kivanolai AtAndDev mrlive02 dillfrescott


New adapt.s for dev  🍔

 Hosted -> https://huggingface.co/spaces/prithivMLmods/FLUX-LoRA-DLC

✨Teen Outfit: https://huggingface.co/prithivMLmods/Teen-Outfit
✨Dark Pink: https://huggingface.co/prithivMLmods/Dark-Thing-Flux-LoRA
✨Shadow Projection: https://huggingface.co/prithivMLmods/Shadow-Projection-Flux-LoRA
✨Abstract Cartoon: https://huggingface.co/prithivMLmods/Abstract-Cartoon-Flux-LoRA
✨Street Bokeh: https://huggingface.co/prithivMLmods/Street-Bokeh-Flux-LoRA
✨Fine Detailed: https://huggingface.co/prithivMLmods/Flux-Realism-FineDetailed
✨Bold Shadows: https://huggingface.co/prithivMLmods/Bold-Shadows-Flux-LoRA
✨Yellow Laser: https://huggingface.co/prithivMLmods/Yellow-Laser-Flux-LoRA

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🎉LoRA Collection: https://huggingface.co/collections/prithivMLmods/flux-lora-collections-66dd5908be2206cfaa8519be

🎉LoRA Spaces: https://huggingface.co/collections/prithivMLmods/lora-space-collections-6714b72e0d49e1c97fbd6a32

------------
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.
.@prithivMLmods  🤗


Sharing a new space to test out ViTPose, a pose estimation model using Visual Transformers.

[ViTPose Playground](https://huggingface.co/spaces/Dref360/vit_pose_playground)

This model will be available in `transformers` once [#30530](https://github.com/huggingface/transformers/pull/30530) is merged. Huge shoutout to @nielsr and @danelcsb for bringing this to HF!

Here's the result on my Ken Halloween costume.


🤯 AI progress keeps blowing my mind! Just experienced Qwen's new Coder demo - built a complete flashcard web app with a single prompt. The results are incredible!

This demo is part of the new Qwen2.5 Coder family (0.5B to 32B models), surpassing/matching GPT4o and Claude Sonnet 3.5 across multiple coding benchmarks.

- 128K context window for 14B/32B models
- Drop-in replacement for GPT-4 in Cursor & Artifacts
- Models on the Hub under Apache 2.0 license

🔗 Try it yourself: https://huggingface.co/spaces/Qwen/Qwen2.5-Coder-Artifacts

This is democratization of coding in real-time. Excited to see AI tools becoming more capable and accessible.

What would you build with this? Share your ideas below! 👇

#AI #Programming #TechInnovation #OpenSource #SoftwareDevelopment


🎵 Introducing Suno Music Generation Dataset - https://huggingface.co/datasets/nyuuzyou/suno

Dataset highlights:

- 659,788 AI-generated music samples with comprehensive metadata from suno.com
- Multilingual content with English as primary language, including Japanese and other languages
- Each entry contains rich metadata including:
  - Unique song ID, audio/video URLs, and thumbnail images
  - AI model version and generation parameters
  - Song metadata (tags, prompts, duration)
  - Creator information and engagement metrics
- Released to the public domain under Creative Commons Zero (CC0) license

The dataset structure includes detailed information about each generated piece, from technical parameters to user engagement metrics, making it particularly valuable for:
- Music generation model training
- Cross-modal analysis (text-to-audio relationships)
- User engagement studies
- Audio classification tasks
- Music style and genre analysis


𝗤𝘄𝗲𝗻𝟮.𝟱-𝗖𝗼𝗱𝗲𝗿-𝟯𝟮𝗕: 𝗻𝗲𝘄 𝗯𝗲𝘀𝘁-𝗶𝗻-𝗰𝗹𝗮𝘀𝘀 𝗼𝗽𝗲𝗻 𝗰𝗼𝗱𝗶𝗻𝗴 𝗺𝗼𝗱𝗲𝗹, 𝗯𝗲𝗮𝘁𝘀 𝗚𝗣𝗧-𝟰𝗼 𝗼𝗻 𝗺𝗼𝘀𝘁 𝗰𝗼𝗱𝗶𝗻𝗴 𝗯𝗲𝗻𝗰𝗵𝗺𝗮𝗿𝗸𝘀!💥

💪 It's the first time Open-Source coding model of this size class that clearly matches GPT-4o's coding capabilities!

✨ Completes the previous two Qwen 2.5 Coder release with 4 new size: 0.5B, 3B, 14B, 32B
📚 Support long context up to 128K (for the 14B and 32B models)
✅ Drop-in replacement to GPT-4o as a coding assistant on Cursor or for Artifacts!
🤗 Models available right now on the Hub, under Apache 2.0 license!

They have setup a crazy Artifacts demo, you should go have a look!
👉 https://huggingface.co/spaces/Qwen/Qwen2.5-Coder-Artifacts

Nymbo Norm gatorand AtAndDev apeSh1t aliyansayz splevine wajeehulhassan yaaaaam1 louisbrulenaudet xi0v Chroma111


𝗔𝗿𝗲 𝘀𝗰𝗮𝗹𝗶𝗻𝗴 𝗹𝗮𝘄𝘀 𝗼𝘃𝗲𝗿? 𝗔 𝗿𝗲𝗽𝗼𝗿𝘁 𝗳𝗿𝗼𝗺 𝘁𝗵𝗲 𝗜𝗻𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 𝗮𝗻𝗻𝗼𝘂𝗻𝗰𝗲𝗱 𝘁𝗵𝗮𝘁 𝗢𝗽𝗲𝗻𝗔𝗜 𝗶𝘀 𝘀𝗲𝗲𝗶𝗻𝗴 𝗱𝗶𝗺𝗶𝗻𝗶𝘀𝗵𝗶𝗻𝗴 𝗿𝗲𝘁𝘂𝗿𝗻𝘀 𝗳𝗿𝗼𝗺 𝘀𝗰𝗮𝗹𝗶𝗻𝗴 𝘂𝗽 𝘁𝗵𝗲 𝗻𝗲𝘅𝘁 𝗚𝗣𝗧 𝗺𝗼𝗱𝗲𝗹𝘀.

📊 What are scaling laws? These are empiric laws that say "Every time you increase compute spent in training 10-fold, your LLM's performance will go up by a predictable tick". Of course, they apply only if you train your model with the right methods.

The image below illustrates it: they're from a paper by Google, "Scaling Autoregressive Models for Content-Rich Text-to-Image Generation", and they show how quality and instruction following of models improve when you scale the model up (which is equivalent to scaling up the compute spent in training).

➡ These scaling laws have immense impact: they triggered the largest gold rush ever, with companies pouring billions into scaling up theiur training. Microsoft and OpenAI spent 100B into their "Startgate" mega training cluster, due to start running in 2028.

🤔 So, what about these reports of scaling laws slowing down?

If they are true, they would mean a gigantic paradigm shift, as the hundreds of billions poured by AI companies into scaling could be a dead-end. ⛔

But I doubt it: until the most recent publications, scaling laws showed no signs of weakness, and the researchers at the higher end of the scale-up seems to imply the scaling up continues.

Wait and see!


Best Debug Prompt

You are a frustrated user who has tested this application extensively. Your job is to list EVERY possible way this app could completely break or become unusable.

For each potential failure:

1. What would make you say "This app is totally broken!"?
2. What exact steps did you take when it broke?
3. What did you see on your screen when it broke?
4. How angry would this make a typical user (1-10)?
5. What would you expect the app to do instead?

Think about:
- What happens if you click buttons really fast?
- What if your internet is slow/disconnected?
- What if you upload weird files/images?
- What if you try to break the app on purpose?
- What if multiple people use it at once?
- What if you use it on mobile/tablet?
- What if you refresh/navigate while it's working?
- What if you paste invalid inputs?
- What if you upload HUGE files?
- What if you leave it running overnight?

Don't worry about being technical - just describe what you saw break as a user.

Format each issue like:

ISSUE #1: [Brief angry user description]
- STEPS TO BREAK IT: [Exactly what you did]
- WHAT HAPPENED: [What you saw]
- ANGER LEVEL: [1-10]
- EXPECTED: [What should happen]

Keep going until you've found every possible way to break this app from a user's perspective!

After outpuiting the list, accoring to the list  optmiced Composer edit block to fix the ones severe that make sense to adjust accoirng to gradio limitations and current usage target )dont suppose we need unecessary funcitons)


I just released Sentence Transformers v3.3.0 & it's huge! 4.5x speedup for CPU with OpenVINO int8 static quantization, training with prompts for a free perf. boost, PEFT integration, evaluation on NanoBEIR, and more! Details:

1. We integrate Post-Training Static Quantization using OpenVINO, a very efficient solution for CPUs that processes 4.78x as many texts per second on average, while only hurting performance by 0.36% on average. There's a new `export_static_quantized_openvino_model` method to quantize a model.

2. We add the option to train with prompts, e.g. strings like "query: ", "search_document: " or "Represent this sentence for searching relevant passages: ". It's as simple as using the `prompts` argument in `SentenceTransformerTrainingArguments`. Our experiments show that you can easily reach 0.66% to 0.90% relative performance improvement on NDCG@10 at no extra cost by adding "query: " before each training query and "document: " before each training answer.

3. Sentence Transformers now supports training PEFT adapters via 7 new methods for adding new adapters or loading pre-trained ones. You can also directly load a trained adapter with SentenceTransformer as if it's a normal model. Very useful for e.g. 1) training multiple adapters on 1 base model, 2) training bigger models than otherwise possible, or 3) cheaply hosting multiple models by switching multiple adapters on 1 base model.

4. We added easy evaluation on NanoBEIR, a subset of BEIR a.k.a. the MTEB Retrieval benchmark. It contains 13 datasets with 50 queries and up to 10k documents each. Evaluation is fast, and can easily be done during training to track your model's performance on general-purpose information retrieval tasks.

Additionally, we also deprecate Python 3.8, add better compatibility with Transformers v4.46.0, and more. Read the full release notes here: https://github.com/UKPLab/sentence-transformers/releases/tag/v3.3.0


Hi everyone, i have been trying to give my chatbots access to the web for a long while now, i have tried using the google search api, taking the links and then scraping them, but it does'nt work that well. does anyone know how you can give a chatbot access to google/the web, so that it has access to current data.


Excited to see my weird https://huggingface.co/datasets/davanstrien/ufo-ColPali dataset featured in a video by @sabrinaesaquino!

The video covers using ColPali with Binary Quantization in Qdant to accelerate retrieval. 2x speed up with no performance drop in results 🛸

Video: https://youtu.be/_A90A-grwIc?si=oB3JAhJG8VQUZGLz
Blog post: https://danielvanstrien.xyz/posts/post-with-code/colpali-qdrant/2024-10-02_using_colpali_with_qdrant.html


NEW RELEASE! Shining Valiant 2 for Llama 3.1 70b is here!

- Trained on high quality science-instruct, complex queries, and general chat data!
- Uses our newest datasets, ALL open-sourced for everyone to use!

GET SV2 70B: https://huggingface.co/ValiantLabs/Llama3.1-70B-ShiningValiant2

- Find the SV datasets here, including the expanded version of our science-instruct dataset:
  - https://huggingface.co/datasets/sequelbox/Celestia
  - https://huggingface.co/datasets/sequelbox/Spurline
  - https://huggingface.co/datasets/sequelbox/Supernova
- SV2 8b and 3b will be updated with the new datasets soon!

Enjoy! :)


Cybertron is back:

We released today a newest version of Cybertron: V4 based on Qwen2.5 7B and trained on MagPie. Scoring #1 LLM on 7B & 8B class.

The model hasn't go thru DPO, so the weights are in good shape to welcome further training sessions and optimizations.
Enjoy it in the hub as usual:
https://huggingface.co/fblgit/cybertron-v4-qw7B-MGS


FLUX De-Distilled and Anti-Bleeding Fine-Tuning / DreamBooth & LoRA Training Experiments

Also Testing CFG Impact for Stylized Images on base FLUX DEV model

Full size grids and full details shared here : https://www.patreon.com/posts/114969137

The aim is finding a workflow that FLUX can be trained for multiple concepts / subjects without bleeding / mixing


⚡ LLMs do a good job at NER, but don't you want to do learn how to do more with less?

Go from 🐢 -> 🐇

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

Bootstrap with a teacher model.

Correct potential mistakes to get high-quality data.

Fine-tune your student model

Go more accurate and more efficient.

Free signup: https://lu.ma/zx2t7irs

prithivMLmods John6666 janinakeno NeuroSpaceX


🚀 Exploring Topic Modeling with BERTopic 🤖

When you come across an interesting dataset, you often wonder:
Which topics frequently appear in these documents? 🤔
What is this data really about? 📊

Topic modeling helps answer these questions by identifying recurring themes within a collection of documents. This process enables quick and efficient exploratory data analysis.

I’ve been working on an app that leverages BERTopic, a flexible framework designed for topic modeling. Its modularity makes BERTopic powerful, allowing you to switch components with your preferred algorithms. It also supports handling large datasets efficiently by merging models using the BERTopic.merge_models approach. 🔗

🔍 How do we make this work?
Here’s the stack we’re using:

📂 Data Source ➡ Hugging Face datasets with DuckDB for retrieval
🧠 Text Embeddings ➡ Sentence Transformers (all-MiniLM-L6-v2)
⚡ Dimensionality Reduction ➡ RAPIDS cuML UMAP for GPU-accelerated performance
🔍 Clustering ➡ RAPIDS cuML HDBSCAN for fast clustering
✂ Tokenization ➡ CountVectorizer
🔧 Representation Tuning ➡ KeyBERTInspired + Hugging Face Inference Client with Meta-Llama-3-8B-Instruct
🌍 Visualization ➡ Datamapplot library
Check out the space and see how you can quickly generate topics from your dataset: https://huggingface.co/spaces/datasets-topics/topics-generator

Powered by @MaartenGr  - BERTopic


🔍 NYT leveraged AI to investigate election interference by analyzing 400+ hours of recorded meetings - that's 5M words of data!

AI spotted patterns, humans verified facts. Every AI-flagged quote was manually verified against source recordings. Really appreciate that they published their full methodology - transparency matters when using AI in journalism.

A perfect blend of tech & journalism.

 The future of journalism isn't robots replacing reporters - it's AI helping humans process massive datasets more efficiently. Sometimes the most powerful tech solutions are the least flashy ones.

Read the article: https://www.nytimes.com/interactive/2024/10/28/us/politics/inside-the-movement-behind-trumps-election-lies.html?unlocked_article_code=1.Vk4.ucv9.dbHVquTQaf0G&smid=nytcore-ios-share

Aurelien-Morgan John6666 io2 OmbelineM wsuff KhalilGuetari owao


🚀 Exciting update! You can now compare multiple models side-by-side with the Hugging Face Open LLM Comparator! 📊

https://huggingface.co/spaces/open-llm-leaderboard/comparator

Dive into multi-model evaluations, pinpoint the best model for your needs, and explore insights across top open LLMs all in one place. Ready to level up your model comparison game?

awacke1 reach-vb Manel alielfilali01 octoplanet averoo


New Dataset: Software-Architecture
Link: https://huggingface.co/datasets/ajibawa-2023/Software-Architecture

I am releasing a Large Dataset covering topics related to Software-Architecture. This dataset consists of around 450,000 lines of data in jsonl.

I have included following topics:

Architectural Frameworks

Architectural Patterns for Reliability

Architectural Patterns for Scalability

Architectural Patterns

Architectural Quality Attributes

Architectural Testing

Architectural Views

Architectural Decision-Making

Advanced Research

Cloud-Based Architectures

Component-Based Architecture

Data Architecture

Emerging Trends

Event-Driven Architecture

Evolvability and Maintainability

Microservices and Monolithic

Microservices Architecture

Security Architecture

Service-Oriented Architecture

Software Design Principles

and Many More!

This dataset is useful in LLM development. Also those who are working on developing Software development related LLMs then this dataset can be useful.

This dataset is very useful to Researchers as well.


Check out The AI Writing Contest with @BrightData!  https://www.contests.hackernoon.com/ai-writing-contest Cash prizes for  innovative approaches to AI and LLM training.  We publish blog posts, research papers, stories of side hustles, you name it.

Any story tagged #AI enters to win.  Most recent stories: https://hackernoon.com/tagged/ai and RSS feed https://hackernoon.com/tagged/ai/feed

Couple of favorite recent posts we published:

Why Salesforce and Microsoft Are Battling for the Future of AI Agents https://hackernoon.com/why-salesforce-and-microsoft-are-battling-for-the-future-of-ai-agents

Decentralized AI Summit at MIT Votes OriginTrail As The Best Decentralized AI Project https://hackernoon.com/decentralized-ai-summit-at-mit-votes-origintrail-as-the-best-decentralized-ai-project

Studying is Overrated https://hackernoon.com/studying-is-overrated

Why Can’t AI Count Letters??? https://hackernoon.com/why-cant-ai-count-letters

The Paradox of AI: If It Can't Replace us, Is It Making Us Dumber? https://hackernoon.com/the-paradox-of-ai-if-it-cant-replace-us-is-it-making-us-dumber

How Does Human Memory Work? https://hackernoon.com/how-does-human-memory-work

Is AI Actually Writing Production-Ready Code? https://hackernoon.com/is-ai-actually-writing-production-ready-code

Our AI Coding Tool Went Viral, Then Everything Broke. This is What We Learned. https://hackernoon.com/our-ai-coding-tool-went-viral-then-everything-broke-this-is-what-we-learned

Startups of The Year: Meet the AI Industry https://hackernoon.com/startups-of-the-year-meet-the-ai-industry

Nobel Prize Winner Geoffrey Hinton Explores Two Paths to Intelligence in AI Lecture https://hackernoon.com/nobel-prize-winner-geoffrey-hinton-explores-two-paths-to-intelligence-in-ai-lecture

Comparing AI vs. Blockchain Hype https://hackernoon.com/comparing-ai-vs-blockchain-hype

The SaaS Apocalypse and How aI Will Give Birth to One-person Tech Giants https://hackernoon.com/the-saas-apocalypse-and-how-ai-wi


Expressive Gaussian Human Avatars from Monocular RGB Video (2407.03204)

🚀🕺🌟 New Research Alert (Avatars Collection)! 🌟💃🚀
📄 Title: Expressive Gaussian Human Avatars from Monocular RGB Video 🔝

📝 Description: The new EVA model enhances the expressiveness of digital avatars by using 3D Gaussians and SMPL-X to capture fine-grained hand and face details from monocular RGB video.

👥 Authors: Hezhen Hu, Zhiwen Fan, Tianhao Wu, Yihan Xi, Seoyoung Lee, Georgios Pavlakos, and Zhangyang Wang

📄 Paper: https://huggingface.co/papers/2407.03204

🌐 Github Page: https://evahuman.github.io/
📁 Repository: https://github.com/evahuman/EVA

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

🚀 WACV-2024-Papers: https://github.com/DmitryRyumin/WACV-2024-Papers

🚀 ICCV-2023-Papers: https://github.com/DmitryRyumin/ICCV-2023-Papers

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

🚀 Added to the Avatars Collection: https://huggingface.co/collections/DmitryRyumin/avatars-65df37cdf81fec13d4dbac36

🔍 Keywords: #DigitalAvatars #3DModeling #ComputerVision #MonocularVideo #SMPLX #3DGaussians #AvatarExpressiveness #HandTracking #FacialExpressions #AI #MachineLearning

DmitryRyumin m1b prithivMLmods not-lain andmev Joe2EZ


MInference 1.0: Accelerating Pre-filling for Long-Context LLMs via
  Dynamic Sparse Attention (2407.02490)

Weclome to use MInference, which leverages the dynamic sparse nature of LLMs' attention, which exhibits some static patterns, to speed up the pre-filling for million tokens LLMs. It first determines offline which sparse pattern each head belongs to, then approximates the sparse index online and dynamically computes attention with the optimal custom kernels. This approach achieves up to a 10x speedup for pre-filling on an A100 while maintaining accuracy with 1M tokens.

For more detail please check,
project page: https://aka.ms/MInference
code: https://github.com/microsoft/MInference
paper: https://huggingface.co/papers/2407.02490
hf demo: https://huggingface.co/spaces/microsoft/MInference


New dataset filtering feature just dropped! 🤗🚀

Find exactly what you need with filters for:
- Modalities (text, image, audio, etc.)
- Dataset size
- File format

Try it now: https://huggingface.co/datasets

What other filters would you find useful? Drop your ideas!


Hi @jonoirwin! Big fan of https://fastvoiceagent.cerebrium.ai/ 🔥
I'd be super happy to give you a GPU grant to host it on a Space, it would allow more people to discover and use it!

lunarflu RakshitAralimatti nyuuzyou Inflammable1230


I've created a space for chatting with Gemma 2 using llama.cpp

- 🎛 Choose between 27B IT and 9b IT models
- 🚀 Fast inference using llama.cpp

- https://huggingface.co/spaces/gokaygokay/Gemma-2-llamacpp

John6666 merterbak victor lunarflu thethinkmachine osanseviero ggerganov juancopi81 ucsahin nedegilefendim Metin


@Omartificial-Intelligence-Space has trained and released 6 Arabic embedding models for semantic similarity. 4 of them outperform all previous models on the STS17 Arabic-Arabic task!

📚 Trained on a large dataset of 558k Arabic triplets translated from the AllNLI triplet dataset: https://huggingface.co/datasets/Omartificial-Intelligence-Space/Arabic-NLi-Triplet
6⃣ 6 different base models: AraBERT, MarBERT, LaBSE, MiniLM, paraphrase-multilingual-mpnet-base, mpnet-base, ranging from 109M to 471M parameters.
🪆 Trained with a Matryoshka loss, allowing you to truncate embeddings with minimal performance loss: smaller embeddings are faster to compare.
📈 Outperforms all commonly used multilingual models like https://huggingface.co/intfloat/multilingual-e5-large, https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2, and https://huggingface.co/sentence-transformers/LaBSE.

Check them out here:
- https://huggingface.co/Omartificial-Intelligence-Space/Arabic-mpnet-base-all-nli-triplet
- https://huggingface.co/Omartificial-Intelligence-Space/Arabic-all-nli-triplet-Matryoshka
- https://huggingface.co/Omartificial-Intelligence-Space/Arabert-all-nli-triplet-Matryoshka
- https://huggingface.co/Omartificial-Intelligence-Space/Arabic-labse-Matryoshka
- https://huggingface.co/Omartificial-Intelligence-Space/Marbert-all-nli-triplet-Matryoshka
- https://huggingface.co/Omartificial-Intelligence-Space/Arabic-MiniLM-L12-v2-all-nli-triplet
Or the collection with all: https://huggingface.co/collections/Omartificial-Intelligence-Space/arabic-matryoshka-embedding-models-666f764d3b570f44d7f77d4e

My personal favourite is likely https://huggingface.co/Omartificial-Intelligence-Space/Arabert-all-nli-triplet-Matryoshka: a very efficient 135M parameters & scores #1 on https://huggingface.co/spaces/mteb/leaderboard.

urchade merterbak victor not-lain Omartificial-Intelligence-Space lunarflu osanseviero


I really like what the @jasperAITeam designed with Flash LoRA. It works really well for something that generates so quickly, and I'm excited to test it out with Animate Diff, because I recently was testing LCM on it's own for AD and the results were already promising.

I put together my own page of models using their code and LoRA. Enjoy!

https://huggingface.co/spaces/alvdansen/flash-lora-araminta-k-styles


Preview:
We will open source the 2.5B ChemVLM and the tool-enhanced ChemLLM-7B in the near future


nanoLLaVA-1.5 is here! Same size (1B), better performance 🔥🔥🔥
And it is much more powerful than v1.0
Try it out now on HF Spaces: https://huggingface.co/spaces/qnguyen3/nanoLLaVA
Model: https://huggingface.co/qnguyen3/nanoLLaVA-1.5

ZennyKenny YaTharThShaRma999 Blucky lunarflu mohamedemam osanseviero ucsahin appvoid qnguyen3 nicolay-r


Below we experiment with negative merger weighting (-1.0!) using task arithmetic. Merge formula on the model card and in the repo itself.

This model is steered to behave opposite to what MopeyMule demonstrated.

Based on the implications of the merge technique, we also propose Orthogonalized Vector Adaptation (OVA). We also extract a LoRA of the counter-refusal abliteration steering vector.

The resulting merger is not a perfect model, but it's a behaviorally interesting model. The model name was inspired by a Philip K. Dick story.
https://huggingface.co/grimjim/Llama-3-Perky-Pat-Instruct-8B

Refusal vector weights ready for use:
https://huggingface.co/grimjim/Llama-3-Instruct-abliteration-OVA-8B
https://huggingface.co/grimjim/Llama-3-Instruct-abliteration-LoRA-8B


An Open-source and super-fast alternative to @OpenAI  GPT4o is here! 🚀

In November last year, Iliad announced a fully open-source-oriented AI lab called @kyutai_labs  🧪

In this very short time they have released Moshi! An open speech-to-speech model 🗣... released publicly even before closed GPT4o (yes, you can try it right now!) 🌐

Demo: https://www.moshi.chat/?queue_id=talktomoshi

This is what you expect an intelligent companion to do! 🤖 It is continuously generating responses and listening at the same time with sub 300 ms latency! ⏱

This level of engagement is so new, it almost feels like I am under pressure to keep up with it! 😅

While it hallucinates like crazy! 🤯 I think fundamentally this is what a true assistant would look like. And did I say they are going to open-source it? 🆓

Weights and full technical report are promised to be coming soon! 📜

This is the work of an incredible but just 8 team members!! 👥👏


Qualitative Emergence: The Paradox of Statistical AI in Language Comprehension

https://empereur-pirate.medium.com/qualitative-emergence-the-paradox-of-statistical-ai-in-language-comprehension-ccd1e221423e

This text explores the nature of generative AI language models, highlighting their ability to produce coherent content despite being based on statistical processes. It discusses the role of random exploration in AI's impressive capabilities, while emphasizing that these models lack true understanding or subjectivity. The article contrasts AI's quantitative-to-qualitative approach with human cognitive development. It argues that AI serves as a tool for rewriting, analogical combination, and reasoned evaluation, rather than being truly intelligent. The text concludes by considering AI's potential applications in fields like law, stressing the importance of transparency and participatory models in AI development and use.


As we advance on the path towards true Artificial General Intelligence (AGI), it's crucial to recognize and address the limitations inherent in current technologies, particularly in large language models (LLMs) like those developed by OpenAI. While LLMs excel in processing and generating text, their capabilities are largely constrained to the domains of natural language understanding and generation. This poses significant limitations when dealing with more complex, abstract mathematical concepts such as topological analysis, 3D geometry, and homotopy type theory.

Topological Analysis and 3D Geometry: LLMs currently do not possess the inherent ability to understand or interpret the spatial and geometric data that is critical in fields like robotics, architecture, and advanced physics. These models lack the capacity to visualize or manipulate three-dimensional objects or comprehend the underlying properties that govern these forms.

Homotopy Type Theory is a branch of mathematics that combines homotopy theory and type theory. Homotopy type theory provides tools for a more robust handling of equivalences and transformations, something that LLMs are not designed to handle directly.

For the development of AGI, it is not sufficient to merely enhance existing models' capacities within their linguistic domains. Instead, a synthesis of symbolic AI with an understanding of homotopy type theory could pave the way. Symbolic AI, which manipulates symbols and performs logical operations, when combined with the abstract mathematical reasoning of homotopy type theory, could lead to breakthroughs in how machines understand and interact with the world.

To address these limitations we have developed Tenzin, which is a one-of-a-kind model with a planned release date within the next 1-2 weeks . To learn more join the waitlist at https://octave-x.com/.


New #NVIDIA paper: Improving Hyperparameter Optimization with Checkpointed Model Weights

Hyperparameter optimization often dominates the cost of model design.  So, we want cheap surrogate functions that approximate model performance to guide our search.  Existing methods can train on optimization metadata – like a trajectory of losses – to build these surrogates.

In our work, we add the ability to train our hyperparameter optimization surrogates on checkpointed model weights with a graph metanetwork.  This allows us to leverage a large, pre-existing source of information that can featurize the architecture, dataset, losses, and optimization procedure.

🔍Project page: https://research.nvidia.com/labs/toronto-ai/FMS/
👨💻 Code for reproduction: https://github.com/NVlabs/forecasting-model-search
📄 Full Paper: https://arxiv.org/abs/2406.18630

Our project was a collaboration between NVIDIA’s Toronto AI Lab and the TAO team.

Check out more work from Toronto AI Lab here: https://research.nvidia.com/labs/toronto-ai/

You can view the TAO toolkit here: https://developer.nvidia.com/tao-toolkit


The Universal Checkpointing paper is out! https://arxiv.org/abs/2406.18820

If you remember the Bigscience BLOOM-176B training, Tunji Ruwase and I co-invented this technology for Megatron-Deepspeed in order to enable to quickly scale up and down node topology while continuing training.

Since then the DeepSpeed team continued improving on that and it has now been fully integrated into Deepspeed.

The blog post is here: https://github.com/microsoft/DeepSpeed/blob/master/blogs/deepspeed-ucp/README.md


AI Agents Solved!
Not a drill and not lying, 100% success rate with GPT 3.5 and Swarm algorithms for AI Agents. GPT 3.5 will create and execute the Swarm algorithms, and they will complete the API call 100% of the time. Here is the Colab, play with it yourself. I have a Github Repo for it all too: https://colab.research.google.com/drive/1EF_JmPidwoCd8tEgOChUt6kCX7TBLe20?usp=sharing


I changed my mind about kaggle nb versions. They're goated


🌟 It's been about a week since @Google dropped Gemma 2 and now Gemma 2 27B is the highest-ranked open-source LLM on LMSYS Chatbot Arena Leaderboard, beating Meta Llama 3 70B and Alibaba Qwen 2 72B! 🚀💪

🔍 Here is what a week of Gemma looked like:

1⃣ First, it's a challenging model to run. The only reason I could find is soft-capping of logits within the attention for longer context optimizations. And no one was doing that before! 🤯

2⃣ Next, the technical report mentions a 2B model... where is it? 🤔

3⃣ Simple things like a context length of 8192 tokens, the Rotary Position Embeddings (RoPE), and the approximated GeGLU non-linearity are similar to earlier Gemma's. 📏🔄

4⃣ But a lot of new stuff is here like Local Sliding Window and Global Attention: they alternate between them at every layer... don't know why! 🤷♂ and of course Logit soft-capping. 💡

5⃣ On-Policy Distillation of Language Models - Knowledge Distillation: Leverage a larger teacher model to train a smaller model (the 9B model). 🎓➡📚

6⃣ Model Merging: Combined average models from experiments run with different hyperparameters. 🧪⚗

7⃣ And borrowed Grouped-Query Attention (GQA) from @Meta Llama-3. 🔄🤝

📖 One of the best articles on Gemma 2: https://huggingface.co/blog/gemma2

📊 Technical report: https://storage.googleapis.com/deepmind-media/gemma/gemma-2-report.pdf

🔗 Models: https://huggingface.co/collections/google/gemma-2-release-667d6600fd5220e7b967f315


Hello!

I've been in the lab, I think one or two of you saw my furtive attempts to create a dolphinized 2b Gemma, which is still waiting for more funding. I get paid in a week. `</3`

Once that funding ran out, I dropped my last pinch of API credits to work on this:

https://huggingface.co/datasets/DigitalClockwork/spatial_instruct_v1

It is an instruct database for spatial interactions with color tokens, i'm planning to tune a TBD model. Been experimenting with Gemma, but i'm welcome to ( smaller! ) model suggestions. If you think your favorite 0.5/0.75/1/2b can handle numbers, distances, or colors especially well, most especially community-enhanced models... I'm listening to the comments, intently!
Have a great day, and enjoy! This was one fun! 🤗

`-<3`


🐦⬛ Nexusflow/𝗡𝗲𝘅𝘂𝘀𝗥𝗮𝘃𝗲𝗻-𝗩𝟮-𝟭𝟯𝗕

🐦⬛ Nexusflow/𝗡𝗲𝘅𝘂𝘀𝗥𝗮𝘃𝗲𝗻-𝗩𝟮-𝟭𝟯𝗕

⌘ CohereForAI/𝗰𝟰𝗮𝗶-𝗰𝗼𝗺𝗺𝗮𝗻𝗱-𝗿-𝗽𝗹𝘂𝘀

⌘ CohereForAI/𝗰𝟰𝗮𝗶-𝗰𝗼𝗺𝗺𝗮𝗻𝗱-𝗿-𝗽𝗹𝘂𝘀

⛵ mistralai/𝗠𝗶𝘅𝘁𝗿𝗮𝗹-𝟴𝘅𝟮𝟮𝗕-𝗜𝗻𝘀𝘁𝗿𝘂𝗰𝘁-𝘃𝟬.𝟭

⛵ mistralai/𝗠𝗶𝘅𝘁𝗿𝗮𝗹-𝟴𝘅𝟮𝟮𝗕-𝗜𝗻𝘀𝘁𝗿𝘂𝗰𝘁-𝘃𝟬.𝟭

𝗬𝗼𝘂 𝗱𝗼𝗻'𝘁 𝗻𝗲𝗲𝗱 "𝗳𝘂𝗻𝗰𝘁𝗶𝗼𝗻 𝗰𝗮𝗹𝗹𝗶𝗻𝗴 𝗳𝗶𝗻𝗲-𝘁𝘂𝗻𝗶𝗻𝗴" 𝘁𝗼 𝗯𝘂𝗶𝗹𝗱 𝗴𝗼𝗼𝗱 𝗮𝗴𝗲𝗻𝘁𝘀 ⛔

It's trendy to share models "fine-tuned for function calling"; but from my observations, this fine-tuning is not necessary or sufficient to build good agent systems.
To name only a few:
🐦⬛ Nexusflow/𝗡𝗲𝘅𝘂𝘀𝗥𝗮𝘃𝗲𝗻-𝗩𝟮-𝟭𝟯𝗕
⌘ CohereForAI/𝗰𝟰𝗮𝗶-𝗰𝗼𝗺𝗺𝗮𝗻𝗱-𝗿-𝗽𝗹𝘂𝘀
⛵ mistralai/𝗠𝗶𝘅𝘁𝗿𝗮𝗹-𝟴𝘅𝟮𝟮𝗕-𝗜𝗻𝘀𝘁𝗿𝘂𝗰𝘁-𝘃𝟬.𝟭
"Fine-tuned for function-calling" generally means "fine-tuned to generate function calls in correct JSON for extremely simple tasks". In other terms, it means "improve the formatting of the tool calls".

Yet I discovered two things while improving Transformers Agents:
🧐 Even when used as JSON agents, these fine-tuned models don't perform very well
🏅 𝙂𝙤𝙤𝙙 𝙗𝙖𝙨𝙚 𝙢𝙤𝙙𝙚𝙡𝙨 𝙥𝙚𝙧𝙛𝙤𝙧𝙢 𝙗𝙚𝙩𝙩𝙚𝙧 𝙬𝙞𝙩𝙝𝙤𝙪𝙩 𝙖𝙣𝙮 𝙛𝙞𝙣𝙚-𝙩𝙪𝙣𝙞𝙣𝙜, 𝙟𝙪𝙨𝙩 𝙥𝙡𝙖𝙞𝙣 𝙥𝙧𝙤𝙢𝙥𝙩𝙞𝙣𝙜. (Llama-3-70B-Instruct, GPT-4o, Claude-3.5-Sonnet)

👇 The graph below shows the count of errors for my GPT-4o validation run on the GAIA benchmark: 𝙰𝚐𝚎𝚗𝚝𝙿𝚊𝚛𝚜𝚒𝚗𝚐𝙴𝚛𝚛𝚘𝚛 and 𝙰𝚐𝚎𝚗𝚝𝙴𝚡𝚎𝚌𝚞𝚝𝚒𝚘𝚗𝙴𝚛𝚛𝚘𝚛 are the ones caused by incorrect formatting.
➤ As you can see, their count is already close to 0!
And given that GPT-4o is certainly not fine-tuned for our Code tool calling format, this shows that "function calling fine-tuning" is not necessary!

The hardest thing to get right in an agent is still to 𝙥𝙡𝙖𝙣 𝙜𝙤𝙤𝙙 𝙩𝙖𝙨𝙠-𝙨𝙤𝙡𝙫𝙞𝙣𝙜 𝙩𝙧𝙖𝙟𝙚𝙘𝙩𝙤𝙧𝙞𝙚𝙨 𝙤𝙫𝙚𝙧 𝙨𝙚𝙫𝙚𝙧𝙖𝙡 𝙨𝙩𝙚𝙥𝙨.
To improve this, we could:
- Use more powerful base models
- Make tool calling datasets with complex solving trajectories
- Use RL! cc @lvwerra

lvwerra merterbak AnkitAI louisbrulenaudet osanseviero Joseph717171 JackCloudman GPT007


X-Mesh: Towards Fast and Accurate Text-driven 3D Stylization via Dynamic
  Textual Guidance (2303.15764)

Here's my cool outputs from "X-Mesh: Towards Fast and Accurate Text-driven 3D Stylization via Dynamic Textual Guidance" article. Their technique transforms mesh objects based on textual prompts. Featured here are a dynamically stylized 3D representation of Cristiano Ronaldo and a diamond dragon.

📄 paper:   https://huggingface.co/papers/2303.15764

osanseviero nikgr merterbak AIDANIAI AptAlbatross


🐦 Do you remember IBIS? Not a fancy bird but the open challenge in Inferring Binding Specificities of unexplored human transcription factors. Check our site (https://ibis.autosome.org/) and have a sip of fresh news below.

👥 More than 100 teams registered for the challenge yet only two dozen are using the opportunity to explore their models on the Leaderboard. Don't miss the chance to participate in the Leaderboard stage, although independently of that you can submit the final solution.

🌐 Remember, the training data for Leaderboard and Final are available online, and you are free to mix-and-match it in any combination.

🌌 For Leaderboard, we have received 650 total submissions of AAA (advanced ML) and 296 PWM models (a whopping set of 6682 PWMs in total).

🚀 For PWMs, the baseline is left far behind, but some TFs remain tough nuts to be cracked (see the attached figure 1).

📈 For AAAs, there is a solid improvement over the best-submitted PWMs in A2G, but the G2A discipline remains unpopular (see the attached figure 2). Free hint: this is your chance!

💡 Another free hint: If your model tends to overfit given a limited set of data for some TFs don't forget to use reverse-complement and shift augmentations. Also, don't hesitate to use multitarget models i.e. predicting the binding of multiple TFs at the same time.

💡 Last but not least, try to combine knowledge from all accessible experiment types, especially for G2A discipline (ChIP-Seq & genomic HT-SELEX) in a single model!

📣 Finally and importantly, following the requests from the community, we decided to EXTEND the Leaderboard until the final submission deadline.

🗓 The final submission deadline is also EXTENDED until Aug 15. The final submission form and details will be posted on the IBIS website in the first half of July, follow our Telegram group and mailing list (see the links at https://ibis.autosome.org).


Topo4D: Topology-Preserving Gaussian Splatting for High-Fidelity 4D Head
  Capture (2406.00440)

🔥🎭🌟 New Research Alert - ECCV 2024 (Avatars Collection)! 🌟🎭🔥
📄 Title: Topo4D: Topology-Preserving Gaussian Splatting for High-Fidelity 4D Head Capture 🔝

📝 Description: Topo4D is a novel method for automated, high-fidelity 4D head tracking that optimizes dynamic topological meshes and 8K texture maps from multi-view time-series images.

👥 Authors: @Dazz1e, Y. Cheng, @Ryan-sjtu, H. Jia, D. Xu, W. Zhu, Y. Yan

📅 Conference: ECCV, 29 Sep – 4 Oct, 2024 | Milano, Italy 🇮🇹

📄 Paper: https://huggingface.co/papers/2406.00440

🌐 Github Page: https://xuanchenli.github.io/Topo4D/
📁 Repository: https://github.com/XuanchenLi/Topo4D

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

🚀 WACV-2024-Papers: https://github.com/DmitryRyumin/WACV-2024-Papers

🚀 ICCV-2023-Papers: https://github.com/DmitryRyumin/ICCV-2023-Papers

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

🚀 Added to the Avatars Collection: https://huggingface.co/collections/DmitryRyumin/avatars-65df37cdf81fec13d4dbac36

🔍 Keywords: #Topo4D #4DHead #3DModeling #4DCapture #FacialAnimation #ComputerGraphics #MachineLearning #HighFidelity #TextureMapping #DynamicMeshes #GaussianSplatting #VisualEffects #ECCV2024

DmitryRyumin Ryan-sjtu VanshRana12 prithivMLmods osanseviero AnkitAI


I've created a Stable Diffusion 3 (SD3) image generation space for convenience. Now you can:

1. Generate SD3 prompts from images
2. Enhance your text prompts (turn 1-2 words into full SD3 prompts)

https://huggingface.co/spaces/gokaygokay/SD3-with-VLM-and-Prompt-Enhancer

These features are based on my custom models:

- VLM captioner for prompt generation:
  - https://huggingface.co/gokaygokay/sd3-long-captioner

- Prompt Enhancers for SD3 Models:
  - https://huggingface.co/gokaygokay/Lamini-Prompt-Enchance-Long
  - https://huggingface.co/gokaygokay/Lamini-Prompt-Enchance

You can now simplify your SD3 workflow with these tools!

John6666 clem ucsahin osanseviero thliang01 AnkitAI netynet Best-codes Wok


MIRAI: Evaluating LLM Agents for Event Forecasting (2407.01231)

Check out our new benchmark paper on LLM agents for global events forecasting! https://huggingface.co/papers/2407.01231

📜 Arxiv: https://arxiv.org/abs/2407.01231
🔗 Project page: https://mirai-llm.github.io
💻 GitHub Repo: https://github.com/yecchen/MIRAI
📁 Dataset: https://drive.google.com/file/d/1xmSEHZ_wqtBu1AwLpJ8wCDYmT-jRpfrN/view?usp=sharing
📊 Interactive Demo Notebook: https://colab.research.google.com/drive/1QyqT35n6NbtPaNtqQ6A7ILG_GMeRgdnO?usp=sharing


LLMs are improving at math faster than my math coursework. I appreciate the all the hardworking engineers for helping me through high school.


✨🚀 Claude Sonnet 3.5 API.  It's already weaving digital magic!
🧠💻 Try it at my space: 🔗 https://huggingface.co/spaces/awacke1/AnthropicClaude3.5Sonnet-ACW

Kudos to @AnthropicAI for this elegant API! 👏 #AI #CodeMagic #AnthropicAI   Thanks Huggingface for hosting the best hub in the world for AI development!


DETRs Beat YOLOs on Real-time Object Detection (2304.08069)

🚀 Transformers are not here to take part but take over... and down goes real-time object detection! 💥

Enter Real-time DEtection Transformer (RT-DETR) 🦾 as suggested capable of real-time object detection. 🎯

Object DEtection Transformer (DETR) is not new (@Meta did it eons ago) but it had the issue of every other transformer, high computational cost 💸

RT-DETR brings an efficient hybrid encoder to expeditiously process multi-scale features by decoupling intra-scale interaction and cross-scale fusion to improve speed 🏎

Gist is RT-DETR speeds up object detection by redesigning its encoder to process features more efficiently and selecting higher quality initial object queries. ⚡

It also allows adjusting the number of decoder layers to balance speed and accuracy for different real-time scenarios. ⚖

This makes RT-DETR faster and more accurate than previous YOLO models. 🏆

How much better😎/faster? ⏱

RT-DETR-R50 achieved 53.1% AP on COCO and 108 FPS on a T4 GPU, while RT-DETR-R101 achieved 54.3% AP and 74 FPS, outperforming advanced YOLO models in both speed and accuracy. 🚀✨

📄 Paper: https://huggingface.co/papers/2304.08069

🧠 Models: https://huggingface.co/models?search=pekingu/rt-detr

merterbak clem osanseviero louisbrulenaudet


Just updated https://huggingface.co/spaces/MohamedRashad/timm-leaderboard with fuzzy search for people who want to search for a certian vision model


Search Hugging Face datasets by column names with a new experimental API! This API allows you to:

- Search for question-answering datasets that include context
- Find alpaca-style datasets
- Locate DPO datasets

Try it out here: https://huggingface.co/spaces/librarian-bots/dataset-column-search-api, or explore real-world applications in this notebook: https://huggingface.co/spaces/librarian-bots/dataset-column-search-api/blob/main/dataset_search_client_notebook.ipynb

clem osanseviero merterbak John6666 louisbrulenaudet


Real-time DEtection Transformer (RT-DETR) landed in transformers 🤩 with Apache 2.0 license 😍

🔖 models: https://huggingface.co/PekingU
🔖 demo: https://huggingface.co/spaces/merve/RT-DETR-tracking-coco
📝 paper: https://huggingface.co/papers/2304.08069
📖 notebook: https://github.com/merveenoyan/example_notebooks/blob/main/RT_DETR_Notebook.ipynb

YOLO models are known to be super fast for real-time computer vision, but they have a downside with being volatile to NMS 🥲

Transformer-based models on the other hand are computationally not as efficient 🥲

Isn't there something in between? Enter RT-DETR!

The authors combined CNN backbone, multi-stage hybrid decoder (combining convs and attn) with a transformer decoder. In the paper, authors also claim one can adjust speed by changing decoder layers without retraining altogether.
The authors find out that the model performs better in terms of speed and accuracy compared to the previous state-of-the-art. 🤩

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talkative Tom-Neverwinter ooaykac GPT007


**How I train a LoRA: m3lt style training overview**

I've just written an article that takes a step by step approach to outlining the method that I used to train the 'm3lt' lora, a blended style model.

I've used the LoRA Ease trainer by @multimodalart  :D

https://huggingface.co/blog/alvdansen/training-lora-m3lt
https://huggingface.co/spaces/multimodalart/lora-ease

ijohn07 clem blender66cat not-lain thedarktrumpet ashemvets glpx IkeaMan

victor multimodalart rishiguin clem not-lain louisbrulenaudet


5,000 new repos (models, datasets, spaces) are created EVERY DAY on HF now. The community is amazing!

umuthopeyildirim osanseviero victor tail-call paulml jeremy-london brunatrevelin Clausss ZeroWw xi0v yuriachermann merterbak nroggendorff sted97 Jaromir not-lain netynet Heresynetwork SixOpen tarob0ba 3thn digiplay prithivMLmods GPT007 Nymbo

victor ngxson Clausss ZeroWw xi0v nold yuriachermann nroggendorff InferenceIllusionist sted97 danielus not-lain netynet Heresynetwork moock 3thn kramp GPT007 nampdn-ai IAmTheCollector

digiplay louisbrulenaudet GPT007 emirhanbilgic


🚨Exciting news for the Multilingual Synthetic Data Community!🚨

I’ve taken inspiration from the MAGPIE paper on Llama-3-8B-instruct and extended its capabilities. Here’s what’s new!

🗞 The MAGPIE paper showcased that if you use the instruction-tuned version (`Llama-3-8B-instruct`) to generate synthetic instructions and then fine-tune the base version (`Llama-3-8B`) on this dataset, you can improve even the it-tuned version

🤔 While reading a script by Sebastian Raschka, PhD, I wondered: Could these advancements be replicated in other languages? Specifically, could they benefit non-English datasets?

🎉 And the answer is YES! At least for Spanish. I've successfully adapted the techniques for Spanish, proving the model's flexibility and multilingual capabilities.

👩💻 To make this accessible, I created a basic script (heavily inspired by the Sebastian Raschka one) that allows you to generate similar datasets using `ollama` models (initially phi and llama3) automatically and upload it to the Hugging Face Hub!
[Script](https://gist.github.com/mrm8488/4650a5e3cc45523798a527a3446eb312)

🔍 Explore the datasets 📚 generated using our new script!

- [Llama-3-8B](https://huggingface.co/datasets/mrm8488/dataset_llama3_5000_samples_es_4231_filtered)
- [Phi-3-medium](https://huggingface.co/datasets/mrm8488/dataset_phi3-medium_5000_samples_es_3906_filtered)
-  [Phi-3-mini](https://huggingface.co/datasets/mrm8488/dataset_phi3_5000_samples_es_3282_filtered)

Note: These datasets have basic filtering. Apply additional quality filters before using them to fine-tune large language models.

Inspiration and base script:
https://github.com/rasbt/LLMs-from-scratch/blob/main/ch07/05_dataset-generation/llama3-ollama.ipynb
https://www.linkedin.com/feed/update/urn:li:activity:7210982019751661568/

Davipar osanseviero apol anakin87 davanstrien victor ucsahin floschne GPT007 vikas Marvin73 pavaldeveloper mrm8488 Taylor658 adorkin


I have finished writing a blogpost about building an image-based retrieval system,  This is one of the first-ever approaches to building such a pipeline using only open-source models/libraries 🤗

You can checkout the blogpost in https://huggingface.co/blog/not-lain/image-retriever and the associated space at https://huggingface.co/spaces/not-lain/image-retriever .

✨ If you want to request another blog post consider letting me know down below or you can reach out to me through any of my social media

📖 Happy reading !

tail-call YaTharThShaRma999 umutphp John6666 MrOvkill sted97 kleberbaum vincentweisser BigOne


Yet another rewarding week in Open Source AI:

1. Google dropped Gemma 27B & 9B - The best open (commercially permissive) LLM out there, according to LYMSYS.
https://huggingface.co/collections/google/gemma-2-release-667d6600fd5220e7b967f315

2. Mars5 TTS - Text to Speech with insane prosodies control & voice cloning.
https://huggingface.co/CAMB-AI/MARS5-TTS

3. Meta shipped LLM Compiler - beats GPT 4 on code optimisation and compiler reasoning.
https://huggingface.co/collections/facebook/llm-compiler-667c5b05557fe99a9edd25cb

4. Arcee-Spark - Qwen2 7B (w/ merging) fine-tuned further to beat GPT 3.5 on MT Bench.
https://huggingface.co/arcee-ai/Arcee-Spark

5. Gemini Nano out in the wild in Chrome - On device LLM with just 2 lines of code (fully offline)

6. Fal released a fully Open Source GAN based Super-Resolution model (with second version already cooking)
https://huggingface.co/fal/AuraSR

7. NYU release Cambrian 1 - Vision Multimodal LLM that beats pretty much all other closed source competition 8-34B model size
https://huggingface.co/nyu-visionx

And.. much more like Open LLM Leaderboard got a major update, LYMSYS released Chat Vision Arena, OpenAI released a paper on CriticGPT!

What a lovely week, can’t wait for the next to see what the community is up to! Put it down in comments if I missed something 🔥

julien-c netynet m-ric jeffboudier SixOpen hsnugavin brandonisntafraid pcuenq louisbrulenaudet victor polinaeterna apolloparty clem tomaarsen ogimgio qnixsynapse linoyts John6666 Nelathan ruben-wleon ddiddi Joseph717171

dashfunnydashdash ddiddi Joseph717171 bradshimmin


Understanding and Diagnosing Deep Reinforcement Learning (2406.16979)

The paper is now available at ICML 2024. This paper introduces a foundational approach to analyze deep reinforcement learning decision making. Truly excited to share these results!

Paper: https://openreview.net/pdf?id=s9RKqT7jVM
HF: https://huggingface.co/papers/2406.16979
Twitter: https://x.com/EzgiKorkmazAI/status/1798990328390996002


Announcing the creation of the "HF for Legal" organization, an open-source community dedicated to demystifying language models for legal professionals 🤗

Whether you're a practicing attorney, a legal scholar, or a technologist interested in legal applications of AI, HF for Legal may be your hub for exploration, learning, and free innovation ⚗

On the occasion of this launch, you'll be able to find several notebooks I've been developing over the last few months for TSDAE pre-training of embedding models, the generation of indexes for semantic search, based on the formidable work of @tomaarsen and @nreimers, adapted to the field of French law, or the addition of information retrieval tasks to the MTEB.

Join us in our mission to make AI more accessible and understandable for the legal world, ensuring that the power of language models can be harnessed effectively and ethically.

Link to the org: https://huggingface.co/HFforLegal

Special thanks to @clem for encouraging me to start this organization. Let's hope we can bring together all the enthusiasts who work in this field.

Let's code and share together! 🚀🔗

tomaarsen clem not-lain netynet GFA-D2 ucalyptus Joseph717171 Elizezen jambracken vincentweisser ruben-wleon TuringsSolutions nroggendorff


𝐖𝐡𝐚𝐭 𝐬𝐩𝐚𝐫𝐤𝐞𝐝 𝐭𝐡𝐢𝐬 𝐢𝐝𝐞𝐚?

𝐖𝐡𝐚𝐭 𝐬𝐩𝐚𝐫𝐤𝐞𝐝 𝐭𝐡𝐢𝐬 𝐢𝐝𝐞𝐚?

𝐇𝐨𝐰 𝐝𝐢𝐝 𝐈 𝐜𝐫𝐞𝐚𝐭𝐞 𝐲𝐨-𝐋𝐥𝐚𝐦𝐚?

𝐇𝐨𝐰 𝐝𝐢𝐝 𝐈 𝐜𝐫𝐞𝐚𝐭𝐞 𝐲𝐨-𝐋𝐥𝐚𝐦𝐚?

How to alter the behavior of a Language Model without fine-tuning or prompting? Say hello to 🎤 yo-Llama 🦙!

Model https://huggingface.co/anakin87/yo-Llama-3-8B-Instruct

This experiment steers Llama-3-8B-Instruct to respond in a rap style.
How? Amplifying the rap direction in the activation space. 😎

𝐖𝐡𝐚𝐭 𝐬𝐩𝐚𝐫𝐤𝐞𝐝 𝐭𝐡𝐢𝐬 𝐢𝐝𝐞𝐚?

Lately, I got interested in mechanistic interpretability of LLMs.

💡 A recent paper, "Refusal in Language Models Is Mediated by a Single Direction," showed how to find the refusal direction in the activation space of Chat Language Models and either erase or amplify it.
A clever jailbreak method for open weights models.

Then, @failspy took it a step further by modifying the models to amplify different traits, such as making a model seem grumpy or irritable.

𝐇𝐨𝐰 𝐝𝐢𝐝 𝐈 𝐜𝐫𝐞𝐚𝐭𝐞 𝐲𝐨-𝐋𝐥𝐚𝐦𝐚?
(📓 notebook in the HF repository, heavily inspired by Failspy's work)

1⃣ Load the Llama-3-8B-Instruct model.
2⃣ Load 1024 examples from Alpaca (instruction dataset).
3⃣ Prepare a system prompt to make the original model act like a rapper.
4⃣ Run inference on the examples, with and without the system prompt, and cache the activations.
5⃣ Compute the rap feature directions (one for each layer) from the activations.
6⃣ Apply the feature directions one by one, checking the results on some examples.
7⃣ Pick the best-performing feature direction.
8⃣ Apply this feature direction and voilà!
yo-Llama-3-8B-Instruct is born! 🥳🎶

This was a fun experiment.

📚 Resources

Refusal in Language Models Is Mediated by a Single Direction - https://arxiv.org/abs/2406.11717

Uncensor any LLM with abliteration: great practical blog post by @mlabonne  https://huggingface.co/blog/mlabonne/abliteration

Practical materials by @failspy
- abliterator library https://github.com/FailSpy/abliterator
- Llama-MopeyMule-3-8B-Instruct model (+ notebook) https://huggingface.co/failspy/Llama-3-8B-Instruct-MopeyMule

mlabonne diwank YaTharThShaRma999 Korakoe


🚀 Llama-3-ELYZA-JP-8B

ELYZA, Inc. has developed two large language models (LLMs) for Japanese called "Llama-3-ELYZA-JP-70B" with 70 billion parameters and "Llama-3-ELYZA-JP-8B" with 8 billion parameters, based on Meta's "Llama 3" series. These models have been fine-tuned through additional pre-training and post-training to improve Japanese language capabilities significantly.

Key Points:

Performance:
- Llama-3-ELYZA-JP-70B surpasses global models such as GPT-4, Claude 3 Sonnet, and Gemini 1.5 Flash.
- Llama-3-ELYZA-JP-8B matches models like GPT-3.5 Turbo and Claude 3 Haiku despite having fewer parameters.

Availability:
- The 8B model is available on Hugging Face Hub and can be used for both research and commercial purposes under the Llama 3 Community License.

Methodology:
- ELYZA enhanced the Japanese performance of the Llama 3 models through additional training with high-quality Japanese corpora and Instruction Tuning with proprietary datasets.

Benchmarks:
- Evaluations using ELYZA Tasks 100 and Japanese MT-Bench showed significant improvements in Japanese language generation.

Inference Speed:
- To address inference speed issues due to model size, ELYZA implemented Speculative Decoding, which achieved up to 1.6 times faster inference for the 70B model.

Overall, ELYZA's models demonstrate state-of-the-art performance in Japanese language tasks and are optimized for both efficiency and effectiveness.

Model URL:
- https://huggingface.co/elyza/Llama-3-ELYZA-JP-8B
- https://huggingface.co/elyza/Llama-3-ELYZA-JP-8B-AWQ
- https://huggingface.co/elyza/Llama-3-ELYZA-JP-8B-GGUF

Blog post (in Japanese):
https://note.com/elyza/n/n360b6084fdbd


📢 I've tested google/Gemma-2-9b-it in Target Sentiment Analysis (TSA), in zero-shot learning mode on RuSentNE-2023 dataset with texts translated into English (🇺🇸).

🔎 Findings: The key contribution with the most recent Gemma-2 release is reasoning alignment between different langauges. This is basically the first model under 10B category which shows equal results in English and non-english texts. In the case of texts in English it performs similar to LLaMa-3-8B / Mistal-7B

Model: https://huggingface.co/google/gemma-2-9b-it
Benchmark: https://github.com/nicolay-r/RuSentNE-LLM-Benchmark


On-Policy Distillation of Language Models: Learning from Self-Generated
  Mistakes (2306.13649)

📅 Remember when at the beginning of the year @Google  gave an update on knowledge distillation! Introducing a way of learning from Self-Generated Mistakes?

📊 Resulting in significant improvements across tasks:
- 📄 2.1x in summarization
- 🌐 1.7x in translation
- 🧠 1.9x in reasoning tasks

🚀 Well, it looks like Google wasn't messing around! According to the Gemma 2 tech report, knowledge distillation was used to pre-train the 9B model, while the 27B model was pre-trained from scratch.

📈 For post-training, the Gemma 2 team generated completions from a stronger teacher model (unspecified in the report, but presumably Gemini Ultra), and then trained the student models on this synthetic data with SFT. This is quite common as seen in many open models, such as Zephyr and OpenHermes.

🤔 Sounds too good to be true? These models suffer from distribution mismatch between output sequences seen during training and those generated by the student during inference.

📰 This is where the January 2024 paper "On-Policy Distillation of Language Models" comes in...

🔍 Gemma 2 team used “on-policy distillation,” where the student generates completions from the SFT prompts. These completions are then used to compute the KL divergence between the teacher’s and student’s logits. By minimizing the KL divergence throughout training, the student learns to model the behavior of the teacher accurately while also minimizing the train-inference mismatch.

📚 Gem🔹 of a blog by @huggingface  uncovering everything Gemma 2: https://huggingface.co/blog/gemma2#knowledge-distillation

📄 On-Policy Distillation of Language Models: https://huggingface.co/papers/2306.13649


LLM-Assisted Patching of Polyfill Supply Chain Attack

A recent supply chain attack on polyfill.io affected over 100,000 websites (see https://www.patched.codes/blog/patching-the-polyfill-supply-chain-attack). To address this issue, we show how developers can leverage Large Language Models (LLMs) for efficient vulnerability patching:

1. Automated Detection: Using Semgrep rules (see https://semgrep.dev/playground/r/KxUvD7w/asankhaya_personal_org.polyfill-compromise-copy) to identify vulnerable code.

2. LLM-Powered Patching: Utilizing Patchwork (https://github.com/patched-codes/patchwork), an open-source solution that employs LLMs to automatically fix vulnerabilities.

3. Custom Workflows: The "Fixpolyfill" patchflow (https://github.com/patched-codes/patchwork-configs/tree/main/patchflows/Fixpolyfill) , tailored for this specific attack, can be easily run across multiple repositories.

4. Scalable Solutions: Options to scan and patch entire GitHub/GitLab organizations, with automated pull request generation.

5. Rapid Response: LLM-assisted patching enables swift action to minimize damage from supply chain attacks.

This approach demonstrates how LLMs can be effectively used to quickly respond to and remediate widespread security vulnerabilities in code.


New LoRA Model!

I trained this model on a new spot I'm really excited to share (soon!)

This Monday I will be posting my first beginning to end blog showing the tool I've used, dataset, captioning techniques, and parameters to finetune this LoRA.

For now, check out the model in the link below.

https://huggingface.co/alvdansen/m3lt

YaTharThShaRma999 multimodalart not-lain netynet Limbicnation nehalecky alvdansen 9voltfan2009 HyperBlaze seasnake HarshitJoshi adarshxs AnkitAI techdonCooper afpro victor yuriachermann louisbrulenaudet beratcmn ashemvets


How to Use SwarmUI & Stable Diffusion 3 on Cloud Services Kaggle (free), Massed Compute & RunPod : https://youtu.be/XFUZof6Skkw

Tutorial link : https://youtu.be/XFUZof6Skkw

It has manually written captions / subtitles and also video chapters.

If you are a GPU poor this is the video you need

In this video, I demonstrate how to install and use #SwarmUI on cloud services. If you lack a powerful GPU or wish to harness more GPU power, this video is essential. You'll learn how to install and utilize SwarmUI, one of the most powerful Generative AI interfaces, on Massed Compute, RunPod, and Kaggle (which offers free dual T4 GPU access for 30 hours weekly). This tutorial will enable you to use SwarmUI on cloud GPU providers as easily and efficiently as on your local PC. Moreover, I will show how to use Stable Diffusion 3 (#SD3) on cloud. SwarmUI uses #ComfyUI backend.

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

🔗 Windows Tutorial for Learn How to Use SwarmUI ➡ https://youtu.be/HKX8_F1Er_w

🔗 How to download models very fast to Massed Compute, RunPod and Kaggle and how to upload models or files to Hugging Face very fast tutorial ➡ https://youtu.be/X5WVZ0NMaTg

🔗 SECourses Discord ➡ https://discord.com/servers/software-engineering-courses-secourses-772774097734074388

🔗 Stable Diffusion GitHub Repo (Please Star, Fork and Watch) ➡ https://github.com/FurkanGozukara/Stable-Diffusion

Coupon Code for Massed Compute : SECourses
Coupon works on Alt Config RTX A6000 and also RTX A6000 GPUs


Hello!

I've been playing with Claude, and we decided to tackle a real thorn in my side.

"The Truthiness Model" - Analyze arbitrary input text for "truthiness", or likelihood of containing true information according to seed text.

P.S. Yes, v1 was broken. I saw the loss rate going down and go excited. Anyway, it just needed some data and a rollback, me and Claude got WAY too carried away trying to tack on features.

Anyway, fixed now, and working! :D

http://samuelmeyerscode.serveblog.net/?p=49


🤗 Hi HF Community!
🧬 As you may now, Evolutionary Scale recently released https://huggingface.co/EvolutionaryScale/esm3-sm-open-v1 model here on the Hub, "a frontier generative model for biology, able to jointly reason across three fundamental biological properties of proteins: sequence, structure, and function" - as it is described on the dedicated GitHub page.
⚡ If you are curious  about it and you want to try it out, you can do it with a space I built, https://huggingface.co/spaces/as-cle-bert/proteins-with-esm
Hope this helps with your research!🚀

introvoyz041 monsoon-nlp prithivMLmods not-lain BrotherMichaels arslan2012 Zhofang


We all agree https://huggingface.co/google/gemma-2-27b-it is trash, right?

hoangkien1703 imshan KingNish danielus SharryOG AIIAR cnmoro MarinaraSpaghetti netynet GPT007 jiweiwuita


📢  I've tested the most recent google/Gemma-2-9b-it in Sentiment Analyis and the obtained results makes me shocked! 🤯 It end up becoming a king 👑 that showcase top-1 across all the models and categories in Target Sentiment Analysis (TSA) on non-english texts (🇷🇺).

That's impressive to say the least, it surpassed all the other models benchmarked before and within categories of 100B and below by F1(PN) and nearly touched GPT-4 by F1(PN0). Google research team did a great job! 👏

Model: https://huggingface.co/google/gemma-2-9b-it
Benchmark: https://github.com/nicolay-r/RuSentNE-LLM-Benchmark


Hi Huggingfacers!

Thrilled to introduce Adam-mini, an optimizer that achieves on-par or better performance than AdamW with 45% to 50% less memory footprint.  Adam-mini can also achieve 49.5% higher throughput than AdamW on Llama2-7B pre-training.

The design of Adam-mini is inspired by certain Hessian structures we observed on Transformers.

Feel free to try it out! Try switching to Adam-mini with the same hyperparams of AdamW, it would work with only half memory. Hope Adam-mini can help save time, cost, and energy in your tasks!

Paper:  "Adam-mini: Use Fewer Learning Rates To Gain More" https://arxiv.org/abs/2406.16793

Code: https://github.com/zyushun/Adam-mini

yushun0410 osanseviero DmitryRyumin louisbrulenaudet kristaller486 Ramikan-BR not-lain badayvedat wjmcat FAU57 davide221 gogo8232 cnmoro dillfrescott netynet adamelliotfields cloudyu

yushun0410 sted97 AdemB jakemannix Ramikan-BR Jaward wjmcat secretmoon dillfrescott

AdemB Ramikan-BR ttkciar SicariusSicariiStuff dillfrescott


I started Friday with decentralized AI using Gemma-2, and it all works without blockchain. This is what I did:

 1. Pinned Gemma-2 9B in the Interplanetary Filesystem IPFS with the LoRA fine-tuning adapters.
 2. Set up a llama-ipfs server to fetch and cache the model and adapters on the fly and inference locally.

Now, I can use my on device AI platform across:

 • All my macOS automation workflows
 • All my browsers
 • My Copilot++ in VSCode
 • My Open Apple Intelligence (OAI, not to be confused with the other closed OAI owned by a nonprofit foundation and BigTech)

The llama-ipfs server’s RPC support lets me decentralize inferencing across all my devices, supercharging computing and energy efficiency.

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


Uploaded two basic SLERP merges of https://huggingface.co/princeton-nlp/Llama-3-Instruct-8B-SimPO and https://huggingface.co/UCLA-AGI/Llama-3-Instruct-8B-SPPO-Iter3, alternating the choice of base model, for people to test out and potentially use as merge fuel. (Personally, I am drawn to intelligent and attentive models, hence the experimentation.)

https://huggingface.co/grimjim/Llama-3-Instruct-8B-SPPO-Iter3-SimPO-merge
https://huggingface.co/grimjim/Llama-3-Instruct-8B-SimPO-SPPO-Iter3-merge


Florence-2, the new vision foundation model by Microsoft, can now run 100% locally in your browser on WebGPU, thanks to Transformers.js! 🤗🤯

It supports tasks like image captioning, optical character recognition, object detection, and many more! 😍 WOW!
- Demo: https://huggingface.co/spaces/Xenova/florence2-webgpu
- Models: https://huggingface.co/models?library=transformers.js&other=florence2
- Source code: https://github.com/xenova/transformers.js/tree/v3/examples/florence2-webgpu

gokaygokay John6666 badayvedat bmorphism Hoangthanh sebastianking fengjj123 louisbrulenaudet Sylvestre osanseviero sted97 Ramikan-BR sudhanshu456 lakkeo prithivMLmods JoPmt vladholubiev ralphilius sbrandeis NERDDISCO

fengjj123 John6666 osanseviero Ramikan-BR omaryshchenko lamhieu


Hi HuggingFacers!🤗
💥 If you are Bioinformaticians or Biologists, you may be familiar with BLAST, a search algorithm that allows researchers to identify the group of organisms (species, taxa...) from which DNA/Protein sequences come.
🥱 You may also be familiar with the difficulties to interpret long and multi-parametric results coming out from BLAST searches: here's where we can operate with LLMs, summarizing the outputs and/or replying to queries about them!
🧬 You can now run BLAST for 16S rRNA bacterial sequences here on HF, summarizing and/or asking questions about the results, or make sense of your online BLAST searches uploading description tables, using the last space I built: https://huggingface.co/spaces/as-cle-bert/BLAST-SummarAIzer
Have fun and may this be helpful to your research!💻


Mixture of Agents now in MLC/LMStudio/Ollama

I've been a bit obsessed with the recent MoA paper and its implementation. I've noticed a HUGE upgrade in the final output and it seems to really be a great way to harness the power of a team of different LLMs. The downside is that it can be a bit slow to generate responses with the bigger models (but worth it if you want to wait). I wanted to get faster results so I made an MLC version and it actually works out great! Much quicker and the responses definitely are better than compared to just running one.

I'm going to keep working on seeing how it can be further integrated (API endpoints, RAG, synthetic data generation, etc) and will share the stuff that I can get to work decently enough :)

https://github.com/severian42/MoA-MLC-Chat

https://github.com/severian42/MoA-Ollama-Chat

https://github.com/severian42/MoA-LMStudio-Chat


Hello!

https://www.youtube.com/watch?v=6NyDkpfNfUs

I had some feedback recently, that perhaps it would be beneficial to expand upon the fallacy dataset. I took this deeply to heart, and exploded it 10x.

https://huggingface.co/datasets/MrOvkill/fallacies-fallacy-base

Produced synthetically with *ALL* the Gemini models on Vertex AI.

*phew* This was a rush. I can promise over 8 it might have been like 16 of straight prompt/copy/paste/fix/re-splice/fix/prompt again/chug caffeine/repeat, but we got there! Thanks for egging me on, all! I appreciate being driven to work! So much better than boredom! 🤗

Have fun!


Portrait4D-v2: Pseudo Multi-View Data Creates Better 4D Head Synthesizer (2403.13570)

🚀🎭🌟 New Research Alert - Portrait4D-v2 (Avatars Collection)! 🌟🎭🚀
📄 Title: Portrait4D-v2: Pseudo Multi-View Data Creates Better 4D Head Synthesizer 🔝

📝 Description: Portrait4D-v2 is a novel method for one-shot 4D head avatar synthesis using pseudo multi-view videos and a vision transformer backbone, achieving superior performance without relying on 3DMM reconstruction.

👥 Authors: Yu Deng, Duomin Wang, and Baoyuan Wang

📄 Paper: https://huggingface.co/papers/2403.13570

🌐 GitHub Page: https://yudeng.github.io/Portrait4D-v2/
📁 Repository: https://github.com/YuDeng/Portrait-4D

📺 Video: https://www.youtube.com/watch?v=5YJY6-wcOJo

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

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

🚀 Added to the Avatars Collection: https://huggingface.co/collections/DmitryRyumin/avatars-65df37cdf81fec13d4dbac36

🔍 Keywords: Portrait4D #4DAvatar #HeadSynthesis #3DModeling #TechInnovation #DeepLearning #ComputerGraphics #ComputerVision #Innovation

DmitryRyumin prithivMLmods ucalyptus attashe Hoangthanh sebastianking fffiloni kavikumarkoneti julien-c IsmaelMousa John6666 nananie143 m-conrad-202 Ck773 3thn seanmiranda ameerazam08 victor Ramikan-BR Martins6 louisbrulenaudet

raonigabriel Hoangthanh julien-c Joze539 nananie143 3thn seanmiranda JNolet loupzeur victor Ramikan-BR Martins6 OjciecTadeusz


𝙂𝙤 𝙘𝙝𝙚𝙘𝙠 𝙤𝙪𝙩 𝙩𝙝𝙚 𝙡𝙚𝙖𝙙𝙚𝙧𝙗𝙤𝙖𝙧𝙙 👉

𝙂𝙤 𝙘𝙝𝙚𝙘𝙠 𝙤𝙪𝙩 𝙩𝙝𝙚 𝙡𝙚𝙖𝙙𝙚𝙧𝙗𝙤𝙖𝙧𝙙 👉

𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐞𝐫𝐬 𝐀𝐠𝐞𝐧𝐭𝐬 𝐫𝐞𝐚𝐜𝐡𝐞𝐬 𝐭𝐡𝐞 𝐭𝐨𝐩 𝐨𝐟 𝐆𝐀𝐈𝐀 𝐥𝐞𝐚𝐝𝐞𝐫𝐛𝐨𝐚𝐫𝐝! 🥳

We've been improving Transformers Agents a lot lately.

So with @sergeipetrov we set out to prove that it's the best agent framework out there.

To prove this, we went to beat the 𝗚𝗔𝗜𝗔 𝗹𝗲𝗮𝗱𝗲𝗿𝗯𝗼𝗮𝗿𝗱, the most comprehensive benchmark out there for evaluating LLM agents.
Its questions make you explore different flavours of pain:

🛠 𝗥𝗲𝗾𝘂𝗶𝗿𝗲 𝘂𝘀𝗶𝗻𝗴 𝘁𝗼𝗼𝗹𝘀, at least a web browser
🔢 𝗥𝗶𝗴𝗼𝗿𝗼𝘂𝘀 𝗹𝗼𝗴𝗶𝗰, many questions having strong math aspects
🖼 𝗠𝘂𝗹𝘁𝗶𝗺𝗼𝗱𝗮𝗹, the agent had to handle all file types: 🔊, 🖼, 🎬...
👣 𝗠𝘂𝗹𝘁𝗶-𝘀𝘁𝗲𝗽, with many questions requiring over 10 steps to be solved.

Some Level 3 questions are crazy hard 😳
> "In NASA’s Astronomy Picture of the Day on 2006 January 21, two astronauts are visible, with one appearing much smaller than the other. As of August 2023, out of the astronauts in the NASA Astronaut Group that the smaller astronaut was a member of, which one spent the least time in space, and how many minutes did he spend in space, rounded to the nearest minute?"
(𝘯𝘰 𝘧𝘪𝘭𝘦 𝘢𝘵𝘵𝘢𝘤𝘩𝘦𝘥 𝘰𝘧 𝘤𝘰𝘶𝘳𝘴𝘦, 𝘵𝘩𝘦 𝘢𝘨𝘦𝘯𝘵 𝘩𝘢𝘴 𝘵𝘰 𝘧𝘪𝘯𝘥 𝘢𝘭𝘭 𝘵𝘩𝘦 𝘪𝘯𝘧𝘰)

➡ We used Transformers Agents' React Code Agent, that writes its actions in code. We created a new planning component that we'll incorporate in the framework. More info soon in a blog post!

𝐑𝐞𝐬𝐮𝐥𝐭𝐬:
🚀 Our submission scores #2 overall on the test set and #1 on the validation set. On both sets we're the leading submission based on a public framework, beating Microsoft's Autogen.
🥇 On both sets we are #1 on the hardest Level 3 questions, reaching nearly 20%.

𝙂𝙤 𝙘𝙝𝙚𝙘𝙠 𝙤𝙪𝙩 𝙩𝙝𝙚 𝙡𝙚𝙖𝙙𝙚𝙧𝙗𝙤𝙖𝙧𝙙 👉 https://huggingface.co/spaces/gaia-benchmark/leaderboard


I am delighted to announce the publication of my LegalKit, a French labeled dataset built for legal ML training 🤗

This dataset comprises multiple query-document pairs (+50k) curated for training sentence embedding models within the domain of French law.

The labeling process follows a systematic approach to ensure consistency and relevance:
- Initial Query Generation: Three instances of the LLaMA-3-70B model independently generate three different queries based on the same document.
- Selection of Optimal Query: A fourth instance of the LLaMA-3-70B model, using a dedicated selection prompt, evaluates the generated queries and selects the most suitable one.
- Final Label Assignment: The chosen query is used to label the document, aiming to ensure that the label accurately reflects the content and context of the original text.

Dataset: https://huggingface.co/datasets/louisbrulenaudet/legalkit

Stay tuned for further updates and release information 🔥

@clem, if we can create an "HF for Legal" organization, similar to what exists for journalists, I am available!

Note : My special thanks to @alvdansen for their illustration models ❤

fffiloni ZeroWw Ramikan-BR ssatz davanstrien clem osanseviero baconnier

Ramikan-BR clem umseeker baconnier louisbrulenaudet LeroyDyer sbrandeis


ESM3 is up and running in a Space if you'd like to try it out: https://huggingface.co/spaces/colbyford/esm3

(You'll need to make a HF token and accept their license agreement to use the Space app.)

@AmelieSchreiber


Self-Play Preference Optimization for Language Model Alignment (2405.00675)

We've open-sourced the code and models for Self-Play Preference Optimization (SPPO)! 🚀🚀🚀
🤗paper: https://huggingface.co/papers/2405.00675
 ⭐ code: https://github.com/uclaml/SPPO
🤗models: https://huggingface.co/collections/UCLA-AGI/sppo-6635fdd844f2b2e4a94d0b9a


🤗 Hello from the Project Fluently team!

🥏 We are ready to announce a new series of Supple Diffusion models, these are new generation diffusion models (about 1-2 weeks left before release).

🦾 The new series aims to take diffusion models to the next level, with performance and versatility as the main goal.

🧐 How will our models be better than others? Firstly, we worked on the CLIP models, now they understand your requests better, it will become easier to process. Secondly, we trained the models with high quality, even better than all our previous ones. Thirdly, you won’t have to keep 20 models on your disk; only 4-6 will be enough.

🗺 Roadmap:
1. Create Supple Diffusion Small
2. Creating Supple Diffusion Medium
3. Create Supple Diffusion Large

🎆 Our models are universal for realism, and for cartoons, and for anime, and for caricatures.

💖 The project really needs your support and your recommendations and reviews, please do not hesitate to write comments under this post, thank you!

🖼 Below are demo images made with the pre-release version of Supple Diffusion Small.

ehristoforu YaTharThShaRma999 rossaai Pocholo testin1234 pshriv16 Tanayeb GPT007 rajsinghparihar Arakinas HDiffusion weiyuhsuan ganslmeier danielus Winnougan GooGooPanda dreamdrop-art Nymbo snakeying

ehristoforu YaTharThShaRma999 rossaai Tanayeb louisbrulenaudet GPT007 Winnougan sudzdpn dreamdrop-art Nymbo

ehristoforu YaTharThShaRma999 rossaai Tanayeb ManniX-ITA GPT007 Winnougan too-ai dreamdrop-art Nymbo

ehristoforu YaTharThShaRma999 rossaai NHLOCAL Tanayeb GPT007 Winnougan dreamdrop-art Nymbo

ehristoforu YaTharThShaRma999 rossaai Tanayeb GPT007 fengjj123 Winnougan dreamdrop-art Nymbo


Unsocial Intelligence: an Investigation of the Assumptions of AGI Discourse

I don't agree with some of the assertions made here, but it is an interesting paper and a good overview.

https://arxiv.org/abs/2401.13142


Per popular request, I'm working on a beginning to end LoRA training workflow blog for a style.

It will focus on dataset curation through training on a pre-determined style to give a better insight on my process.

Curious what are some questions you might have that I can try to answer in it?

YaTharThShaRma999 Noeloise louisbrulenaudet fffiloni eljanmahammadli attashe not-lain Imtiazsyed nicholaskao


I'm developing a tool to simplify finding datasets suitable for specific tasks or libraries. Although it's still a work in progress, I've compiled a collection of datasets that likely support DPO: https://huggingface.co/collections/davanstrien/probably-dpo-datasets-667c409a557fe99a9ed39f0b


It is time for some Aura.

First in our series of fully open sourced / commercially available models by @fal-ai: AuraSR - a 600M parameter upscaler based on GigaGAN.

Blog: https://blog.fal.ai/introducing-aurasr-an-open-reproduction-of-the-gigagan-upscaler-2/

HF: https://huggingface.co/fal-ai/AuraSR

Code: https://github.com/fal-ai/aura-sr

Playground: https://fal.ai/models/fal-ai/aura-sr/playground

What other models would you like to see open-sourced and commercially available? :)


Look at that 👀

Actual benchmarks have become too easy for recent models, much like grading high school students on middle school problems makes little sense. So the team worked on a new version of the Open LLM Leaderboard with new benchmarks.

Stellar work from @clefourrier @SaylorTwift and the team!

👉 Read the blog post: https://huggingface.co/spaces/open-llm-leaderboard/blog
👉 Explore the leaderboard: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard

anakin87 clefourrier SaylorTwift dillfrescott louisbrulenaudet


𝐄𝐱𝐩𝐞𝐫𝐢𝐦𝐞𝐧𝐭𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐭𝐡𝐢𝐬 𝐢𝐝𝐞𝐚 👇

𝐄𝐱𝐩𝐞𝐫𝐢𝐦𝐞𝐧𝐭𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐭𝐡𝐢𝐬 𝐢𝐝𝐞𝐚 👇

🔹llamafile 🦙🗂  to run our model locally.

🔹llamafile 🦙🗂  to run our model locally.

🌌 Creating adventures with local LLMs

What if 🤔... Homer Simpson met Spider-Man and they went on a quest for donuts? 🍩
Or if Fred Astaire and Corporal Hicks teamed up to fight xenomorphs? 👾

In the words of Karpathy, LLMs are dream machines...
they seem specially made to simulate these wild scenarios!

𝐄𝐱𝐩𝐞𝐫𝐢𝐦𝐞𝐧𝐭𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐭𝐡𝐢𝐬 𝐢𝐝𝐞𝐚 👇
Nous Research / @teknium  recently released https://huggingface.co/datasets/NousResearch/CharacterCodex:
a massive dataset with information on 16k characters, both fictional and real.
I couldn't wait to play it...

After a few attempts, I found that combining the information in this dataset with a good model (like https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) opens the doors to a myriad of chat adventures.

🛠 Stack:
🔹Haystack for orchestration 🏗
🔹llamafile 🦙🗂  to run our model locally.

📓 Check out the notebook: https://t.ly/y6jrZ
(includes a bonus 🕵 Mystery Character Quiz)


LLaVA 1.6 - 34 billion parameters - loaded as 8-bit precision caption. Uses around 37 GB VRAM on Massed Compute with our installers (31 cents per hour with SECourses coupon).  A new tutorial for this will be made hopefully soon.


Just shipped: introduction to vision language models (aka image-text-to-text) https://huggingface.co/tasks/image-text-to-text

Learn about more machine learning tasks at https://huggingface.co/tasks

dblasko gokaygokay John6666 ucsahin damerajee hewliyang


Hi everyone!
I'm Alex, I'm 16, I've been an internship at Hugging Face for a little over a week and I've already learned a lot about using and prompting LLM models.  With @victor as tutor I've just finished a space that analyzes your feelings by prompting an LLM chat model. The aim is to extend it so that it can categorize hugging face posts.

https://huggingface.co/spaces/alex-abb/LLM_Feeling_Analyzer

jgitsolutions dblasko victor nefnef Linaqruf Wauplin clem thomwolf mfuntowicz HugoLaurencon Narsil davidberenstein1957 joaogante ArthurZ SixOpen not-lain Sylvestre meganariley davanstrien pierrci osanseviero DavidGF adamelliotfields louisbrulenaudet nataliaElv lvwerra azzr-duke tengomucho gabrielmbmb julien-c eustlb sayakpaul reach-vb RoacherM agenkit Rexhaif mishig Cuiunbo VishvaNCS sted97 kramp jcudit MKSH pcuenq

Norod78 clem thomwolf OzzyGT mfuntowicz davidberenstein1957 not-lain osanseviero varuy322 reach-vb mishig frederikai pcuenq

davidberenstein1957 osanseviero reach-vb agenkit mishig pcuenq

davidberenstein1957 ArthurZ eustlb reach-vb pcuenq


Hello!

I've been in the lab playing with various data formats today, and jammed out with some plain text to produce a nice list of fallacies and their solutions from Wikipedia's List of fallacies as JSONL for data processing.

Had some bumps along the way, but me and Gemini 1.5 Pro got there in the end. I must really learn to work with Gemini 1.5 Flash more effectively in future.

https://huggingface.co/datasets/MrOvkill/fallacies-list-wikipedia

Enjoy!

```
-<3
```


Looking to move away from OpenAI's closed-source models?

We've made switching from to open-source as easy as possible with a drop-in replacement OpenAI’s chat completion endpoint. You can specify any model you'd like in just a few lines of code. We call it the OpenAI Switch Kit.

Check out the docs here: https://postgresml.org/docs/guides/opensourceai

Or the blog post here: https://postgresml.org/blog/introducing-the-openai-switch-kit-move-from-closed-to-open-source-ai-in-minutes

Happy building!


Recently, we open-sourced YaFSDP, Yandex’s tool for efficient distributed training of LLMs.

Here are some of the key ideas used in YaFSDP to provide speedup and memory savings over FSDP:
• Allocate and utilize just two buffers throughout the transformer for all collected weights to circumvent the torch memory allocator;
• Gather small normalization layers at the beginning of the iteration and average the gradients only at the end;
• Move gradient division to the very end of the backward pass.

To learn more about how YaFSDP works, check out our latest blog post: https://medium.com/yandex/yafsdp-a-tool-for-faster-llm-training-and-optimized-gpu-utilization-is-no-632b7539f5b3


Hello beautiful people.
I wanted to thank everyone that read my blogpost and I am glad to share that we have achieved 11000 readers 🥳
I couldn't have done this without you, so once again thanks a lot everyone for the support 💖
If you haven't already you can read my blog post at: https://huggingface.co/blog/not-lain/rag-chatbot-using-llama3


🌍 Cohere for AI has announced that this July and August, it is inviting researchers from around the world to join Expedition Aya, a global initiative focused on launching projects using multilingual tools like Aya 23 and Aya 101. 🌐

Participants can start by joining the Aya server, where all organization will take place. They can share ideas and connect with others on Discord and the signup sheet. Various events will be hosted to help people find potential team members. 🤝

To support the projects, Cohere API credits will be issued. 💰

Over the course of six weeks, weekly check-in calls are also planned to help teams stay on track and receive support with using Aya. 🖥

The expedition will wrap up at the end of August with a closing event to showcase everyone’s work and plan next steps. Participants who complete the expedition will also receive some Expedition Aya swag. 🎉

Links:
Join the Aya Discord: https://discord.com/invite/q9QRYkjpwk
Visit the Expedition Aya Minisite: https://sites.google.com/cohere.com/expedition-aya/home


Florence-2 has a great capability of detecting various objects in a zero-shot setting with the task prompt "<OD>". However, if you want to detect specific objects that the base model is not able to in its current form, you can easily finetune it for this particular task. Below I show how to finetune the model to detect tables in a given image, but a similar process can be applied to detect any objects. Thanks to @andito, @merve, and @SkalskiP for sharing the fix for finetuning the Florence-2 model. Please also check their great blog post at https://huggingface.co/blog/finetune-florence2.

Colab notebook: https://colab.research.google.com/drive/1Y8GVjwzBIgfmfD3ZypDX5H1JA_VG0YDL?usp=sharing
Finetuned model: https://huggingface.co/ucsahin/Florence-2-large-TableDetection

andito qubvel-hf John6666 Metin osanseviero sosoai kevinjeswani danelcsb dblasko maddosaientisuto radames


I'm decentralizing my AI end2end, from the AI model distribution to on device AI inferencing. llama-ipfs - llama.cpp integrated with Interplanetary File System for distributing peer2peer and loading AI models without the need for cloud storage or AI model Hub.

llama.cpp now supports decentralized inferencing with RPC, allowing the distribution of workload across all home devices. This functionality can be enhanced with a P2P ad-hoc VPN, enabling the extension of distributed inferencing to any device on any network.

Imagine an open-source AI that's as decentralized as a potluck dinner - everyone brings something to the table, and there's ZERO need for blockchain. It's like a digital fortress, with security and privacy baked right in, not to mention a dollop of integrity and trust. This could be the secret sauce for an enterprise AI platform, complete with an integrated IT policy. It might just be the cherry on top for the next generation of Apple Intelligence and Copilot+ PCs.

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

rvpierre Ramikan-BR graphicaldot brainhome sa8 pegak


NEW #DecentralizeAI Writing Contest, by InternetComputer.org and HackerNoon.com!  😜 https://www.contests.hackernoon.com/decentralize-ai-writing-contest 🤪

"Not going to beat centralized AI with more centralized AI." - Emad Mostaque

To enter, submit a blog post with the #decentralize-ai tag on HackerNoon.


Use GPT-4o + GPT-4-Turbo-Preview + GPT-3.5-Turbo + BingAI

https://huggingface.co/spaces/NiansuhAI/Copilot


Were you aware that we have a dedicated guide on different prompting mechanisms to improve the image generation quality? 🧨

Takes you through simple prompt engineering, prompt weighting, prompt enhancement using GPT-2, and more.

Check out the guide here 🦯
https://huggingface.co/docs/diffusers/main/en/using-diffusers/weighted_prompts

ashikurrahman GPT007 linoyts louisbrulenaudet radames chuangxinlezhi mynkchaudhry


I've noticed  some people are still downloading https://huggingface.co/neph1/sd-seer-griffin-3b
Should I make an update based on a more modern architecture? (griffin-3b is llama (1!))


📢 Interested in #LLM safety?

We have just uploaded a new version of ALERT 🚨 on ArXiv with novel insights into the weaknesses and vulnerabilities of LLMs! 👀 https://arxiv.org/abs/2404.08676

For a summary of the paper, read this blog post: https://huggingface.co/blog/sted97/alert 🤗


📣Thrilled to make public our recent work ENVISIONS !!!

- Without human annotations !
- Without Distilling Strong LLMs !
- Self-improve LLMs in the environment
- Amazing performances on agentic and reasoning tasks
- Insightful analysis on "why" questions

📝 Title: Interactive Evolution: A Neural-Symbolic Self-Training Framework For Large Language Models

📎 Repo: https://github.com/xufangzhi/ENVISIONS


🚀 KARAKURI LM 8x7B Instruct v0.1

KARAKURI Inc. has publicly released "KARAKURI LM 8x7B Instruct v0.1", the first domestic Large Language Model (LLM) in Japan to support Function calling and Retrieval-Augmented Generation (RAG). This AI agent can handle tasks across various applications autonomously, significantly reducing implementation costs compared to traditional models.

Model Features:
- Capable of autonomously choosing optimal documents and databases for various tasks.
- Applied extensively in customer support for automating responses and processes, analyzing Voice of Customer (VoC), and predicting optimal outreach timings.

Model URL:
https://huggingface.co/karakuri-ai/karakuri-lm-8x7b-instruct-v0.1

Detailed press release (in Japanese):
https://karakuri.ai/seminar/news/karakuri-lm-8x7b-instruct-v0-1/


Doraemon AI your future friend

https://hf.co/chat/assistant/667a0d8482b5bcd065dd882f


Judging the Judges: Evaluating Alignment and Vulnerabilities in
  LLMs-as-Judges (2406.12624)

𝐂𝐚𝐧 𝐋𝐋𝐌𝐬 𝐬𝐞𝐫𝐯𝐞 𝐚𝐬 𝐫𝐞𝐥𝐢𝐚𝐛𝐥𝐞 𝐣𝐮𝐝𝐠𝐞𝐬 ⚖?

𝐂𝐚𝐧 𝐋𝐋𝐌𝐬 𝐬𝐞𝐫𝐯𝐞 𝐚𝐬 𝐫𝐞𝐥𝐢𝐚𝐛𝐥𝐞 𝐣𝐮𝐝𝐠𝐞𝐬 ⚖?

𝗝𝘂𝗱𝗴𝗶𝗻𝗴 𝘁𝗵𝗲 𝗝𝘂𝗱𝗴𝗲𝘀: 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗻𝗴 𝗔𝗹𝗶𝗴𝗻𝗺𝗲𝗻𝘁 𝗮𝗻𝗱 𝗩𝘂𝗹𝗻𝗲𝗿𝗮𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀 𝗶𝗻 𝗟𝗟𝗠𝘀-𝗮𝘀-𝗝𝘂𝗱𝗴𝗲𝘀
https://huggingface.co/papers/2406.12624

𝐂𝐚𝐧 𝐋𝐋𝐌𝐬 𝐬𝐞𝐫𝐯𝐞 𝐚𝐬 𝐫𝐞𝐥𝐢𝐚𝐛𝐥𝐞 𝐣𝐮𝐝𝐠𝐞𝐬 ⚖?

We aim to identify the right metrics for evaluating Judge LLMs and understand their sensitivities to prompt guidelines, engineering, and specificity. With this paper, we want to raise caution ⚠ to blindly using LLMs as human proxy.

Blog - https://huggingface.co/blog/singh96aman/judgingthejudges
Arxiv - https://arxiv.org/abs/2406.12624
Tweet - https://x.com/iamsingh96aman/status/1804148173008703509

@singh96aman @kartik727 @Srinik-1 @sankaranv @dieuwkehupkes


How to generate LLM embeddings with open source models from Hugging Face 🤗 in PostgresML.

This article is the first in a multipart series that will show you how to build a post-modern semantic search and recommendation engine.

➡ https://postgresml.org/blog/generating-llm-embeddings-with-open-source-models-in-postgresml

PostgresML is a backend for your AI app that unifies LLMs w/ vector memory + embedding generation + reranking & pruning models — all in a single process for better performance.

We're always looking for ways to make PostgresML better — let us know what you think!


Fine-tune Florence-2 on any task 🔥

Today we release a notebook and a walkthrough blog on fine-tuning Florence-2 on DocVQA dataset @andito @SkalskiP

Blog: https://huggingface.co/blog 📕
Notebook: https://colab.research.google.com/drive/1hKDrJ5AH_o7I95PtZ9__VlCTNAo1Gjpf?usp=sharing 📖
Florence-2 is a great vision-language model thanks to it's massive dataset and small size!

This model requires conditioning through task prefixes and it's not as generalist, requiring fine-tuning on a new task, such as DocVQA 📝

We have fine-tuned the model on A100 (and one can also use a smaller GPU with smaller batch size) and saw that model picks up new tasks 🥹

See below how it looks like before and after FT 🤩
Play with the demo here https://huggingface.co/spaces/andito/Florence-2-DocVQA 🏄♀

omaryshchenko pcuenq MrDragonFox SixOpen Taylor658 KvrParaskevi jxtngx fdaudens gokaygokay osanseviero John6666 nicolay-r IsmaelMousa emanuelevivoli JoPmt CelineLind pandora-s IlyasMoutawwakil damerajee bharatxoni louisbrulenaudet ucsahin victor not-lain LeonceNsh andito fcakyon


DELLA-Merging: Reducing Interference in Model Merging through
  Magnitude-Based Sampling (2406.11617)

🎉  We are thrilled to share our work on model merging. We proposed a new approach, Della-merging, which combines expert models from various domains into a single, versatile model. Della employs a magnitude-based sampling approach to eliminate redundant delta parameters, reducing interference when merging homologous models (those fine-tuned from the same backbone).

Della outperforms existing homologous model merging techniques such as DARE and TIES. Across three expert models (LM, Math, Code) and their corresponding benchmark datasets (AlpacaEval, GSM8K, MBPP), Della achieves an improvement of 3.6 points over TIES and 1.2 points over DARE.

Paper: https://huggingface.co/papers/2406.11617
Github: https://github.com/declare-lab/della

@soujanyaporia @Tej3


Hello!

Fixed Moondream 2 Multi-Interrogation, ( Use ZeroGPU correctly, Sam. *doink* )
Located here:

 https://huggingface.co/spaces/MrOvkill/moondream-2-multi-interrogation

Also, uploaded pdox-reversed to include some new fields, my bad for not putting the Paradox name in from the start. All good now.

https://huggingface.co/datasets/MrOvkill/pdox-reversed

osanseviero Ramikan-BR dillfrescott kaltre


@dwancin Can you please reset your toggle component's space? It's stuck for some reason. Happy to help

https://huggingface.co/spaces/dwancin/gradio_toggle


A few new styles added as SDXL LoRA:

Midsommar Cartoon
A playful cartoon style featuring bold colors and a retro aesthetic. Personal favorite at the moment.
https://huggingface.co/alvdansen/midsommarcartoon
---
Wood Block XL
I've started training public domain styles to create some interesting datasets. In this case I found a group of images taken from really beautiful and colorful Japanese Blockprints.
https://huggingface.co/alvdansen/wood-block-xl
--
Dimension W
For this model I did actually end up working on an SD 1.5 model as well as an SDXL. I prefer the SDXL version, and I am still looking for parameters I am really happy with for SD 1.5. That said, both have their merits. I trained this with the short film I am working on in mind.
https://huggingface.co/alvdansen/dimension-w
https://huggingface.co/alvdansen/dimension-w-sd15


Last week, Intel's new Xeon CPUs, Sapphire Rapids (SPR), landed on Inference Endpoints and I think they got the potential to reduce the cost of your RAG pipelines 💸

Why ? Because they come with Intel® AMX support, which is a set of instructions that support and accelerate BF16 and INT8 matrix multiplications on CPU ⚡

I went ahead and built a Space to showcase how to efficiently deploy embedding models on SPR for both Retrieving and Ranking documents, with Haystack compatible components: https://huggingface.co/spaces/optimum-intel/haystack-e2e

Here's how it works:

- Document Store: A FAISS document store containing the seven-wonders dataset, embedded, indexed and stored on the Space's persistent storage to avoid unnecessary re-computation of embeddings.

- Retriever: It embeds the query at runtime and retrieves from the dataset N documents that are most semantically similar to the query's embedding.
We use the small variant of the BGE family here because we want a model that's fast to run on the entire dataset and has a small embedding space for fast similarity search. Specifically we use an INT8 quantized bge-small-en-v1.5, deployed on an Intel Sapphire Rapids CPU instance.

- Ranker: It re-embeds the retrieved documents at runtime and re-ranks them based on semantic similarity to the query's embedding. We use the large variant of the BGE family here because it's optimized for accuracy allowing us to filter the most relevant k documents that we'll use in the LLM prompt. Specifically we use an INT8 quantized bge-large-en-v1.5, deployed on an Intel Sapphire Rapids CPU instance.

Space: https://huggingface.co/spaces/optimum-intel/haystack-e2e
Retriever IE: https://huggingface.co/optimum-intel/fastrag-retriever
Ranker IE: https://huggingface.co/optimum-intel/fastrag-ranker

davidberenstein1957 louisbrulenaudet IlyasMoutawwakil anakin87 mfuntowicz lunarflu not-lain nbroad

IlyasMoutawwakil mfuntowicz lunarflu not-lain jeffboudier nbroad


So far I've implemented more accurate 👌 assessment of LLMs reasoning capabilities in Target Sentiment Analysis (zero-shot mode).  With that, recalculated tables of the related benchmark 📊 also has better separation into categories, with the following 🏆 top 🏆 performing models:

🟩 1. Proprietary models (🏆 GPT-4  🇺🇸 / GPT-3.5-0613  🇷🇺 )
🟥 2. Open and < 100B (🏆 LLaMA-3-70B)
🟧 3. Open and < 10B (🏆LLaMA-3-8B-Instruct 🇺🇸 / Qwen-2-7B-Instruct 🇷🇺)
🟨 4. Open and less 1B (🏆Flan-T5-large 🇺🇸 / Qwen2-0.5B-Instruct 🇷🇺)

Benchmark: https://github.com/nicolay-r/RuSentNE-LLM-Benchmark


I'm decentralizing my AI. I'll be using Radicle for decentralized Git and IPFS for distributing AI models.

I believe there is a significant opportunity to democratize open AI development moving forward. I appreciate that Radicle is open-source, prioritizes local operations, functions offline, seeds data peer-to-peer from my node, is programmable, and incorporates built-in security features.

IPFS is great decentralized data storage, and I have already begun seeding SLMs and LoRa adapters. Tomorrow will add my collection of LLMs, VLMs, etc models and datasets I'm actively using. I have 10Gbps fiber optics at home so my node has enough bandwidth.

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


Hey everyone!

I'm excited to share a new demo for my ChartInstruct model from our ACL 2024 paper. It excels at various chart understanding tasks like QA, captioning, open-ended QA, fact checking and more!
Thanks to Hugging Face's ZeroGPU program, the demo runs smoothly even with the model's 7B parameters!

Check it out and enjoy!

Demo: https://huggingface.co/spaces/ahmed-masry/ChartInstruct-LLama2
Model: https://huggingface.co/ahmed-masry/ChartInstruct-LLama2
Paper: https://arxiv.org/abs/2403.09028


Hello!

I've made a little evaluation dataset for LLMs that require advanced and convoluted logical reasoning. It's composed of 81 unique paradoxes, with admittedly a couple in the same category ( absolutes. ) It's available here: https://huggingface.co/datasets/MrOvkill/pdox

**Update**: I have upgraded the dataset to v3, ( don't worry about v2, it can be forgotten... ) and placed in a separate repo here:
https://huggingface.co/datasets/MrOvkill/pdox-reversed

Enjoy & Have fun!
`-<3`

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I'm very proud to have supported @CGIAR and @Digigreen in making http://Farmer.chat, an app that supports 20k smallholder farmers on a daily basis 🌾

There are ~500 million smallholder farmers globally, playing a critical role in global food security. Having access to accurate information is essential for them.

💬 An “agricultural extension service” offers technical advice on agriculture, and also supplies farmers with the necessary inputs and services to support their agricultural production.

But agriculture extension agents are not in large enough numbers to cope with all the requests, especially in countries like Kenya, India, Ethiopia, and Nigeria.

🚀 So the team set out to build an app called http://Farmer.Chat, to provide an agricultural extension service, by building on the immense knowledge accumulated by CGIAR.

✨ The app is technically impressive: behind the Whatsapp-type UX, an agent interprets the user's intent, and identifies which tool to call to best answer their request: weather API, RAG on a CGIAR-provided knowledge base, market data, etc. The RAG on the knowledge base is in itself a work of art.

🎯 A key part of building such a complex system is to be able to evaluate it properly. During our bi-weekly sessions with the team, I could support them in  implementing the method called "LLM-as-a-judge" to tackle this problem.

It worked really well : thanks to the amazing work of the team, the app now successfully answered over 300 thousand requests, in 6 different languages, and it keeps growing!

➡ @Vinsingh, @rajgreen and I just wrote a blog post to describe how the app works, especially the LLM-as-a-judge system!

Read it here 👉 https://huggingface.co/blog/digital-green-llm-judge


If you are like me, I like to find up and coming datasets and spaces before everyone else.

I made a trending repo space https://huggingface.co/spaces/cfahlgren1/trending-repos where it shows:

- New up and coming Spaces in the last day
- New up and coming Datasets in the last 2 weeks

It's a really good way to find some new gems before they become popular. For example, someone is working on a way to dynamically create assets inside a video game here: https://huggingface.co/spaces/gptcall/AI-Game-Creator


Hello, researchers! I've tried to made reading HF Daily Papers easier and made a tool that does reviews with LLMs like Claude 3.5, GPT-4o and sometimes FLUX.

📚 Classification by topics
📅 Sorting by publication date and HF addition date
🔄 Syncing every 2 hours
💻 Hosted on GitHub
🌏 English, Russian, and Chinese
📈 Top by week/month (in progress)

👉 https://hfday.ru

Let me know what do you think of it.

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Hugging Face Hub Python library now comes with easy inference for vision language models! ✨

$ pip install huggingface_hub 🤗

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Good folks from @Microsoft have released an exciting breakthrough in GUI automation!

OmniParser – a game-changing approach for pure vision-based GUI agents that works across multiple platforms and applications.

Key technical innovations:
- Custom-trained interactable icon detection model using 67k screenshots from popular websites
- Specialized BLIP-v2 model fine-tuned on 7k icon-description pairs for extracting functional semantics
- Novel combination of icon detection, OCR, and semantic understanding to create structured UI representations

The results are impressive:
- Outperforms GPT-4V baseline by significant margins on the ScreenSpot benchmark
- Achieves 73% accuracy on Mind2Web without requiring HTML data
- Demonstrates a 57.7% success rate on AITW mobile tasks

What makes OmniParser special is its ability to work across platforms (mobile, desktop, web) using only screenshot data – no HTML or view hierarchy needed. This opens up exciting possibilities for building truly universal GUI automation tools.

The team has open-sourced both the interactable region detection dataset and icon description dataset to accelerate research in this space.

Kudos to the Microsoft Research team for pushing the boundaries of what's possible with pure vision-based GUI understanding!

What are your thoughts on vision-based GUI automation?


Allegro: New Open Source SOTA Text to Image Model - 27 Amazing Examples With Prompts, Apache 2.0 License - Models and inference code published already

Video to watch all : https://www.youtube.com/watch?v=0tsLqNXQ5Mk

Official repo : https://github.com/rhymes-ai/Allegro

Hugging Face : https://huggingface.co/rhymes-ai/Allegro

Paper : https://arxiv.org/abs/2410.15458