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albertvillanova
updated
a
dataset
4 days ago
albertvillanova
posted
an
update
5 days ago
Post
1651
Discover all the improvements in the new version of Lighteval: https://huggingface.co./docs/lighteval/
clefourrier
authored
a
paper
about 1 month ago
SaylorTwift
posted
an
update
about 2 months ago
Post
415
How do I test an LLM for my unique needs?
If you work in finance, law, or medicine, generic benchmarks are not enough.
This blog post uses Argilla, Distilllabel and 🌤️Lighteval to generate evaluation dataset and evaluate models.
https://github.com/argilla-io/argilla-cookbook/blob/main/domain-eval/README.md
If you work in finance, law, or medicine, generic benchmarks are not enough.
This blog post uses Argilla, Distilllabel and 🌤️Lighteval to generate evaluation dataset and evaluate models.
https://github.com/argilla-io/argilla-cookbook/blob/main/domain-eval/README.md
albertvillanova
posted
an
update
about 2 months ago
Post
1515
🚨 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!
👉 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
2 months ago
Post
1561
🚀 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 🌐
🛠️ 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
Post
3140
🚀 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?
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
Post
1232
🚨 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
3 months ago
Post
1923
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! 👇
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
3 months ago
Post
1959
🚨 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!
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
4 months ago
Post
1527
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
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
clefourrier
authored
2
papers
7 months ago
albertvillanova
posted
an
update
8 months ago
Post
2702
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
🛠️ 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
8 months ago
Post
4061
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
albertvillanova
posted
an
update
8 months ago
Post
1663
🚀 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! 💥
🔥 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
Post
2581
Do I need to make it a tradition to post here every Friday? Well, here we are again!
This week, I'm happy to share that we have two official Mistral models on the Leaderboard! 🔥 You can check them out: mistralai/Mixtral-8x22B-Instruct-v0.1 and mistralai/Mixtral-8x22B-v0.1
The most exciting thing here? mistralai/Mixtral-8x22B-Instruct-v0.1 model got a first place among pretrained models with an impressive average score of 79.15!🥇 Not far behind is the Mixtral-8x22B-v0.1, achieving second place with an average score of 74.47! Well done, Mistral AI! 👏
Check out my screenshot here or explore it yourself at the https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard
The second news is that CohereForAI/c4ai-command-r-plus model in 4-bit quantization got a great average score of 70.08. Cool stuff, Cohere! 😎 (and I also have the screenshot for this, don't miss it)
The last news, which might seem small but is still significant, the Leaderboard frontpage now supports Python 3.12.1. This means we're on our way to speed up the Leaderboard's performance! 🚀
If you have any comments or suggestions, feel free to also tag me on X (Twitter), I'll try to help – [at]ailozovskaya
Have a nice weekend! ✨
This week, I'm happy to share that we have two official Mistral models on the Leaderboard! 🔥 You can check them out: mistralai/Mixtral-8x22B-Instruct-v0.1 and mistralai/Mixtral-8x22B-v0.1
The most exciting thing here? mistralai/Mixtral-8x22B-Instruct-v0.1 model got a first place among pretrained models with an impressive average score of 79.15!🥇 Not far behind is the Mixtral-8x22B-v0.1, achieving second place with an average score of 74.47! Well done, Mistral AI! 👏
Check out my screenshot here or explore it yourself at the https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard
The second news is that CohereForAI/c4ai-command-r-plus model in 4-bit quantization got a great average score of 70.08. Cool stuff, Cohere! 😎 (and I also have the screenshot for this, don't miss it)
The last news, which might seem small but is still significant, the Leaderboard frontpage now supports Python 3.12.1. This means we're on our way to speed up the Leaderboard's performance! 🚀
If you have any comments or suggestions, feel free to also tag me on X (Twitter), I'll try to help – [at]ailozovskaya
Have a nice weekend! ✨
- 2 replies
clefourrier
posted
an
update
9 months ago
Post
5546
In a basic chatbots, errors are annoyances. In medical LLMs, errors can have life-threatening consequences 🩸
It's therefore vital to benchmark/follow advances in medical LLMs before even thinking about deployment.
This is why a small research team introduced a medical LLM leaderboard, to get reproducible and comparable results between LLMs, and allow everyone to follow advances in the field.
openlifescienceai/open_medical_llm_leaderboard
Congrats to @aaditya and @pminervini !
Learn more in the blog: https://huggingface.co./blog/leaderboard-medicalllm
It's therefore vital to benchmark/follow advances in medical LLMs before even thinking about deployment.
This is why a small research team introduced a medical LLM leaderboard, to get reproducible and comparable results between LLMs, and allow everyone to follow advances in the field.
openlifescienceai/open_medical_llm_leaderboard
Congrats to @aaditya and @pminervini !
Learn more in the blog: https://huggingface.co./blog/leaderboard-medicalllm
clefourrier
posted
an
update
9 months ago
Post
4541
Contamination free code evaluations with LiveCodeBench! 🖥️
LiveCodeBench is a new leaderboard, which contains:
- complete code evaluations (on code generation, self repair, code execution, tests)
- my favorite feature: problem selection by publication date 📅
This feature means that you can get model scores averaged only on new problems out of the training data. This means... contamination free code evals! 🚀
Check it out!
Blog: https://huggingface.co./blog/leaderboard-livecodebench
Leaderboard: livecodebench/leaderboard
Congrats to @StringChaos @minimario @xu3kev @kingh0730 and @FanjiaYan for the super cool leaderboard!
LiveCodeBench is a new leaderboard, which contains:
- complete code evaluations (on code generation, self repair, code execution, tests)
- my favorite feature: problem selection by publication date 📅
This feature means that you can get model scores averaged only on new problems out of the training data. This means... contamination free code evals! 🚀
Check it out!
Blog: https://huggingface.co./blog/leaderboard-livecodebench
Leaderboard: livecodebench/leaderboard
Congrats to @StringChaos @minimario @xu3kev @kingh0730 and @FanjiaYan for the super cool leaderboard!
clefourrier
posted
an
update
9 months ago
Post
2213
🆕 Evaluate your RL agents - who's best at Atari?🏆
The new RL leaderboard evaluates agents in 87 possible environments (from Atari 🎮 to motion control simulations🚶and more)!
When you submit your model, it's run and evaluated in real time - and the leaderboard displays small videos of the best model's run, which is super fun to watch! ✨
Kudos to @qgallouedec for creating and maintaining the leaderboard!
Let's find out which agent is the best at games! 🚀
open-rl-leaderboard/leaderboard
The new RL leaderboard evaluates agents in 87 possible environments (from Atari 🎮 to motion control simulations🚶and more)!
When you submit your model, it's run and evaluated in real time - and the leaderboard displays small videos of the best model's run, which is super fun to watch! ✨
Kudos to @qgallouedec for creating and maintaining the leaderboard!
Let's find out which agent is the best at games! 🚀
open-rl-leaderboard/leaderboard