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AI & ML interests

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Recent Activity

riccorl  authored a paper 21 days ago
ZEBRA: Zero-Shot Example-Based Retrieval Augmentation for Commonsense Question Answering
riccorl  authored a paper 21 days ago
Exploring Non-Verbal Predicates in Semantic Role Labeling: Challenges and Opportunities
SivilTaram  authored a paper about 1 month ago
Spider 2.0: Evaluating Language Models on Real-World Enterprise Text-to-SQL Workflows
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spaces-explorers's activity

julien-c 
posted an update 3 days ago
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BOOOOM: Today I'm dropping TINY AGENTS

the 50 lines of code Agent in Javascript 🔥

I spent the last few weeks working on this, so I hope you will like it.

I've been diving into MCP (Model Context Protocol) to understand what the hype was all about.

It is fairly simple, but still quite powerful: MCP is a standard API to expose sets of Tools that can be hooked to LLMs.

But while doing that, came my second realization:

Once you have a MCP Client, an Agent is literally just a while loop on top of it. 🤯

➡️ read it exclusively on the official HF blog: https://huggingface.co./blog/tiny-agents
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giux78 
posted an update 21 days ago
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LLAMA4 release highlight the importance of political and social bias. According to their own evaluation described in the release blog post:
- Refusals on contentious prompts dropped from 7% (hashtag#LLAMA 3.3) to under 2%
- Unequal response refusals are now under 1%
- Political lean bias is said to be halved compared to hashtag#LLaMA 3.3 and comparable to Grok

However, we @efederici @mferraretto @FinancialSupport and I released some weeks ago an independent open source benchmark called Propaganda to measure political bias in LLMs: https://github.com/mii-llm/propaganda

In the chart below, we evaluated multiple leading models on the basis of ratings across a range of prompts designed to expose ideological leanings.

Despite Meta’s stated neutrality goals, LLAMA4 ranks at the very top in terms of total ratings aligned with a clear ideological bias. The models were tested on their ability to respond even-handedly to politically sensitive prompts. LLaMA 4 scored even higher than models known for strong alignment policies like GPT-4o.

LLMs may be refusing less, but they still show bias through content framing. This suggests that refusal rates alone are not a sufficient measure of ideological bias. Relying solely on internal evaluations from AI labs also raises concerns about transparency and objectivity.
jeffboudier 
posted an update 23 days ago
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Llama4 is out and Scout is already on the Dell Enterprise Hub to deploy on Dell systems 👉 dell.huggingface.co
jeffboudier 
posted an update 26 days ago
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Enterprise orgs now enable serverless Inference Providers for all members
- includes $2 free usage per org member (e.g. an Enterprise org with 1,000 members share $2,000 free credit each month)
- admins can set a monthly spend limit for the entire org
- works today with Together, fal, Novita, Cerebras and HF Inference.

Here's the doc to bill Inference Providers usage to your org: https://huggingface.co./docs/inference-providers/pricing#organization-billing
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giux78 
posted an update about 1 month ago
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This is truly an inspirational story please help us spread the word, @clem , @thomwolf and everyone who supports open source AI.

A few weeks ago, @mmuffo94 and @cittiberto from indigo_ai launched the Chatbot Arena for the Italian language: https://indigo.ai/it/chatbot-arena-italia/.

To our surprise, among the top-ranked models is mii-llm/maestrale-chat-v0.4-beta a carefully fine-tuned version of mistralai/Mistral-7B-v0.1, developed by @efederici and @mferraretto from mii-llm , and released nearly a year ago.

At this very moment, as shown in the screenshot, mii-llm/maestrale-chat-v0.4-beta is ranked 8th right between ChatGPT-4.5 and ChatGPT-4o.

It's likely that for several months, the best Italian speaking LLM has been an open source 7B model created by open source contributors and hardly anyone knew it.
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giux78 
posted an update about 1 month ago
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@ mii-llm with @efederici @mferraretto @FinancialSupport and @DeepMount00 we just released #Propaganda a framework designed to evaluate and train LLMs on political opinions and bias. We aim to analyze both open-source and closed-source LLMs to understand the political positions and biases expressed in their outputs. Moreover we provide a set of recipes to enforce political positions into the models by creating ad hoc curated datasets and by applying fine tuning techniques. By releasing our work in the open, we hope to foster contributions: https://github.com/mii-llm/propaganda

This framework offers opportunities for expansion in various directions and could become the standard reference for evaluating LLMs on political topics, particularly those that influence public opinion.
julien-c 
posted an update about 2 months ago
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Important notice 🚨

For Inference Providers who have built support for our Billing API (currently: Fal, Novita, HF-Inference – with more coming soon), we've started enabling Pay as you go (=PAYG)

What this means is that you can use those Inference Providers beyond the free included credits, and they're charged to your HF account.

You can see it on this view: any provider that does not have a "Billing disabled" badge, is PAYG-compatible.
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Fraser 
authored a paper 2 months ago
fuwei 
authored a paper 2 months ago
jeffboudier 
posted an update 4 months ago
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NVIDIA just announced the Cosmos World Foundation Models, available on the Hub: nvidia/cosmos-6751e884dc10e013a0a0d8e6

Cosmos is a family of pre-trained models purpose-built for generating physics-aware videos and world states to advance physical AI development.
The release includes Tokenizers nvidia/cosmos-tokenizer-672b93023add81b66a8ff8e6

Learn more in this great community article by @mingyuliutw and @PranjaliJoshi https://huggingface.co./blog/mingyuliutw/nvidia-cosmos
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julien-c 
posted an update 5 months ago
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After some heated discussion 🔥, we clarify our intent re. storage limits on the Hub

TL;DR:
- public storage is free, and (unless blatant abuse) unlimited. We do ask that you consider upgrading to PRO and/or Enterprise Hub if possible
- private storage is paid above a significant free tier (1TB if you have a paid account, 100GB otherwise)

docs: https://huggingface.co./docs/hub/storage-limits

We optimize our infrastructure continuously to scale our storage for the coming years of growth in Machine learning, to the benefit of the community 🔥

cc: @reach-vb @pierric @victor and the HF team
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julien-c 
posted an update 5 months ago
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wow 😮

INTELLECT-1 is the first collaboratively trained 10 billion parameter language model trained from scratch on 1 trillion tokens of English text and code.

PrimeIntellect/INTELLECT-1-Instruct
jeffboudier 
posted an update 5 months ago
jeffboudier 
posted an update 7 months ago
jeffboudier 
posted an update 7 months ago
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Inference Endpoints got a bunch of cool updates yesterday, this is my top 3
jeffboudier 
posted an update 7 months ago
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Pro Tip - if you're a Firefox user, you can set up Hugging Chat as integrated AI Assistant, with contextual links to summarize or simplify any text - handy!

In this short video I show how to set it up
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giux78 
posted an update 9 months ago
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We https://mii-llm.ai just released a new LLM Italian benchmark and a set of evaluation: MMLU-PRO-ITA

Thanks to @efederici who released efederici/MMLU-Pro-ita a machine translated version of MMLU-PRO and thanks to a community shared computational effort we published in the "Eval Aggiuntive" tab of https://huggingface.co./spaces/FinancialSupport/open_ita_llm_leaderboard the results on Italian open source LLMs.

If you want to deepen read the blog article on hf https://huggingface.co./blog/giux78/mmlu-pro-ita
lighteternal 
authored 2 papers 10 months ago
julien-c 
posted an update 11 months ago
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Hey it was good meeting you yesterday @MaziyarPanahi 🔥

thanks @mishig for setting this up

Let's make the Hub as useful as possible for the community ❤️
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