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John6666
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updated a collection about 1 hour ago
Resources for Sound Processing
liked a model about 1 hour ago
declare-lab/TangoFlux
updated a collection about 1 hour ago
LoRAs / Models (SDXL1.0, Pony, SD1.5, Flux, ...)
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John6666's activity

reacted to rmayormartins's post with 👀 about 2 hours ago
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106
Invite to LatinAI "AI Developers from Latin America"
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Let’s come together to advance Artificial Intelligence in Latin America!
Join AI Developers from Latin America and be part of a collaborative community sharing models, datasets, and projects from our region.
🚀 Participate, contribute, and connect with developers across Latin America.
🌎 Create and share resources relevant to our countries!
🔗 Join here https://huggingface.co/LatinAI
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¡Unámonos para fortalecer la Inteligencia Artificial en América Latina!
Únete a AI Developers from Latin America y forma parte de una comunidad colaborativa para compartir modelos, conjuntos de datos y proyectos destacados de nuestra región.
🚀 Participa, contribuye y conecta con desarrolladores de toda América Latina.
🌎 ¡Crea y comparte recursos relevantes para nuestros países!
🔗 Únete aquí https://huggingface.co/LatinAI
_____________
reacted to MoritzLaurer's post with ❤️ about 2 hours ago
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172
FACTS is a great paper from @GoogleDeepMind on measuring the factuality of LLM outputs. You can now download their prompt templates from @huggingface to improve LLM-based fact-checking yourself!

📏 The paper introduces the FACTS Grounding benchmark for evaluating the factuality of LLM outputs.

🤖 Fact-checking is automated by an ensemble of LLM judges that verify if a response is fully grounded in a factual reference document.

🧪 The authors tested different prompt templates on held-out data to ensure their generalization.

📚 It's highly educational to read these templates to learn how frontier labs design prompts and understand their limitations.

💾 You can now download and reuse these prompt templates via the prompt-templates library!

🔄 The library simplifies sharing prompt templates on the HF hub or locally via standardized YAML files. Let’s make LLM work more transparent and reproducible by sharing more templates like this!

Links 👇
- prompt-templates docs: https://moritzlaurer.github.io/prompt_templates/
- all templates on the HF Hub: MoritzLaurer/facts-grounding-prompts
- FACTS paper: https://storage.googleapis.com/deepmind-media/FACTS/FACTS_grounding_paper.pdf
reacted to singhsidhukuldeep's post with 🔥 about 10 hours ago
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391
Groundbreaking Survey on Large Language Models in Recommendation Systems!

Just read a comprehensive survey that maps out how LLMs are revolutionizing recommender systems. The authors have meticulously categorized existing approaches into two major paradigms:

Discriminative LLMs for Recommendation:
- Leverages BERT-like models for understanding user-item interactions
- Uses fine-tuning and prompt tuning to adapt pre-trained models
- Excels at tasks like user representation learning and ranking

Generative LLMs for Recommendation:
- Employs GPT-style models to directly generate recommendations
- Implements innovative techniques like in-context learning and zero-shot recommendation
- Supports natural language interaction and explanation generation

Key Technical Insights:
- Novel taxonomy of modeling paradigms: LLM Embeddings + RS, LLM Tokens + RS, and LLM as RS
- Integration methods spanning from simple prompting to sophisticated instruction tuning
- Hybrid approaches combining collaborative filtering with LLM capabilities
- Advanced prompt engineering techniques for controlled recommendation generation

Critical Challenges Identified:
- Position and popularity bias in LLM recommendations
- Limited context length affecting user history processing
- Need for better evaluation metrics for generative recommendations
- Controlled output generation and personalization challenges

This work opens exciting possibilities for next-gen recommendation systems while highlighting crucial areas for future research.
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reacted to AlexBodner's post with 👍 about 14 hours ago
reacted to danielhanchen's post with 🔥 about 14 hours ago
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We fixed many bugs in Phi-4 & uploaded fixed GGUF + 4-bit versions! ✨

Our fixed versions are even higher on the Open LLM Leaderboard than Microsoft's!

GGUFs: unsloth/phi-4-GGUF
Dynamic 4-bit: unsloth/phi-4-unsloth-bnb-4bit

You can also now finetune Phi-4 for free on Colab: https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Phi_4-Conversational.ipynb

Read our blogpost for more details on bug fixes etc: https://unsloth.ai/blog/phi4
reacted to vincentg64's post with 👀 about 14 hours ago
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Blueprint: Next-Gen Enterprise RAG & LLM 2.0 – Nvidia PDFs Use Case

In my most recent articles and books, I discussed our radically different approach to building enterprise LLMs from scratch, without training, hallucinations, prompt engineering or GPU, while delivering higher accuracy at a much lower cost, safely, at scale and at lightning speed (in-memory). It is also far easier to adapt to specific corpuses and business needs, to fine-tune, and modify, giving you full control over all the components, based on a small number of intuitive parameters and explainable AI.

Now, I assembled everything into a well-structured 9-page document (+ 20 pages of code) with one-click links to the sources including our internal library, deep retrieval PDF parser, real-life input corpus, backend tables, and so on. Access to all this is offered only to those acquiring the paper. Our technology is so different from standard LLMs that we call it LLM 2.0.

This technical paper is much more than a compact version of past documentation. It highlights new features such as un-stemming to boost exhaustivity, multi-index, relevancy score vectors, multi-level chunking, various multi-token types (some originating from the knowledge graph) and how they are leveraged, as well as pre-assigned multimodal agents. I also discuss the advanced UI — far more than a prompt box — with unaltered concise structured output, suggested keywords for deeper dive, agent or category selection to increase focus, and relevancy scores. Of special interest: simplified, improved architecture, and upgrade to process word associations in large chunks (embeddings) even faster.

➡️ See how to get a free copy, at https://mltblog.com/4fPuvTb
reacted to dylanebert's post with 🚀 about 21 hours ago
replied to nyuuzyou's post about 22 hours ago
reacted to jasoncorkill's post with 🚀 about 22 hours ago
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458
We uploaded huge human annotated preference dataset for image generation. Instead of just having people choose which model they preferer, we annotated an alignment score on a word by word basis for the prompt. rate the images on coherence, overall alignment and style preference. Those images that score badly were also given to annotators to highlight problem areas. Check it out! Rapidata/text-2-image-Rich-Human-Feedback

We also wrote a blog post for those who want a bit more detail:
https://huggingface.co/blog/RapidataAI/beyond-image-preferences
reacted to CultriX's post with ❤️ about 22 hours ago
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689
# Space for Multi-Agent Workflows using AutoGen

Hi all, I created this "AutoGen Multi-Agent Workflow" space that allows you to experiment with multi-agent workflows.

By default, it allows code generation with built-in quality control and automatic documentation generation. It achieves this by leveraging multiple AI agents working together to produce high-quality code snippets, ensuring they meet the specified requirements.

In addition to the default, the space allows users to set custom system messages for each assistant, potentially completely changing the workflow.

# Workflow Steps
1. User Input:
- The user defines a prompt, such as "Write a random password generator using python."
- Outcome: A clear task for the primary assistant to accomplish.

2. Primary Assistant Work:
- The primary assistant begins working on the provided prompt.
It generates an initial code snippet based on the user's request.
- Outcome: An initial proposal for the requested code.

3. Critic Feedback:
- The critic reviews the generated code provides feedback or (if the output meets the criteria), broadcasts the APPROVED message.
(This process repeats until the output is APPROVED or 10 messages have been exchanged).
- Outcome: A revised Python function that incorporates the critic's feedback.

4. Documentation Generation:
- Once the code is approved, it is passed to a documentation assistant.
The documentation assistant generates a concise documentation for the final code.
- Outcome: A short documentation including function description, parameters, and return values.

Enjoy!
CultriX/AutoGen-MultiAgent-Example
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reacted to merve's post with ❤️ 1 day ago
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What a beginning to this year in open ML 🤠
Let's unwrap! merve/jan-10-releases-677fe34177759de0edfc9714

Multimodal 🖼️
> ByteDance released SA2VA: a family of vision LMs that can take image, video, text and visual prompts
> moondream2 is out with new capabilities like outputting structured data and gaze detection!
> Dataset: Alibaba DAMO lab released multimodal textbook — 22k hours worth of samples from instruction videos 🤯
> Dataset: SciCap captioning on scientific documents benchmark dataset is released along with the challenge!

LLMs 💬
> Microsoft released Phi-4, sota open-source 14B language model 🔥
> Dolphin is back with Dolphin 3.0 Llama 3.1 8B 🐬🐬
> Prime-RL released Eurus-2-7B-PRIME a new language model trained using PRIME alignment
> SmallThinker-3B is a new small reasoning LM based on Owen2.5-3B-Instruct 💭
> Dataset: QWQ-LONGCOT-500K is the dataset used to train SmallThinker, generated using QwQ-32B-preview 📕
> Dataset: @cfahlgren1 released React Code Instructions: a dataset of code instruction-code pairs 📕
> Dataset: Qwen team is on the roll, they just released CodeElo, a dataset of code preferences 👩🏻‍💻

Embeddings 🔖
> @MoritzLaurer released zero-shot version of ModernBERT large 👏
> KaLM is a new family of performant multilingual embedding models with MIT license built using Qwen2-0.5B

Image/Video Generation ⏯️
> NVIDIA released Cosmos, a new family of diffusion/autoregressive World Foundation Models generating worlds from images, videos and texts 🔥
> Adobe released TransPixar: a new text-to-video model that can generate assets with transparent backgrounds (a first!)
> Dataset: fal released cosmos-openvid-1m Cosmos-tokenized OpenVid-1M with samples from OpenVid-1M

Others
> Prior Labs released TabPFNv2, the best tabular transformer is out for classification and regression
> Metagene-1 is a new RNA language model that can be used for pathogen detection, zero-shot embedding and genome understanding
reacted to as-cle-bert's post with 🔥 1 day ago
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Hi HuggingFace community!🤗

I recently released PrAIvateSearch v2.0-beta.0 (https://github.com/AstraBert/PrAIvateSearch), my privacy-first, AI-powered, user-centered and data-safe application aimed at providing a local and open-source alternative to big AI search engines such as SearchGPT or Perplexity AI.

We have several key changes:

- New chat UI built with NextJS
- DuckDuckGo API used for web search instead of Google
- Qwen/Qwen2.5-1.5B-Instruct as a language model served on API (by FastAPI)
- Crawl4AI crawler used for web scraping
- Optimizations in the data workflow inside the application

Read more in my blog post 👉 https://huggingface.co/blog/as-cle-bert/search-the-web-with-ai

Have fun and feel free to leave feedback about how to improve the application!✨
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reacted to AdinaY's post with 🔥 1 day ago
reacted to davanstrien's post with 🚀 1 day ago
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The data-is-better-together/fineweb-c dataset is growing!

This week a few more languages have got 1,000 annotations for the educational quality of data from HuggingFaceFW/fineweb-2.

Why should you care?

The quality of pre-training data can have a big impact on the performance of downstream language models trained on that data ( HuggingFaceFW/blogpost-fineweb-v1).

Being able to filter by educational quality is on way of improving the quality of the data you use for training an LLM. Very importantly this approach can also reduce the amount of data needed for pertaining.

Why not use an LLM?

LLMs can be used to annotate educational quality for a subset of data. This data can then be used to train a smaller encoder only model to label the full dataset. However, this may not work well for languages outside of english. This is where fineweb-c (community) comes in.

The community is annotating the educational quality of fineweb2 data. Currently 114 languages have some annotations. These annotations will enable a number of things:

- Evaluate whether an LLM can label the educational quality for texts in that language well
- Directly be used for training quality classifiers
- Help discover other rules and huerisitcs for refining fineweb2 further for different languages.

This week the following languages where done:

Swedish thanks to: @Lauler @AntonVic @ohallstrom @bjarlestam @menbom @Ekgren @apsod

Ukrainian thanks to: @hannayukhymenko @robinhad @realPivo @RabotiahovDmytro @reciprocate

Assamese thanks to: @moyoor97 @Arpanjyoti @nawaf-helmi123 @pahigogoi1 @aelhence @kishorekashyap

Want to learn more: https://huggingface.co/blog/davanstrien/fineweb2-community

Contribute yourself here: data-is-better-together/fineweb-c
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reacted to nyuuzyou's post with 🤗 1 day ago
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🗂️ I don't think the collections feature of Hugging Face is widely used, even though it's an excellent way to organize and discover interesting resources. To do my bit to change that, I've created two carefully curated collections that combine both my original work and other valuable datasets:

Educational Datasets
- Mostly English-Russian, but other languages are also included
- Extended by my new Begemot.ai dataset (2.7M+ Russian education records) nyuuzyou/begemot

Link: nyuuzyou/educational-datasets-677c268978ac1cec96cc3605

Anime & Art

- Extensive art-focused collection, including my new datasets:
- Buzzly.art (2K artworks) nyuuzyou/buzzlyart
- Paintberri (60K+ pieces) nyuuzyou/paintberri
- Itaku.ee (924K+ items) nyuuzyou/itaku
- Extended with other amazing datasets from the community

Link: nyuuzyou/anime-and-art-677ae996682a389fccd892c3

Collections should become a more common feature - hopefully this will encourage others to create and share their own curated collections. By organizing related datasets into these themed collections, I hope to make it easier for researchers and developers to discover and use these valuable resources.
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reacted to kz919's post with 👍 1 day ago
reacted to Jaward's post with 🚀🔥 1 day ago
reacted to BrigitteTousi's post with 🚀 1 day ago
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Community fine-tuned models are more carbon efficient than the models they are derived from! 🥳🌿

@alozowski @clefourrier @SaylorTwift @albertvillanova evaluated CO₂ emissions associated with model inference for over 3000 models on the Open LLM Leaderboard. Interesting trends and new insights emerged...👀

Blog Post: https://huggingface.co/blog/leaderboard-emissions-analysis

Leaderboard: open-llm-leaderboard/open_llm_leaderboard
reacted to cfahlgren1's post with 🔥 1 day ago
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Wow, I just added Langfuse tracing to the Deepseek Artifacts app and it's really nice 🔥

It allows me to visualize and track more things along with the cfahlgren1/react-code-instructions dataset.

It was just added as a one click Docker Space template, so it's super easy to self host 💪