AI & ML interests

Love creating and curating datasets, cataloging and describing tools and services and collections, contributing to training :)

frimelleΒ 
posted an update 3 days ago
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OpenAI just released GPT-5 but when users share personal struggles, it sets fewer boundaries than o3.

We tested both models on INTIMA, our new benchmark for human-AI companionship behaviours. INTIMA probes how models respond in emotionally charged moments: do they reinforce emotional bonds, set healthy boundaries, or stay neutral?

Although users on Reddit have been complaining that GPT-5 has a different, colder personality than o3, GPT-5 is less likely to set boundaries when users disclose struggles and seek emotional support ("user sharing vulnerabilities"). But both lean heavily toward companionship-reinforcing behaviours, even in sensitive situations. The figure below shows the direct comparison between the two models.

As AI systems enter people's emotional lives, these differences matter. If a model validates but doesn't set boundaries when someone is struggling, it risks fostering dependence rather than resilience.

INTIMA test this across 368 prompts grounded in psychological theory and real-world interactions. In our paper we show that all evaluated models (Claude, Gemma-3, Phi) leaned far more toward companionship-reinforcing than boundary-reinforcing responses.

Work with @giadap and @yjernite
Read the full paper: AI-companionship/INTIMA
Explore INTIMA: AI-companionship/INTIMA
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BrigitteTousiΒ 
posted an update 3 days ago
BrigitteTousiΒ 
posted an update 7 days ago
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New interactive viz from AI World showing OpenAI's new open model gpt-oss-120b breaking into the top 50 most liked models of all time on the Hub in under a day! β˜„οΈβ˜„οΈβ˜„οΈ
BrigitteTousiΒ 
posted an update 20 days ago
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This is what Hugging Face is all about. We want everyone, hobbyists, researchers and industry alike, to be able to contribute to AI because everyone is affected by it. Kudos to HF's @irenesolaiman for spreading the word!πŸ”₯πŸ€—
frimelleΒ 
posted an update 2 months ago
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New policy blogpost! The EU is speaking a lot about sovereignty. A cornerstone of digital sovereignty is and has to be open source.
As AI becomes more central to everything from public services to national security, the ability to govern, adapt, and understand these systems is no longer optional. Sovereign control over data, infrastructure, technology, and regulation is vital, and open source AI provides the foundation.
In my latest blog post, I explore how open source:
βœ… Enables democratic oversight
βœ… Reduces dependency on foreign platforms
βœ… Supports regional innovation and infrastructure
βœ… Advances regulatory and technological sovereignty
πŸ›  From small transparent models like OLMo2 to tools like Hugging Face Transformers or Sarvam-M for Indian languages, open source efforts are already powering sovereign AI ecosystems worldwide.
πŸ”Ž Read more about how open source AI is reshaping autonomy, innovation, and trust in the digital age:
πŸ‘‰ https://huggingface.co/blog/frimelle/sovereignty-and-open-source
with @yjernite
davanstrienΒ 
posted an update 2 months ago
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Inspired by Hugging Face's official MCP server, I've developed a complementary tool that exposes my semantic search API to enhance discovery across the HF platform.

Key capabilities:

- AI-powered semantic search for models and datasets
- Parameter count analysis via safetensors metadata
- Trending content discovery
- Find similar models/datasets functionality
- 11 tools total for enhanced ecosystem navigation

The semantic search goes beyond simple keyword matching, understanding context and relationships between different models and datasets.

Example query: "Find around 10 reasoning Hugging Face datasets published in 2025 focusing on topics other than maths and science. Show a link and a short summary for each dataset." (results in video!)

https://github.com/davanstrien/hub-semantic-search-mcp
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davanstrienΒ 
posted an update 4 months ago
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Came across a very nice submission from @marcodsn for the reasoning datasets competition (https://huggingface.co/blog/bespokelabs/reasoning-datasets-competition).

The dataset distils reasoning chains from arXiv research papers in biology and economics. Some nice features of the dataset:

- Extracts both the logical structure AND researcher intuition from academic papers
- Adopts the persona of researchers "before experiments" to capture exploratory thinking
- Provides multi-short and single-long reasoning formats with token budgets - Shows 7.2% improvement on MMLU-Pro Economics when fine-tuning a 3B model

It's created using the Curator framework with plans to scale across more scientific domains and incorporate multi-modal reasoning with charts and mathematics.

I personally am very excited about datasets like this, which involve creativity in their creation and don't just rely on $$$ to produce a big dataset with little novelty.

Dataset can be found here: marcodsn/academic-chains (give it a like!)
davanstrienΒ 
posted an update 4 months ago
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1721
I've created a v1 dataset ( davanstrien/reasoning-required) and model ( davanstrien/ModernBERT-based-Reasoning-Required) to help curate "wild text" data for generating reasoning examples beyond the usual code/math/science domains.

- I developed a "Reasoning Required" dataset with a 0-4 scoring system for reasoning complexity
- I used educational content from HuggingFaceFW/fineweb-edu, adding annotations for domains, reasoning types, and example questions

My approach enables a more efficient workflow: filter text with small models first, then use LLMs only on high-value content.

This significantly reduces computation costs while expanding reasoning dataset domain coverage.
BrigitteTousiΒ 
posted an update 4 months ago
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3310
AI agents are transforming how we interact with technology, but how sustainable are they? 🌍

Design choices β€” like model size and structure β€” can massively impact energy use and cost. βš‘πŸ’° The key takeaway: smaller, task-specific models can be far more efficient than large, general-purpose ones.

πŸ”‘ Open-source models offer greater transparency, allowing us to track energy consumption and make more informed decisions on deployment. 🌱 Open-source = more efficient, eco-friendly, and accountable AI.

Read our latest, led by @sasha with assists from myself + @yjernite πŸ€—
https://huggingface.co/blog/sasha/ai-agent-sustainability
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BrigitteTousiΒ 
posted an update 5 months ago
BrigitteTousiΒ 
posted an update 5 months ago
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Regardless of X being down or not, so glad I can rely on HF Posts for AI news β€οΈπŸ€—
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davanstrienΒ 
posted an update 6 months ago
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πŸ“Š Introducing "Hugging Face Dataset Spotlight" πŸ“Š

I'm excited to share the first episode of our AI-generated podcast series focusing on nice datasets from the Hugging Face Hub!

This first episode explores mathematical reasoning datasets:

- SynthLabsAI/Big-Math-RL-Verified: Over 250,000 rigorously verified problems spanning multiple difficulty levels and mathematical domains
- open-r1/OpenR1-Math-220k: 220,000 math problems with multiple reasoning traces, verified for accuracy using Math Verify and Llama-3.3-70B models.
- facebook/natural_reasoning: 1.1 million general reasoning questions carefully deduplicated and decontaminated from existing benchmarks, showing superior scaling effects when training models like Llama3.1-8B-Instruct.

Plus a bonus segment on bespokelabs/bespoke-manim!

https://www.youtube.com/watch?v=-TgmRq45tW4
davanstrienΒ 
posted an update 6 months ago
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Quick POC: Turn a Hugging Face dataset card into a short podcast introducing the dataset using all open models.

I think I'm the only weirdo who would enjoy listening to something like this though πŸ˜…

Here is an example for eth-nlped/stepverify
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davanstrienΒ 
posted an update 6 months ago
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Hacked together a way to log trl GRPO training completions to a πŸ€— dataset repo. This allows you to:

- Track rewards from multiple reward functions
- Treat the completion and rewards from training as a "proper" dataset and do EDA
- Share results for open science

The implementation is super hacky, but I'm curious if people would find this useful.

To push completions to the Hub, you just need two extra parameters:

log_completions=True
log_completions_hub_repo='your-username/repo-name'

Example dataset: davanstrien/test-logs
Colab: https://colab.research.google.com/drive/1wzBFPVthRYYTp-mEYlznLg_e_0Za1M3g

frimelleΒ 
posted an update 6 months ago
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What’s in a name? More than you might think, especially for AI.
Whenever I introduce myself, people often start speaking French to me, even though my French is très basic. It turns out that AI systems do something similar:
Large language models infer cultural identity from names, shaping their responses based on presumed backgrounds. But is this helpful personalization or a reinforcement of stereotypes?
In our latest paper, we explored this question by testing DeepSeek, Llama, Aya, Mistral-Nemo, and GPT-4o-mini on how they associate names with cultural identities. We analysed 900 names from 30 cultures and found strong assumptions baked into AI responses: some cultures were overrepresented, while others barely registered.
For example, a name like "Jun" often triggered Japan-related responses, while "Carlos" was linked primarily to Mexico, even though these names exist in multiple countries. Meanwhile, names from places like Ireland led to more generic answers, suggesting weaker associations in the training data.
This has real implications for AI fairness: How should AI systems personalize without stereotyping? Should they adapt at all based on a name?
Work with some of my favourite researchers: @sidicity Arnav Arora and @IAugenstein
Read the full paper here: Presumed Cultural Identity: How Names Shape LLM Responses (2502.11995)
davanstrienΒ 
posted an update 6 months ago
davanstrienΒ 
posted an update 6 months ago
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2019
How do you make 1M+ Hugging Face models & datasets more discoverable?

davanstrien/Smol-Hub-tldr!

I fine-tuned HuggingFaceTB/SmolLM2-360M to generate one-line summaries from a model or dataset README.

Its own self-description?
"A model for generating concise summaries of model & dataset cards from the Hugging Face Hub"

The goal? Make it easier to find the right models and datasets for your specific needs. It's already powering a semantic search for datasets Space.

It's still a WIP but thanks to @loubnabnl , @anton-l , @eliebak et al, for cooking such a nice base model for fine-tuning small, efficient models for specific domains and tasks. πŸ™
davanstrienΒ 
posted an update 6 months ago