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Democratizing access to useful AI tools and resources for journalists

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reach-vb  authored a paper 9 days ago
SmolVLM: Redefining small and efficient multimodal models
fdaudens  updated a Space 13 days ago
JournalistsonHF/first-llm-classifier
fdaudens  published a Space 21 days ago
JournalistsonHF/first-llm-classifier
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JournalistsonHF's activity

clem 
posted an update about 4 hours ago
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You can now bill your inference costs from all our inference partners (together, fireworks, fal, sambanova, cerebras, hyperbolic,...) to your Hugging Face organization.

Useful to drive more company-wide usage of AI without the billing headaches!
yjernite 
posted an update about 4 hours ago
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Today in Privacy & AI Tooling - introducing a nifty new tool to examine where data goes in open-source apps on 🤗

HF Spaces have tons (100Ks!) of cool demos leveraging or examining AI systems - and because most of them are OSS we can see exactly how they handle user data 📚🔍

That requires actually reading the code though, which isn't always easy or quick! Good news: code LMs have gotten pretty good at automatic review, so we can offload some of the work - here I'm using Qwen/Qwen2.5-Coder-32B-Instruct to generate reports and it works pretty OK 🙌

The app works in three stages:
1. Download all code files
2. Use the Code LM to generate a detailed report pointing to code where data is transferred/(AI-)processed (screen 1)
3. Summarize the app's main functionality and data journeys (screen 2)
4. Build a Privacy TLDR with those inputs

It comes with a bunch of pre-reviewed apps/Spaces, great to see how many process data locally or through (private) HF endpoints 🤗

Note that this is a POC, lots of exciting work to do to make it more robust, so:
- try it: yjernite/space-privacy
- reach out to collab: yjernite/space-privacy
thomwolf 
posted an update 2 days ago
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If you've followed the progress of robotics in the past 18 months, you've likely noticed how robotics is increasingly becoming the next frontier that AI will unlock.

At Hugging Face—in robotics and across all AI fields—we believe in a future where AI and robots are open-source, transparent, and affordable; community-built and safe; hackable and fun. We've had so much mutual understanding and passion working with the Pollen Robotics team over the past year that we decided to join forces!

You can already find our open-source humanoid robot platform Reachy 2 on the Pollen website and the Pollen community and people here on the hub at pollen-robotics

We're so excited to build and share more open-source robots with the world in the coming months!
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Kseniase 
posted an update 4 days ago
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16 new research on inference-time scaling:

For the last couple of weeks a large amount of studies on inference-time scaling has emerged. And it's so cool, because each new paper adds a trick to the toolbox, making LLMs more capable without needing to scale parameter count of the models.

So here are 13 new methods + 3 comprehensive studies on test-time scaling:

1. Inference-Time Scaling for Generalist Reward Modeling (2504.02495)
Probably, the most popular study. It proposes to boost inference-time scalability by improving reward modeling. To enhance performance, DeepSeek-GRM uses adaptive critiques, parallel sampling, pointwise generative RM, and Self-Principled Critique Tuning (SPCT)

2. T1: Tool-integrated Self-verification for Test-time Compute Scaling in Small Language Models (2504.04718)
Allows small models to use external tools, like code interpreters and calculator, to enhance self-verification

3. Z1: Efficient Test-time Scaling with Code (2504.00810)
Proposes to train LLMs on code-based reasoning paths to make test-time scaling more efficient, limiting unnecessary tokens with a special dataset and a Shifted Thinking Window

4. GenPRM: Scaling Test-Time Compute of Process Reward Models via Generative Reasoning (2504.00891)
Introduces GenPRM, a generative PRM, that uses CoT reasoning and code verification for step-by-step judgment. With only 23K training examples, GenPRM outperforms prior PRMs and larger models

5. Can Test-Time Scaling Improve World Foundation Model? (2503.24320)
SWIFT test-time scaling framework improves World Models' performance without retraining, using strategies like fast tokenization, Top-K pruning, and efficient beam search

6. Relevance Isn't All You Need: Scaling RAG Systems With Inference-Time Compute Via Multi-Criteria Reranking (2504.07104)
Proposes REBEL for RAG systems scaling, which uses multi-criteria optimization with CoT prompting for better performance-speed tradeoffs as inference compute increases

7. $φ$-Decoding: Adaptive Foresight Sampling for Balanced Inference-Time Exploration and Exploitation (2503.13288)
Proposes a φ-Decoding strategy that uses foresight sampling, clustering and adaptive pruning to estimate and select optimal reasoning steps

Read further below 👇

Also, subscribe to the Turing Post https://www.turingpost.com/subscribe
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thomwolf 
authored 2 papers 9 days ago
Kseniase 
posted an update 11 days ago
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9 Types of AI inference

AI inference refers to the process when AI models generate predictions, classifications, or decisions based on input data and pre-trained models. It encompasses a wide range of approaches with different computational methods and deployment.

Firstly, here are 5 inference types, based on how the model reasons:

1. Probabilistic inference -> https://arxiv.org/pdf/2502.05244
Uses probability theory to reason under uncertainty. The system maintains degrees of belief over hypotheses and updates them as evidence comes in.

2. Rule-based inference -> Logicbreaks: A Framework for Understanding Subversion of Rule-based Inference (2407.00075)
Draws conclusions by applying explicit if-then rules encoded in a knowledge base. Mostly used in neurosymbolic AI.

3. Logical inference -> https://arxiv.org/abs/2009.03393
Uses formal logic to draw conclusions that are guaranteed true if the premises are. It supports theorem proving, logic programming, and tasks needing correctness, like software verification.

4. Abductive inference -> Can ChatGPT Make Explanatory Inferences? Benchmarks for Abductive Reasoning (2404.18982)
Involves forming hypotheses that would best explain a given set of observations - among multiple possible explanations, the goal is to choose the most plausible. Abduction is inherently creative and uncertain.

5. Fuzzy inference -> DCNFIS: Deep Convolutional Neuro-Fuzzy Inference System (2308.06378)
Applies fuzzy logic – reasoning with degrees of truth rather than binary true/false. Inputs are mapped to fuzzy sets with membership grades between 0 and 1.

Secondly, here are 4 inference types based on its execution contexts:

1. Batch inference -> BatchLLM: Optimizing Large Batched LLM Inference with Global Prefix Sharing and Throughput-oriented Token Batching (2412.03594)
Involves generating model predictions on large sets of data in bulk, often on a scheduled basis or as needed for analysis rather than immediate use.

2. Real-time inference -> Real-time Inference and Extrapolation via a Diffusion-inspired Temporal Transformer Operator (DiTTO) (2307.09072)
Produces outputs on-demand with minimal latency, so results are available immediately when needed.

Read further in the comments 👇
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clem 
posted an update 11 days ago
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Llama 4 is in transformers!

Fun example using the instruction-tuned Maverick model responding about two images, using tensor parallel for maximum speed.

From https://huggingface.co/blog/llama4-release
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fdaudens 
updated a Space 13 days ago
clem 
posted an update 13 days ago
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Llama models (arguably the most successful open AI models of all times) just represented 3% of total model downloads on Hugging Face in March.

People and media like stories of winner takes all & one model/company to rule them all but the reality is much more nuanced than this!

Kudos to all the small AI builders out there!
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clem 
posted an update 14 days ago
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Now in Enterprise Hub organizations, you can centralize your billing not only for HF usage but also inference through our inference partners.

Will prevent some headaches for your finance & accounting teams haha (so feel free to share that with them).
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giadap 
authored a paper 14 days ago
clem 
posted an update 16 days ago
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Before 2020, most of the AI field was open and collaborative. For me, that was the key factor that accelerated scientific progress and made the impossible possible—just look at the “T” in ChatGPT, which comes from the Transformer architecture openly shared by Google.

Then came the myth that AI was too dangerous to share, and companies started optimizing for short-term revenue. That led many major AI labs and researchers to stop sharing and collaborating.

With OAI and sama now saying they're willing to share open weights again, we have a real chance to return to a golden age of AI progress and democratization—powered by openness and collaboration, in the US and around the world.

This is incredibly exciting. Let’s go, open science and open-source AI!
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thomwolf 
posted an update 17 days ago
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The new DeepSite space is really insane for vibe-coders
enzostvs/deepsite

With the wave of vibe-coding-optimized LLMs like the latest open-source DeepSeek model (version V3-0324), you can basically prompt out-of-the-box and create any app and game in one-shot.

It feels so powerful to me, no more complex framework or under-the-hood prompt engineering to have a working text-to-app tool.

AI is eating the world and *open-source* AI is eating AI itself!

PS: and even more meta is that the DeepSite app and DeepSeek model are both fully open-source code => time to start recursively improve?

PPS: you still need some inference hosting unless you're running the 600B param model at home, so check the very nice list of HF Inference Providers for this model: deepseek-ai/DeepSeek-V3-0324
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Kseniase 
posted an update 18 days ago
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9 Multimodal Chain-of-Thought methods

How Chain-of-Thought (CoT) prompting can unlock models' full potential across images, video, audio and more? Finding special multimodal CoT techniques is the answer.

Here are 9 methods of Multimodal Chain-of-Thought (MCoT). Most of them are open-source:

1. KAM-CoT -> KAM-CoT: Knowledge Augmented Multimodal Chain-of-Thoughts Reasoning (2401.12863)
This lightweight framework combines CoT prompting with knowledge graphs (KGs) and achieves 93.87% accuracy

2. Multimodal Visualization-of-Thought (MVoT) -> Imagine while Reasoning in Space: Multimodal Visualization-of-Thought (2501.07542)
Lets models generate visual reasoning traces, using a token discrepancy loss to improve visual quality

3. Compositional CoT (CCoT) -> Compositional Chain-of-Thought Prompting for Large Multimodal Models (2311.17076)
Uses scene graph (SG) representations generated by the LMM itself to improve performance on compositional and general multimodal benchmarks

4. URSA -> URSA: Understanding and Verifying Chain-of-thought Reasoning in Multimodal Mathematics (2501.04686)
Brings System 2-style thinking to multimodal math reasoning, using a 3-module CoT data synthesis process with CoT distillation, trajectory-format rewriting and format unification

5. MM-Verify -> MM-Verify: Enhancing Multimodal Reasoning with Chain-of-Thought Verification (2502.13383)
Introduces a verification mechanism with MM-Verifier and MM-Reasoner that implements synthesized high-quality CoT data for multimodal reasoning

6. Duty-Distinct CoT (DDCoT) -> DDCoT: Duty-Distinct Chain-of-Thought Prompting for Multimodal Reasoning in Language Models (2310.16436)
Divides the reasoning responsibilities between LMs and visual models, integrating the visual recognition capabilities into the joint reasoning process

7. Multimodal-CoT from Amazon Web Services -> Multimodal Chain-of-Thought Reasoning in Language Models (2302.00923)
A two-stage framework separates rationale generation from answer prediction, allowing the model to reason more effectively using multimodal inputs

8. Graph-of-Thought (GoT) -> Beyond Chain-of-Thought, Effective Graph-of-Thought Reasoning in Large Language Models (2305.16582)
This two-stage framework models reasoning as a graph of interconnected ideas, improving performance on text-only and multimodal tasks

More in the comments👇
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clem 
posted an update 19 days ago
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What's this cool purple banner haha 😶😶😶
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clem 
posted an update 20 days ago
clem 
posted an update 21 days ago
fdaudens 
published a Space 21 days ago
giadap 
posted an update 21 days ago
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We've all become experts at clicking "I agree" without a second thought. In my latest blog post, I explore why these traditional consent models are increasingly problematic in the age of generative AI.

I found three fundamental challenges:
- Scope problem: how can you know what you're agreeing to when AI could use your data in different ways?
- Temporality problem: once an AI system learns from your data, good luck trying to make it "unlearn" it.
- Autonomy trap: the data you share today could create systems that pigeonhole you tomorrow.

Individual users shouldn't bear all the responsibility, while big tech holds all the cards. We need better approaches to level the playing field, from collective advocacy and stronger technological safeguards to establishing "data fiduciaries" with a legal duty to protect our digital interests.

Available here: https://huggingface.co/blog/giadap/beyond-consent