openslr (OpenSLR)

posted an update about 6 hours ago

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198

🚀 Introducing @huggingface Open Deep-Research💥

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

55% on GAIA validation set! Help us improve it!💡
https://huggingface.co/blog/open-deep-research

1 reply

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albertvillanova

posted an update 29 days ago

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2000

Discover all the improvements in the new version of Lighteval: https://huggingface.co/docs/lighteval/

lhoestq

authored a paper about 2 months ago

Croissant: A Metadata Format for ML-Ready Datasets

Paper • 2403.19546 • Published Mar 28, 2024 • 1

anton-l

posted an update about 2 months ago

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2362

Introducing 📐𝐅𝐢𝐧𝐞𝐌𝐚𝐭𝐡: the best public math pre-training dataset with 50B+ tokens!
HuggingFaceTB/finemath

Math remains challenging for LLMs and by training on FineMath we see considerable gains over other math datasets, especially on GSM8K and MATH.

We build the dataset by:
🛠️ carefully extracting math data from Common Crawl;
🔎 iteratively filtering and recalling high quality math pages using a classifier trained on synthetic annotations to identify math reasoning and deduction.

We conducted a series of ablations comparing the performance of Llama-3.2-3B-Base after continued pre-training on FineMath and observe notable gains compared to the baseline model and other public math datasets.

We hope this helps advance the performance of LLMs on math and reasoning! 🚀
We’re also releasing all the ablation models as well as the evaluation code.

HuggingFaceTB/finemath-6763fb8f71b6439b653482c2

lhoestq

posted an update about 2 months ago

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1791

Made a HF Dataset editor a la gg sheets here: lhoestq/dataset-spreadsheets

With Dataset Spreadsheets:
✏️ Edit datasets in the UI
🔗 Share link with collaborators
🐍 Use locally in DuckDB or Python

Available for the 100,000+ parquet datasets on HF :)

albertvillanova

posted an update 3 months ago

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1763

🚨 How green is your model? 🌱 Introducing a new feature in the Comparator tool: Environmental Impact for responsible #LLM research!
👉 open-llm-leaderboard/comparator
Now, you can not only compare models by performance, but also by their environmental footprint!

🌍 The Comparator calculates CO₂ emissions during evaluation and shows key model characteristics: evaluation score, number of parameters, architecture, precision, type... 🛠️
Make informed decisions about your model's impact on the planet and join the movement towards greener AI!

albertvillanova

posted an update 3 months ago

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1574

🚀 New feature of the Comparator of the 🤗 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!

🛠️ 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? 🏆 Try the 🤗 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 🌐

albertvillanova

posted an update 3 months ago

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3153

🚀 Exciting update! You can now compare multiple models side-by-side with the Hugging Face Open LLM Comparator! 📊

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?

albertvillanova

posted an update 3 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? 📊 Compare models: open-llm-leaderboard/comparator

albertvillanova

posted an update 4 months ago

Post

1937

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? 🤔

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 example👇

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! 📊

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! 👇

albertvillanova

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 🤗 Open LLM Leaderboard: the Comparator 🎉
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. 🦙🧵👇

1/ Load the Models' Results
- Go to the 🤗 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 📊
- 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 ⚙️
- 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! ✅

4/ Compare Predictions by Sample in the Details Tab 🔍
- 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.

🚀 Try the 🤗 Open LLM Leaderboard Comparator now and take your model evaluations to the next level!

albertvillanova

posted an update 5 months ago

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1535

Check out the new Structured #Wikipedia dataset by Wikimedia Enterprise: abstract, infobox, structured sections, main image,...

Currently in early beta (English & French). Explore it and give feedback: wikimedia/structured-wikipedia

More info: https://enterprise.wikimedia.com/blog/hugging-face-dataset/
@sdelbecque @resquito-wmf

albertvillanova

updated 2 datasets 6 months ago

openslr/openslr

Updated Aug 14, 2024 • 518 • 22

openslr/librispeech_asr

Updated Aug 14, 2024 • 13.3k • 137

lhoestq

posted an update 6 months ago

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4181

Hey ! I'm working on a 100% synthetic Dataset Hub here (you can search for any kind of datasets an the app invents them). The link is here: infinite-dataset-hub/infinite-dataset-hub

Question for the Community:

Which models should I use to generate images and audio samples for those datasets ? 🤗

4 replies

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anton-l

authored a paper 7 months ago

The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scale

Paper • 2406.17557 • Published Jun 25, 2024 • 91

albertvillanova

posted an update 9 months ago

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Easily convert your script-based datasets to Parquet and explore them in the dataset viewer. 🌟

🛠️ Use @huggingface Datasets CLI:
$ 𝚍𝚊𝚝𝚊𝚜𝚎𝚝𝚜-𝚌𝚕𝚒 𝚌𝚘𝚗𝚟𝚎𝚛𝚝_𝚝𝚘_𝚙𝚊𝚛𝚚𝚞𝚎𝚝 𝚄𝚂𝙴𝚁𝙽𝙰𝙼𝙴/𝙳𝙰𝚃𝙰𝚂𝙴𝚃_𝙽𝙰𝙼𝙴

Learn more: https://huggingface.co/docs/datasets/main/en/cli#convert-to-parquet
#Data #AI

albertvillanova

posted an update 9 months ago

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4064

Recently, the Hugging Face 🤗 datasets team met with the Language Technologies team led by Marta Villegas ( @mvillegas ) at Barcelona Supercomputing Center @BSC-LT . Eager to collaborate to promote AI across Catalan, Spanish, Basque, and Galician languages and share open-source datasets/models. 🤝 #AI #LanguageTech #OpenSource

1 reply

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albertvillanova

posted an update 9 months ago

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1667

🚀 We recently released datasets 2.19.0! 📦

🔥 What's New:
- Polars integration 🐻‍❄️
- fsspec support for conversion to JSON, CSV, and Parquet
- Mode parameter for Image feature
- CLI function to convert script-datasets to Parquet
- Dataset.take and Dataset.skip

Plus, a bunch of general improvements & bug fixes!

Check out the release notes: https://github.com/huggingface/datasets/releases/tag/2.19.0

Upgrade now and power up your data workflows! 💥

2 replies

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lhoestq

posted an update 10 months ago

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3063

✨ Easy Synthetic Dataset File Generation using LLM DataGen ! Link: https://huggingface.co/spaces/lhoestq/LLM_DataGen

features + how it works:

✍️ Generate the dataset content you want just by entering a file name
💡 Optionally specify the column names you need
💨 The dataset is streamed and generated on-the-fly in JSON Lines format
✅ Generation is constrained to always output valid JSON

How does this work ?
1/ Enter a file name
2/ The model generates column names for such a file. Using structured generation, it can generate 2 to 5 column names using lower case characters and underscores. I use a prompt that asks to generate column names for a realistic dataset and low temperature.
3/ The columns are used to update the Finite State Machine for the dataset content structured generation, so that it is used to generate JSON objects using those columns
4/ The model generates JSON objects using structured generation again, using the updated Finite State Machine. I use a prompt that asks for realistic data and a temperature of 1.

> Why update a Finite State Machine instead of re-creating one ?

Creating one can take up to 30sec, while updating one takes 0.1s (though it requires to manipulate a graph which is not easy to implement)

> Batched generation is faster, why not use it ?

Generate in batches is faster but tends to generate duplicates for this demo.
Further work can be to provide different prompts (one per sequence in the batch) to end up with a different distribution of sequences in each batch. Or implement a custom sampler that would forbid generating the same data in sequences of the same batch.

> How does structured generation work ?

I used the outlines library with transformers to to define a JSON schema that the generation has to follow. It uses a Finite State Machine with token_id as transitions.

Let me know what you think ! And feel free to duplicate/modify it to try other models/prompts or sampling methods :)

OpenSLR

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Croissant: A Metadata Format for ML-Ready Datasets

openslr/openslr

openslr/librispeech_asr

The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scale

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