich normalization for controlled internal activations

What is currently available?
The base model delivers impressive 480p video generation with exceptional motion quality and prompt adherence. Released under the Apache 2.0 license, it's freely available for both personal and commercial applications.

What's Coming?
Genmo has announced Mochi 1 HD, scheduled for release later this year, which will feature:
- Enhanced 720p resolution
- Improved motion fidelity
- Better handling of complex scene warping

clem akhaliq thomwolf sepal AtAndDev theospeak VDBLOI2024 abhi22 RaphaelLiu viktsys DivaVivy

sugatoray Aurelien-Morgan AtAndDev abhi22


October version of Claude 3.5 lifts SOTA (set by its June version) by 7 points.
https://huggingface.co/spaces/onekq-ai/WebApp1K-models-leaderboard

Closed sourced models are widening the gap again.

Note: Our frontier leaderboard now uses double test scenarios because the single-scenario test suit has been saturated.


Benchmarks!

I have lately been diving deep into the main benchmarks we all use to evaluate and compare models.

If you've never actually looked under the hood for how benchmarks work, check out the LM eval harness from EleutherAI: https://github.com/EleutherAI/lm-evaluation-harness

+ check out the benchmark datasets, you can find the ones for the LLM leaderboard on the about tab here: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard, then click the dataset and actually peak at the data that comprises these benchmarks.

It feels to me like benchmarks only represent a tiny portion of what we actually use and want LLMs for, and I doubt I'm alone in that sentiment.

Beyond this, the actual evaluations of responses from models are extremely strict and often use even rudimentary NLP techniques when, at this point, we have LLMs themselves that are more than capable at evaluating and scoring responses.

It feels like we've made great strides in the quality of LLMs themselves, but almost no change in the quality of how we benchmark.

If you have any ideas for how benchmarks could be a better assessment of an LLM, or know of good research papers that tackle this challenge, please share!

julien-c Dlbk lewtun dillfrescott mmhamdy Noomam welkson


So hard to keep up with pace!!! Lots of new Chinese fine-tunes are being released on  HF

So I asked my agent to create a collection
https://huggingface.co/collections/xianbao/llama3-zh-662ba8503bdfe51948a28403

code: https://colab.research.google.com/drive/1ap6fP-VytZE367Nqk26DeQqgQkYaf-cD#scrollTo=eljRbYb4c92M

Would be nice to run then regularly. Any thoughts / suggestions on where to host this cron job?

victor jharshraj Jaye13 Dlbk fdaudens svjack Tonic


How do Microsoft and Alphabet (Google) results compare?

Microsoft Reports Rising Revenues as A.I. Investments Bear Fruit
- 17 % jump in revenue and a 20 % increase in profit for the first three months of the year.
- Revenue was $61.9 billion, up from $52.9 billion a year earlier.
- Profit hit $21.9 billion, up from $18.3 billion.
- More than a fifth of that growth came from its generative A.I. services
https://www.nytimes.com/2024/04/25/technology/microsoft-earnings.html

Alphabet’s Revenue Jumps 15% to $80.5 Billion
- $80.5 billion in quarterly sales, up 15 % from a year earlier. Profit climbed 36 % to $23.7 billion.
- For the first time, a dividend of 20 cents per share
- It spent $12 billion on capital expenditures in the first quarter, soaring 91 % from a year earlier.
https://www.nytimes.com/2024/04/25/technology/alphabet-earnings.html

Meta’s Open Source Llama 3 Is Already Nipping at OpenAI’s Heels - Wired
- "if open source models prove competitive, developers and entrepreneurs may decide to stop paying to access the latest model from OpenAI or Google and use Llama 3 or one of the other increasingly powerful open source models that are popping up."
- "Open models appear to be dropping at an impressive clip."
https://www.wired.com/story/metas-open-source-llama-3-nipping-at-openais-heels/


Announcing that we are on our way to solve a long standing issue of document processing: correction of OCR mistakes. Pleias publishes the largest dataset to date with automated OCR correction, 1 billion words in English, French, German and Italian.

OCR quality is long-standing issue of digitization. Cultural heritage texts are especially concerned due to the primary sources being old documents (with many artifacts, blots, degradation) and to the limitation of OCR technology for historical scripts. When we released Common Corpus, a 500 Billion words corpus in the public domain, this was the primary criticism.

Recent breakthrough in post-OCR correction has been made possible thanks to progress in open LLM research and several months of dedicated training and alignment by Pleias as well as the HPC resources from GENCI–IDRIS (Grant 2023-AD011014736) on Jean-Zay.

Announcement: https://huggingface.co/blog/Pclanglais/post-ocr-correction

Post-OCR-Correction dataset: https://huggingface.co/datasets/PleIAs/Post-OCR-Correction

fdaudens davanstrien andrewrreed jharshraj Dlbk AkimfromParis louisbrulenaudet mexicanamerican VictorSanh victor Molbap clem J-Hansen LucasThil


Татар теле көне белән!

Татар теле көне белән!

Today, April 26, is the Day of the Tatar Language! 🌟
To celebrate, we release our new language model, Tweety Tatar 🐣

https://huggingface.co/Tweeties/tweety-tatar-base-7b-2024-v1

The model was converted from Mistral Instruct v0.2 using a novel technique called trans-tokenization. As a result, the model uses a brand-new tokenizer, fully tailored for the Tatar language.

We also release a model which can be finetuned for translation of English or Russian into Tatar, and achieves a performance similar to commercial offerings:

https://huggingface.co/Tweeties/tweety-tatar-hydra-base-7b-2024-v1

More details in our upcoming paper 👀
François REMY, Pieter Delobelle, Alfiya Khabibullina

Татар теле көне белән!

justalphie pdelobelle monsoon-nlp murat Moibe IPSAN midwestcyr


Finally, Qwen1.5-110B is out! With weights and demo!

Blog: https://qwenlm.github.io/blog/qwen1.5-110b/
Demo: https://huggingface.co/spaces/Qwen/Qwen1.5-110B-Chat-demo
Base: https://huggingface.co/Qwen/Qwen1.5-110B
Chat: https://huggingface.co/Qwen/Qwen1.5-110B-Chat

This model has some specific features:
* GQA
* 32K token context length
* Multilingual support

We feel good about its performance on benchmarks, including those for base models and chat models, but we still need more of your testing and feedback to help us know its capabilities and limitations!

Additionally, the base model has not learned chatml tokens. Yeah if you use chatml format, you need to be careful about it!

Enjoy and stay tuned for Qwen2!

louisbrulenaudet AdinaY hiyouga tomaarsen xianbao lunarflu martineden Chunte lewtun svjack

AdinaY hiyouga tomaarsen fdaudens xianbao lunarflu Chunte lewtun


ConsistentID: Portrait Generation with Multimodal Fine-Grained Identity
  Preserving (2404.16771)

🚀🎭🔥 New Research Alert (Avatars Collection)! 🔥🎭🚀
📄 Title: ConsistentID: Portrait Generation with Multimodal Fine-Grained Identity Preserving 🔝

📝 Description: ConsistentID is a novel portrait generation method that preserves the fine-grained identity of a single reference image.

👥 Authors: Jiehui Huang et al.

🔗 Paper: https://huggingface.co/papers/2404.16771

🌐 Github Page: https://ssugarwh.github.io/consistentid.github.io/
📁 Repository: https://github.com/JackAILab/ConsistentID

📚 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 Avatars Collection: https://huggingface.co/collections/DmitryRyumin/avatars-65df37cdf81fec13d4dbac36

🔍 Keywords: #ConsistentID #PortraitGeneration #IdentityPreservation #FineGrainedControl #ImageSynthesis #GenerativeModels  #ComputerVision #DeepLearning


🦙🦙 LLaMA Duo project update

Last time, I gave a brief introduction about LLaMA Duo project with @sayakpaul . It is a simple toolset to aligning sLLM with service LLM with coverage dataset 👉🏻 (https://huggingface.co/posts/chansung/708646454991943).
- coverage dataset is what we believe to be the most important/desired (instruction, response) pairs. In system thinking, each instruction could be an analogy of a function from traditional programming. We make unit tests and measure the coverage % for all the features/functions. Similarly, we need to ensure if our fine-tuned model could handle what % of given instructions from coverage dataset satisfactory (hence coverage dataset).

We have tested it with "Coding" category of data from https://huggingface.co/datasets/HuggingFaceH4/no_robots dataset. It has about 300 SFT training data points under Coding category. After fine-tuning Gemma 7B model on that, the result was very poor. LLaMA Duo's evaluation tool gave < 20% of metrics in similarity and preciseness on the test split.

So, we used LLaMA Duo's synthetic data generation tool to generate 60k data points that looks similar to the original dataset. We first created ~10k synthetic data points, then created 50k more based on the synthetic dataset itself.

After fine-tuning Gemma 7B on the 60k synthetic dataset, the evaluation result went up to 80~90% high. Also, when testing out the model in UI, it tends to give good responses.

It is a good showcase to transition from service LLM to sLLM or having a backup sLLM for service LLM failure scenarios. I am going to expand this experiments on all categories of no_robots dataset. It will roughly generate > 100k data points.

Here are some links:
- LLaMA Duo project repo: https://github.com/deep-diver/llamaduo
- 60k Coding synthetic dataset: https://huggingface.co/datasets/chansung/merged_ds_coding
- Fine-tuned Gemma 7B model: https://huggingface.co/chansung/coding_llamaduo_60k_v0.2

Tonic peterschmidt85 chansung lunarflu juyongjiang

hllj chansung lunarflu Hiraishin juyongjiang


CatLIP: CLIP-level Visual Recognition Accuracy with 2.7x Faster
  Pre-training on Web-scale Image-Text Data (2404.15653)

CatLIP

CLIP-level Visual Recognition Accuracy with 2.7x Faster Pre-training on Web-scale Image-Text Data

https://huggingface.co/papers/2404.15653

Contrastive learning has emerged as a transformative method for learning effective visual representations through the alignment of image and text embeddings. However, pairwise similarity computation in contrastive loss between image and text pairs poses computational challenges. This paper presents a novel weakly supervised pre-training of vision models on web-scale image-text data. The proposed method reframes pre-training on image-text data as a classification task. Consequently, it eliminates the need for pairwise similarity computations in contrastive loss, achieving a remarkable 2.7times acceleration in training speed compared to contrastive learning on web-scale data. Through extensive experiments spanning diverse vision tasks, including detection and segmentation, we demonstrate that the proposed method maintains high representation quality.


Updated the https://huggingface.co/datasets/vikhyatk/lnqa dataset to include images, so you no longer need to separately download them from OpenImages!

beomi clem akashicmarga lunarflu catastropiyush