Rethinking LLM Evaluation with 3C3H: AraGen Benchmark and Leaderboard
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31
Ali El Filali
alielfilali01
AI & ML interests
AI Psychometrician ? | NLP (mainly for Arabic) | Other interests include Reinforcement Learning and Cognitive sciences among others
Recent Activity
updated
a dataset
about 14 hours ago
alielfilali01/AE8B-AraTrust
published
a dataset
about 14 hours ago
alielfilali01/AE8B-AraTrust
updated
a dataset
about 18 hours ago
OALL/requests
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alielfilali01's activity
reacted to
fantos's
post with 🔥
2 days ago
Post
3918
🚀 HuggingFace Spaces Ranking Tracker - Your Complete AI Trend Analytics!
Introducing the Spaces Ranking Tracker, a comprehensive analytics dashboard that tracks and analyzes every AI application in the HuggingFace ecosystem.
✨ Key Features:
• Real-time tracking of daily ranking changes over 30 days
• Detailed analysis of top 100 trending spaces
• User-based integrated score visualization
• One-click access to space details
• Interactive rank change graphs
📊 Dashboard Components:
1. Main Dashboard
- Daily rank trend graphs
- Top 20 creators' combined score chart
- Detailed space information cards
- Real-time trending score updates
2. Space Detailed Analysis
- Creation date, current rank, and trending score
- 30-day ranking history
- Direct space access
- Custom color coding for intuitive rank display
🎯 How to Use:
• Monitor latest AI community trends
• Track your project's performance
• Discover popular AI demos
• Analyze competing projects
• Follow AI ecosystem dynamics
3. Interactive Features
- Custom filtering options
- Sorting by various metrics
- Detailed performance statistics
- Comprehensive trending scores
- Historical data tracking
Stay on top of every movement in the HuggingFace ecosystem with daily ranking updates! 👉 Try it now!
🔗 Access Dashboard: fantos/Ranking-Tracker
#HuggingFace #AI #DataVisualization #TrendAnalysis #AITrends
Introducing the Spaces Ranking Tracker, a comprehensive analytics dashboard that tracks and analyzes every AI application in the HuggingFace ecosystem.
✨ Key Features:
• Real-time tracking of daily ranking changes over 30 days
• Detailed analysis of top 100 trending spaces
• User-based integrated score visualization
• One-click access to space details
• Interactive rank change graphs
📊 Dashboard Components:
1. Main Dashboard
- Daily rank trend graphs
- Top 20 creators' combined score chart
- Detailed space information cards
- Real-time trending score updates
2. Space Detailed Analysis
- Creation date, current rank, and trending score
- 30-day ranking history
- Direct space access
- Custom color coding for intuitive rank display
🎯 How to Use:
• Monitor latest AI community trends
• Track your project's performance
• Discover popular AI demos
• Analyze competing projects
• Follow AI ecosystem dynamics
3. Interactive Features
- Custom filtering options
- Sorting by various metrics
- Detailed performance statistics
- Comprehensive trending scores
- Historical data tracking
Stay on top of every movement in the HuggingFace ecosystem with daily ranking updates! 👉 Try it now!
🔗 Access Dashboard: fantos/Ranking-Tracker
#HuggingFace #AI #DataVisualization #TrendAnalysis #AITrends
- 1 reply
reacted to
burtenshaw's
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2 days ago
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2249
Manic few days in open source AI, with game changing development all over the place. Here's a round up of the resources:
- The science team at @huggingface reproduced and open source the seek r1. https://github.com/huggingface/open-r1
- @qwen released a series of models with 1 million token context! https://qwenlm.github.io/blog/qwen2.5-1m/
- SmolVLM got even smaller with completely new variants at 256m and 500m https://huggingface.co/blog/smolervlm
There's so much you could do with these developments. Especially combining them together into agentic applications or fine-tuning them on your use case.
- The science team at @huggingface reproduced and open source the seek r1. https://github.com/huggingface/open-r1
- @qwen released a series of models with 1 million token context! https://qwenlm.github.io/blog/qwen2.5-1m/
- SmolVLM got even smaller with completely new variants at 256m and 500m https://huggingface.co/blog/smolervlm
There's so much you could do with these developments. Especially combining them together into agentic applications or fine-tuning them on your use case.
-
- 1 reply
reacted to
AdinaY's
post with 🔥🧠
9 days ago
Post
2786
BIG release by DeepSeek AI🔥🔥🔥
DeepSeek-R1 & DeepSeek-R1-Zero: two 660B reasoning models are here, alongside 6 distilled dense models (based on Llama & Qwen) for the community!
https://huggingface.co/deepseek-ai
deepseek-ai/DeepSeek-R1
✨ MIT License : enabling distillation for custom models
✨ 32B & 70B models match OpenAI o1-mini in multiple capabilities
✨ API live now! Access Chain of Thought reasoning with model='deepseek-reasoner'
DeepSeek-R1 & DeepSeek-R1-Zero: two 660B reasoning models are here, alongside 6 distilled dense models (based on Llama & Qwen) for the community!
https://huggingface.co/deepseek-ai
deepseek-ai/DeepSeek-R1
✨ MIT License : enabling distillation for custom models
✨ 32B & 70B models match OpenAI o1-mini in multiple capabilities
✨ API live now! Access Chain of Thought reasoning with model='deepseek-reasoner'
reacted to
MohamedRashad's
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23 days ago
Post
1962
The winners of Best Paper Award in NeurIPs2024 (FoundationVision)
Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale
Prediction (2404.02905) has just released a new paper called infinty:
Infinity: Scaling Bitwise AutoRegressive Modeling for High-Resolution Image Synthesis (2412.04431)
And i managed to build a space for it so anyone can try it out: MohamedRashad/Infinity
The idea of a text to image model using autoregressive archticture is quite interesting in my opinion.
Infinity: Scaling Bitwise AutoRegressive Modeling for High-Resolution Image Synthesis (2412.04431)
And i managed to build a space for it so anyone can try it out: MohamedRashad/Infinity
The idea of a text to image model using autoregressive archticture is quite interesting in my opinion.
posted
an
update
23 days ago
Post
1887
3C3H AraGen Leaderboard welcomes today
deepseek-ai/DeepSeek-V3 and 12 other models (including the late gpt-3.5 💀) to the ranking of best LLMs in Arabic !
Observations:
- DeepSeek-v3 ranked 3rd and only Open model among the top 5 !
- A 14B open model ( Qwen/Qwen2.5-14B-Instruct) outperforms gpt-3.5-turbo-0125 (from last year). This shows how much we came in advancing and supporting Arabic presence within the LLM ecosystem !
- Contrary to what observed in likelihood-acc leaderboards (like OALL/Open-Arabic-LLM-Leaderboard) further finetuned models like maldv/Qwentile2.5-32B-Instruct actually decreased the performance compared to the original model Qwen/Qwen2.5-32B-Instruct.
It's worth to note that the decrease is statiscally insignificant which imply that at best, the out-domain finetuning do not really hurts the model original capabilities acquired during pretraining.
Previous work addressed this (finetuning VS pretraining) but more investigation in this regard is required (any PhDs here ? This could be your question ...)
Check out the latest rankings: inceptionai/AraGen-Leaderboard
Observations:
- DeepSeek-v3 ranked 3rd and only Open model among the top 5 !
- A 14B open model ( Qwen/Qwen2.5-14B-Instruct) outperforms gpt-3.5-turbo-0125 (from last year). This shows how much we came in advancing and supporting Arabic presence within the LLM ecosystem !
- Contrary to what observed in likelihood-acc leaderboards (like OALL/Open-Arabic-LLM-Leaderboard) further finetuned models like maldv/Qwentile2.5-32B-Instruct actually decreased the performance compared to the original model Qwen/Qwen2.5-32B-Instruct.
It's worth to note that the decrease is statiscally insignificant which imply that at best, the out-domain finetuning do not really hurts the model original capabilities acquired during pretraining.
Previous work addressed this (finetuning VS pretraining) but more investigation in this regard is required (any PhDs here ? This could be your question ...)
Check out the latest rankings: inceptionai/AraGen-Leaderboard
reacted to
prithivMLmods's
post with 🚀
23 days ago
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5906
Reasoning SmolLM2 🚀
🎯Fine-tuning SmolLM2 on a lightweight synthetic reasoning dataset for reasoning-specific tasks. Future updates will focus on lightweight, blazing-fast reasoning models. Until then, check out the blog for fine-tuning details.
🔥Blog : https://huggingface.co/blog/prithivMLmods/smollm2-ft
🔼 Models :
+ SmolLM2-CoT-360M : prithivMLmods/SmolLM2-CoT-360M
+ Reasoning-SmolLM2-135M : prithivMLmods/Reasoning-SmolLM2-135M
+ SmolLM2-CoT-360M-GGUF : prithivMLmods/SmolLM2-CoT-360M-GGUF
🤠 Other Details :
+ Demo : prithivMLmods/SmolLM2-CoT-360M
+ Fine-tune nB : prithivMLmods/SmolLM2-CoT-360M
🎯Fine-tuning SmolLM2 on a lightweight synthetic reasoning dataset for reasoning-specific tasks. Future updates will focus on lightweight, blazing-fast reasoning models. Until then, check out the blog for fine-tuning details.
🔥Blog : https://huggingface.co/blog/prithivMLmods/smollm2-ft
🔼 Models :
+ SmolLM2-CoT-360M : prithivMLmods/SmolLM2-CoT-360M
+ Reasoning-SmolLM2-135M : prithivMLmods/Reasoning-SmolLM2-135M
+ SmolLM2-CoT-360M-GGUF : prithivMLmods/SmolLM2-CoT-360M-GGUF
🤠 Other Details :
+ Demo : prithivMLmods/SmolLM2-CoT-360M
+ Fine-tune nB : prithivMLmods/SmolLM2-CoT-360M
reacted to
merve's
post with ❤️
28 days ago
Post
4835
supercharge your LLM apps with smolagents 🔥
however cool your LLM is, without being agentic it can only go so far
enter smolagents: a new agent library by Hugging Face to make the LLM write code, do analysis and automate boring stuff!
Here's our blog for you to get started https://huggingface.co/blog/smolagents
however cool your LLM is, without being agentic it can only go so far
enter smolagents: a new agent library by Hugging Face to make the LLM write code, do analysis and automate boring stuff!
Here's our blog for you to get started https://huggingface.co/blog/smolagents
reacted to
suayptalha's
post with ❤️
about 1 month ago
Post
1926
🚀 Introducing 𝐅𝐢𝐫𝐬𝐭 𝐇𝐮𝐠𝐠𝐢𝐧𝐠 𝐅𝐚𝐜𝐞 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧 𝐨𝐟 𝐦𝐢𝐧𝐆𝐑𝐔 𝐌𝐨𝐝𝐞𝐥𝐬 from the paper 𝐖𝐞𝐫𝐞 𝐑𝐍𝐍𝐬 𝐀𝐥𝐥 𝐖𝐞 𝐍𝐞𝐞𝐝𝐞𝐝?
🖥 I have integrated 𝐧𝐞𝐱𝐭-𝐠𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧 𝐑𝐍𝐍𝐬, specifically minGRU, which offer faster performance compared to Transformer architectures, into HuggingFace. This allows users to leverage the lighter and more efficient minGRU models with the "𝐭𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐞𝐫𝐬" 𝐥𝐢𝐛𝐫𝐚𝐫𝐲 for both usage and training.
💻 I integrated two main tasks: 𝐌𝐢𝐧𝐆𝐑𝐔𝐅𝐨𝐫𝐒𝐞𝐪𝐮𝐞𝐧𝐜𝐞𝐂𝐥𝐚𝐬𝐬𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 and 𝐌𝐢𝐧𝐆𝐑𝐔𝐅𝐨𝐫𝐂𝐚𝐮𝐬𝐚𝐥𝐋𝐌.
𝐌𝐢𝐧𝐆𝐑𝐔𝐅𝐨𝐫𝐒𝐞𝐪𝐮𝐞𝐧𝐜𝐞𝐂𝐥𝐚𝐬𝐬𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧:
You can use this class for 𝐒𝐞𝐪𝐮𝐞𝐧𝐜𝐞 𝐂𝐥𝐚𝐬𝐬𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 tasks. I also trained a Sentiment Analysis model with stanfordnlp/imdb dataset.
𝐌𝐢𝐧𝐆𝐑𝐔𝐅𝐨𝐫𝐂𝐚𝐮𝐬𝐚𝐥𝐋𝐌:
You can use this class for 𝐂𝐚𝐮𝐬𝐚𝐥 𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐌𝐨𝐝𝐞𝐥 tasks such as GPT, Llama. I also trained an example model with roneneldan/TinyStories dataset. You can fine-tune and use it!
🔗 𝐋𝐢𝐧𝐤𝐬:
Models: suayptalha/mingru-676fe8d90760d01b7955d7ab
GitHub: https://github.com/suayptalha/minGRU-hf
LinkedIn Post: https://www.linkedin.com/posts/suayp-talha-kocabay_mingru-a-suayptalha-collection-activity-7278755484172439552-wNY1
📰 𝐂𝐫𝐞𝐝𝐢𝐭𝐬:
Paper Link: https://arxiv.org/abs/2410.01201
I am thankful to Leo Feng, Frederick Tung, Mohamed Osama Ahmed, Yoshua Bengio and Hossein Hajimirsadeghi for their papers.
🖥 I have integrated 𝐧𝐞𝐱𝐭-𝐠𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧 𝐑𝐍𝐍𝐬, specifically minGRU, which offer faster performance compared to Transformer architectures, into HuggingFace. This allows users to leverage the lighter and more efficient minGRU models with the "𝐭𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐞𝐫𝐬" 𝐥𝐢𝐛𝐫𝐚𝐫𝐲 for both usage and training.
💻 I integrated two main tasks: 𝐌𝐢𝐧𝐆𝐑𝐔𝐅𝐨𝐫𝐒𝐞𝐪𝐮𝐞𝐧𝐜𝐞𝐂𝐥𝐚𝐬𝐬𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 and 𝐌𝐢𝐧𝐆𝐑𝐔𝐅𝐨𝐫𝐂𝐚𝐮𝐬𝐚𝐥𝐋𝐌.
𝐌𝐢𝐧𝐆𝐑𝐔𝐅𝐨𝐫𝐒𝐞𝐪𝐮𝐞𝐧𝐜𝐞𝐂𝐥𝐚𝐬𝐬𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧:
You can use this class for 𝐒𝐞𝐪𝐮𝐞𝐧𝐜𝐞 𝐂𝐥𝐚𝐬𝐬𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 tasks. I also trained a Sentiment Analysis model with stanfordnlp/imdb dataset.
𝐌𝐢𝐧𝐆𝐑𝐔𝐅𝐨𝐫𝐂𝐚𝐮𝐬𝐚𝐥𝐋𝐌:
You can use this class for 𝐂𝐚𝐮𝐬𝐚𝐥 𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐌𝐨𝐝𝐞𝐥 tasks such as GPT, Llama. I also trained an example model with roneneldan/TinyStories dataset. You can fine-tune and use it!
🔗 𝐋𝐢𝐧𝐤𝐬:
Models: suayptalha/mingru-676fe8d90760d01b7955d7ab
GitHub: https://github.com/suayptalha/minGRU-hf
LinkedIn Post: https://www.linkedin.com/posts/suayp-talha-kocabay_mingru-a-suayptalha-collection-activity-7278755484172439552-wNY1
📰 𝐂𝐫𝐞𝐝𝐢𝐭𝐬:
Paper Link: https://arxiv.org/abs/2410.01201
I am thankful to Leo Feng, Frederick Tung, Mohamed Osama Ahmed, Yoshua Bengio and Hossein Hajimirsadeghi for their papers.
posted
an
update
about 1 month ago
Post
1916
~75% on the challenging GPQA with only 40M parameters 🔥🥳
GREAT ACHIEVEMENT ! Or is it ?
This new Work, "Data Laundering: Artificially Boosting Benchmark Results through Knowledge Distillation", take out the mystery about many models i personally suspected their results. Speacially on leaderboards other than the english one, Like the Open Arabic LLM Leaderbaord OALL/Open-Arabic-LLM-Leaderboard.
The authors of this work, first started by training a model on the GPQA data, which, unsurprisingly, led to the model achieving 100% performance.
Afterward, they trained what they referred to as a 'legitimate' model on legitimate data (MedMCQA). However, they introduced a distillation loss from the earlier, 'cheated' model.
What they discovered was fascinating: the knowledge of GPQA leaked through this distillation loss, even though the legitimate model was never explicitly trained on GPQA during this stage.
This raises important questions about the careful use of distillation in model training, especially when the training data is opaque. As they demonstrated, it’s apparently possible to (intentionally or unintentionally) leak test data through this method.
Find out more: Data Laundering: Artificially Boosting Benchmark Results through Knowledge Distillation (2412.15255)
GREAT ACHIEVEMENT ! Or is it ?
This new Work, "Data Laundering: Artificially Boosting Benchmark Results through Knowledge Distillation", take out the mystery about many models i personally suspected their results. Speacially on leaderboards other than the english one, Like the Open Arabic LLM Leaderbaord OALL/Open-Arabic-LLM-Leaderboard.
The authors of this work, first started by training a model on the GPQA data, which, unsurprisingly, led to the model achieving 100% performance.
Afterward, they trained what they referred to as a 'legitimate' model on legitimate data (MedMCQA). However, they introduced a distillation loss from the earlier, 'cheated' model.
What they discovered was fascinating: the knowledge of GPQA leaked through this distillation loss, even though the legitimate model was never explicitly trained on GPQA during this stage.
This raises important questions about the careful use of distillation in model training, especially when the training data is opaque. As they demonstrated, it’s apparently possible to (intentionally or unintentionally) leak test data through this method.
Find out more: Data Laundering: Artificially Boosting Benchmark Results through Knowledge Distillation (2412.15255)
- 1 reply
replied to
their
post
about 2 months ago
You are a HERO !
posted
an
update
about 2 months ago
Post
3453
Unpopular opinion: Open Source takes courage to do !
Not everyone is brave enough to release what they have done (the way they've done it) to the wild to be judged !
It really requires a high level of "knowing wth are you doing" ! It's kind of a super power !
Cheers to the heroes here who see this!
Not everyone is brave enough to release what they have done (the way they've done it) to the wild to be judged !
It really requires a high level of "knowing wth are you doing" ! It's kind of a super power !
Cheers to the heroes here who see this!
-
- 5 replies
reacted to
takarajordan's
post with 🔥❤️
about 2 months ago
Post
2339
I'm super excited to release my first open-source text dataset:
WorldScenario 20K is a novel dataset of 20,000 synthetically generated multi-stakeholder scenarios designed to simulate real-world decision-making processes. Each scenario explores a unique environmental, societal, or economic issue.
I used the brand new meta-llama/Llama-3.3-70B-Instruct model to generate this dataset and I put the dataset through some post processing to clean and evaluate the dataset for diversity.
I'd appreciate some feedback and thoughts on my new release! Thanks!
takarajordan/WorldScenario_20K
WorldScenario 20K is a novel dataset of 20,000 synthetically generated multi-stakeholder scenarios designed to simulate real-world decision-making processes. Each scenario explores a unique environmental, societal, or economic issue.
I used the brand new meta-llama/Llama-3.3-70B-Instruct model to generate this dataset and I put the dataset through some post processing to clean and evaluate the dataset for diversity.
I'd appreciate some feedback and thoughts on my new release! Thanks!
takarajordan/WorldScenario_20K
- 8 replies
reacted to
AdinaY's
post with 🤗
about 2 months ago
Post
892
Updates from the Chinese community last week 🔥
LLM:
✨ Sailor 2 , multilingual model supporting 10+ South Asian languages by Sea AI Lab. https://huggingface.co/sailor2
MLLM:
✨InternVL 2.5 , new open multimodal LLM by OpenGVLab
https://huggingface.co/collections/OpenGVLab/internvl-25-673e1019b66e2218f68d7c1c
✨Qwen2-VL 2B/7B/72B base model, the latest iteration of our Qwen-VL model by Alibaba Qwen
Qwen/qwen2-vl-66cee7455501d7126940800d
Video model:
✨HunyuanVideo , 13B open video model by Tencent
tencent/HunyuanVideo
Reasoning model:
✨ LLaMA-O1 🦙 base & supervised model; pretrain & finetune datasets and demo all released
zh-ai-community/reasoning-models-67409fb3aa1ed78f10087cd7
Audio model:
✨Fish Speech 1.5, Text-to-speech in 13 languages, trained on 1M+ hours of audio by FishAudio
fishaudio/fish-speech-1.5
✨ClearVoice, An advanced voice processing framework by Alibaba Tongyi SpeechAI https://huggingface.co/alibabasglab
More details 👉 https://huggingface.co/zh-ai-community
LLM:
✨ Sailor 2 , multilingual model supporting 10+ South Asian languages by Sea AI Lab. https://huggingface.co/sailor2
MLLM:
✨InternVL 2.5 , new open multimodal LLM by OpenGVLab
https://huggingface.co/collections/OpenGVLab/internvl-25-673e1019b66e2218f68d7c1c
✨Qwen2-VL 2B/7B/72B base model, the latest iteration of our Qwen-VL model by Alibaba Qwen
Qwen/qwen2-vl-66cee7455501d7126940800d
Video model:
✨HunyuanVideo , 13B open video model by Tencent
tencent/HunyuanVideo
Reasoning model:
✨ LLaMA-O1 🦙 base & supervised model; pretrain & finetune datasets and demo all released
zh-ai-community/reasoning-models-67409fb3aa1ed78f10087cd7
Audio model:
✨Fish Speech 1.5, Text-to-speech in 13 languages, trained on 1M+ hours of audio by FishAudio
fishaudio/fish-speech-1.5
✨ClearVoice, An advanced voice processing framework by Alibaba Tongyi SpeechAI https://huggingface.co/alibabasglab
More details 👉 https://huggingface.co/zh-ai-community
posted
an
update
about 2 months ago
Post
1535
Apparently i forgot to put this here !
Well, this is a bit late but consider given our recent blog a read if you are interested in Evaluation.
You don't have to be into Arabic NLP in order to read it, the main contribution we are introducing is a new evaluation measure for NLG. We made the fisrt application of this measure on Arabic for now and we will be working with colleagues from the community to expand it to other languages.
Blog:
Rethinking LLM Evaluation with 3C3H: AraGen Benchmark and Leaderboard
https://huggingface.co/blog/leaderboard-3c3h-aragen
Space:
inceptionai/AraGen-Leaderboard
Give it a read and let me know your thoughts 🤗
Well, this is a bit late but consider given our recent blog a read if you are interested in Evaluation.
You don't have to be into Arabic NLP in order to read it, the main contribution we are introducing is a new evaluation measure for NLG. We made the fisrt application of this measure on Arabic for now and we will be working with colleagues from the community to expand it to other languages.
Blog:
Rethinking LLM Evaluation with 3C3H: AraGen Benchmark and Leaderboard
https://huggingface.co/blog/leaderboard-3c3h-aragen
Space:
inceptionai/AraGen-Leaderboard
Give it a read and let me know your thoughts 🤗
reacted to
dvilasuero's
post with ❤️🔥
about 2 months ago
Post
2336
🌐 Announcing Global-MMLU: an improved MMLU Open dataset with evaluation coverage across 42 languages, built with Argilla and the Hugging Face community.
Global-MMLU is the result of months of work with the goal of advancing Multilingual LLM evaluation. It's been an amazing open science effort with collaborators from Cohere For AI, Mila - Quebec Artificial Intelligence Institute, EPFL, Massachusetts Institute of Technology, AI Singapore, National University of Singapore, KAIST, Instituto Superior Técnico, Carnegie Mellon University, CONICET, and University of Buenos Aires.
🏷️ +200 contributors used Argilla MMLU questions where regional, dialect, or cultural knowledge was required to answer correctly. 85% of the questions required Western-centric knowledge!
Thanks to this annotation process, the open dataset contains two subsets:
1. 🗽 Culturally Agnostic: no specific regional, cultural knowledge is required.
2. ⚖️ Culturally Sensitive: requires dialect, cultural knowledge or geographic knowledge to answer correctly.
Moreover, we provide high quality translations of 25 out of 42 languages, thanks again to the community and professional annotators leveraging Argilla on the Hub.
I hope this will ensure a better understanding of the limitations and challenges for making open AI useful for many languages.
Dataset: CohereForAI/Global-MMLU
Global-MMLU is the result of months of work with the goal of advancing Multilingual LLM evaluation. It's been an amazing open science effort with collaborators from Cohere For AI, Mila - Quebec Artificial Intelligence Institute, EPFL, Massachusetts Institute of Technology, AI Singapore, National University of Singapore, KAIST, Instituto Superior Técnico, Carnegie Mellon University, CONICET, and University of Buenos Aires.
🏷️ +200 contributors used Argilla MMLU questions where regional, dialect, or cultural knowledge was required to answer correctly. 85% of the questions required Western-centric knowledge!
Thanks to this annotation process, the open dataset contains two subsets:
1. 🗽 Culturally Agnostic: no specific regional, cultural knowledge is required.
2. ⚖️ Culturally Sensitive: requires dialect, cultural knowledge or geographic knowledge to answer correctly.
Moreover, we provide high quality translations of 25 out of 42 languages, thanks again to the community and professional annotators leveraging Argilla on the Hub.
I hope this will ensure a better understanding of the limitations and challenges for making open AI useful for many languages.
Dataset: CohereForAI/Global-MMLU
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If you remember my work on MAMF - to find the realistic TFLOPS achievable ceiling - the Intel AI team has shared their measurements and they scored ...
an incredible 99.4% TFLOPS efficiency for Gaudi 2!
That's quite amazing! Your ROI on these accelerators will be very high.
The full table is here: https://github.com/stas00/ml-engineering/tree/master/compute/accelerator#maximum-achievable-matmul-flops-comparison-table
As we have seen the competitors get their achievable efficiency worse with each new generation, I'm looking forward to see if Gaudi 3 will keep the high bar!
Thanks to Avi Rubin, Lakshman Chari, Imtiaz Sajwani, Ramy J and Zhiqi Tao for helping to get these numbers to the community.
an incredible 99.4% TFLOPS efficiency for Gaudi 2!
That's quite amazing! Your ROI on these accelerators will be very high.
The full table is here: https://github.com/stas00/ml-engineering/tree/master/compute/accelerator#maximum-achievable-matmul-flops-comparison-table
As we have seen the competitors get their achievable efficiency worse with each new generation, I'm looking forward to see if Gaudi 3 will keep the high bar!
Thanks to Avi Rubin, Lakshman Chari, Imtiaz Sajwani, Ramy J and Zhiqi Tao for helping to get these numbers to the community.
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2023 & 2024 Top Downloaded (all time) Open Models on the hub are both from the Chinese community 👀
2023 👉 Bge base by BAAI
BAAI/bge-base-en-v1.5
2024 👉 Qwen 2.5 by Alibaba Qwen
Qwen/Qwen2.5-1.5B-Instruct
Can’t wait to see what incredible models the Chinese community will bring in 2025🚀
✨ Follow https://huggingface.co/zh-ai-community to get the latest updates from the Chinese community
✨ Explore the 2024 Year in Review huggingface/open-source-ai-year-in-review-2024
2023 👉 Bge base by BAAI
BAAI/bge-base-en-v1.5
2024 👉 Qwen 2.5 by Alibaba Qwen
Qwen/Qwen2.5-1.5B-Instruct
Can’t wait to see what incredible models the Chinese community will bring in 2025🚀
✨ Follow https://huggingface.co/zh-ai-community to get the latest updates from the Chinese community
✨ Explore the 2024 Year in Review huggingface/open-source-ai-year-in-review-2024