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albertvillanovaΒ 
posted an update 2 days ago
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1808
πŸš€ Introducing @huggingface Open Deep-ResearchπŸ’₯

In just 24 hours, we built an open-source agent that:
βœ… Autonomously browse the web
βœ… Search, scroll & extract info
βœ… Download & manipulate files
βœ… Run calculations on data

55% on GAIA validation set! Help us improve it!πŸ’‘
https://huggingface.co/blog/open-deep-research
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frimelleΒ 
posted an update 2 days ago
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423
I was quoted in an article about the French Lucie AI in La Presse. While I love the name for obvious reasons πŸ‘€ there were still a lot of problems with the model and how and when it was deployed. Nevertheless seeing new smaller models being developed is an exciting direction for the next years of AI development to come!

https://www.lapresse.ca/affaires/techno/2025-02-02/radioscopie/lucie-l-ia-francaise-qui-ne-passe-pas-le-test.php

Also fun to see my comments in French.
frimelleΒ 
posted an update 2 days ago
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1580
Seeing AI develop has been a wild ride, from trying to explain why we'd bother to generate a single sentence with a *neural network* to explaining that AI is not a magic, all-knowing box. The recent weeks and months have been a lot of talking about how AI works; to policy makers, to other developers, but also and mainly friends and family without a technical background.

Yesterday, the first provisions of the EU AI Act came into force, and one of the the key highlights are the AI literacy requirements for organisations deploying AI systems. This isn't just a box-ticking exercise. Ensuring that employees and stakeholders understand AI systems is crucial for fostering responsible and transparent AI development. From recognising biases to understanding model limitations, AI literacy empowers individuals to engage critically with these technologies and make informed decisions.

In the context of Hugging Face, AI literacy has many facets: allowing more people to contribute to AI development, providing courses and documentation to ensuring access is possible, and accessible AI tools that empower users to better understand how AI systems function. This isn't just a regulatory milestone; it’s an opportunity to foster a culture where AI literacy becomes foundational, enabling stakeholders to recognise biases, assess model limitations, and engage critically with technology.

Embedding these principles into daily practice, and eventually extending our learnings in AI literacy to the general public, is essential for building trustworthy AI that aligns with societal values.
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albertvillanovaΒ 
posted an update about 1 month ago
albertvillanovaΒ 
posted an update 3 months ago
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1766
🚨 How green is your model? 🌱 Introducing a new feature in the Comparator tool: Environmental Impact for responsible #LLM research!
πŸ‘‰ open-llm-leaderboard/comparator
Now, you can not only compare models by performance, but also by their environmental footprint!

🌍 The Comparator calculates COβ‚‚ emissions during evaluation and shows key model characteristics: evaluation score, number of parameters, architecture, precision, type... πŸ› οΈ
Make informed decisions about your model's impact on the planet and join the movement towards greener AI!
resquito-wmfΒ 
in wikimedia/structured-wikipedia 3 months ago

Reverts timestamp data type to string

1
#10 opened 5 months ago by
albertvillanova
resquito-wmfΒ 
updated a dataset 3 months ago
resquito-wmfΒ 
in wikimedia/structured-wikipedia 3 months ago

Upload README.md
#14 opened 3 months ago by
resquito-wmf
albertvillanovaΒ 
posted an update 3 months ago
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1576
πŸš€ New feature of the Comparator of the πŸ€— Open LLM Leaderboard: now compare models with their base versions & derivatives (finetunes, adapters, etc.). Perfect for tracking how adjustments affect performance & seeing innovations in action. Dive deeper into the leaderboard!

πŸ› οΈ Here's how to use it:
1. Select your model from the leaderboard.
2. Load its model tree.
3. Choose any base & derived models (adapters, finetunes, merges, quantizations) for comparison.
4. Press Load.
See side-by-side performance metrics instantly!

Ready to dive in? πŸ† Try the πŸ€— Open LLM Leaderboard Comparator now! See how models stack up against their base versions and derivatives to understand fine-tuning and other adjustments. Easier model analysis for better insights! Check it out here: open-llm-leaderboard/comparator 🌐
albertvillanovaΒ 
posted an update 3 months ago
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3153
πŸš€ Exciting update! You can now compare multiple models side-by-side with the Hugging Face Open LLM Comparator! πŸ“Š

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?
albertvillanovaΒ 
posted an update 4 months ago
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1239
🚨 Instruct-tuning impacts models differently across families! Qwen2.5-72B-Instruct excels on IFEval but struggles with MATH-Hard, while Llama-3.1-70B-Instruct avoids MATH performance loss! Why? Can they follow the format in examples? πŸ“Š Compare models: open-llm-leaderboard/comparator
albertvillanovaΒ 
posted an update 4 months ago
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1939
Finding the Best SmolLM for Your Project

Need an LLM assistant but unsure which hashtag#smolLM to run locally? With so many models available, how can you decide which one suits your needs best? πŸ€”

If the model you’re interested in is evaluated on the Hugging Face Open LLM Leaderboard, there’s an easy way to compare them: use the model Comparator tool: open-llm-leaderboard/comparator
Let’s walk through an exampleπŸ‘‡

Let’s compare two solid options:
- Qwen2.5-1.5B-Instruct from Alibaba Cloud Qwen (1.5B params)
- gemma-2-2b-it from Google (2.5B params)

For an assistant, you want a model that’s great at instruction following. So, how do these two models stack up on the IFEval task?

What about other evaluations?
Both models are close in performance on many other tasks, showing minimal differences. Surprisingly, the 1.5B Qwen model performs just as well as the 2.5B Gemma in many areas, even though it's smaller in size! πŸ“Š

This is a great example of how parameter size isn’t everything. With efficient design and training, a smaller model like Qwen2.5-1.5B can match or even surpass larger models in certain tasks.

Looking for other comparisons? Drop your model suggestions below! πŸ‘‡
frimelleΒ 
authored a paper 4 months ago
albertvillanovaΒ 
posted an update 4 months ago
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1973
🚨 We’ve just released a new tool to compare the performance of models in the πŸ€— Open LLM Leaderboard: the Comparator πŸŽ‰
open-llm-leaderboard/comparator

Want to see how two different versions of LLaMA stack up? Let’s walk through a step-by-step comparison of LLaMA-3.1 and LLaMA-3.2. πŸ¦™πŸ§΅πŸ‘‡

1/ Load the Models' Results
- Go to the πŸ€— Open LLM Leaderboard Comparator: open-llm-leaderboard/comparator
- Search for "LLaMA-3.1" and "LLaMA-3.2" in the model dropdowns.
- Press the Load button. Ready to dive into the results!

2/ Compare Metric Results in the Results Tab πŸ“Š
- Head over to the Results tab.
- Here, you’ll see the performance metrics for each model, beautifully color-coded using a gradient to highlight performance differences: greener is better! 🌟
- Want to focus on a specific task? Use the Task filter to hone in on comparisons for tasks like BBH or MMLU-Pro.

3/ Check Config Alignment in the Configs Tab βš™οΈ
- To ensure you’re comparing apples to apples, head to the Configs tab.
- Review both models’ evaluation configurations, such as metrics, datasets, prompts, few-shot configs...
- If something looks off, it’s good to know before drawing conclusions! βœ…

4/ Compare Predictions by Sample in the Details Tab πŸ”
- Curious about how each model responds to specific inputs? The Details tab is your go-to!
- Select a Task (e.g., MuSR) and then a Subtask (e.g., Murder Mystery) and then press the Load Details button.
- Check out the side-by-side predictions and dive into the nuances of each model’s outputs.

5/ With this tool, it’s never been easier to explore how small changes between model versions affect performance on a wide range of tasks. Whether you’re a researcher or enthusiast, you can instantly visualize improvements and dive into detailed comparisons.

πŸš€ Try the πŸ€— Open LLM Leaderboard Comparator now and take your model evaluations to the next level!
KingNishΒ 
posted an update 4 months ago
KingNishΒ 
posted an update 4 months ago
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8243
Exciting news! Introducing super-fast AI video assistant, currently in beta. With a minimum latency of under 500ms and an average latency of just 600ms.

DEMO LINK:
KingNish/Live-Video-Chat
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sdelbecqueΒ 
updated a dataset 4 months ago
KingNishΒ 
posted an update 5 months ago
albertvillanovaΒ 
posted an update 5 months ago
KingNishΒ 
posted an update 5 months ago
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3585
Mistral Nemo is better than many models in 1st grader level reasoning.