We just crossed 1,500,000 public models on Hugging Face (and 500k spaces, 330k datasets, 50k papers). One new repository is created every 15 seconds. Congratulations all!
Another impressive model that joined the ranking today is ALLaM-AI/ALLaM-7B-Instruct-preview. After a long wait finally ALLaM is here and it is IMPRESSIVE given its size !
Google just released PaliGemma 2 Mix: new versatile instruction vision language models ๐ฅ
> Three new models: 3B, 10B, 28B with res 224, 448 ๐ > Can do vision language tasks with open-ended prompts, understand documents, and segment or detect anything ๐คฏ
๐ Excited to share our technical report on the Southeast Asian multilingual model Sailor2 and its latest updates!
Our 49-page report details Sailor2's development journey, including multilingual data cleaning, small model data mixture simulations, multi-stage continual pre-training, multi-stage post-training, and multi-cultural multi-lingual evaluations. Sailor2 aims to streamline the multilingual model pre-training process efficiently for the community.
๐งญ We highlight Sailor2's impressive performance in low-resource language translation scenarios and its cultural understanding advantages in Southeast Asia, promoting practical applications for regional languages.
Model updates include:ย ๐ก More precise outputs: Reduced redundancy in model outputs through refined post-training data and optimization techniques.ย ๐ Handling longer texts: Expanded to handle up to 128K context length in Southeast Asian languages through long-text training.ย โก๏ธ Faster inference: Achieved 2.5x faster inference speed with speculative decoding.ย ๐ช๏ธ More model sizes: Introduced new sizes of 3B and 14B through model pruning.
๐ All models are Apache-licensed for commercial use; development tools (code, resources) are open-source.
๐ 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.
โจ Key Features: โข Real-time tracking of daily ranking changes over 30 days โข Detailed analysis of top 100 trending spaces โข User-based integrated score visualization โข One-click access to space details โข Interactive rank change graphs
๐ 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
๐ฏ How to Use: โข Monitor latest AI community trends โข Track your project's performance โข Discover popular AI demos โข Analyze competing projects โข 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! ๐ Try it now!
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
ยท
reacted to AdinaY's
post with ๐ฅ๐ง about 2 months ago
โจ MIT License : enabling distillation for custom models โจ 32B & 70B models match OpenAI o1-mini in multiple capabilities โจ API live now! Access Chain of Thought reasoning with model='deepseek-reasoner'
reacted to MohamedRashad's
post with โค๏ธ2 months ago
3C3H AraGen Leaderboard welcomes today deepseek-ai/DeepSeek-V3 and 12 other models (including the late gpt-3.5 ๐) 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 ...)
๐ฏFine-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.
๐ Introducing ๐ ๐ข๐ซ๐ฌ๐ญ ๐๐ฎ๐ ๐ ๐ข๐ง๐ ๐ ๐๐๐ ๐๐ง๐ญ๐๐ ๐ซ๐๐ญ๐ข๐จ๐ง ๐จ๐ ๐ฆ๐ข๐ง๐๐๐ ๐๐จ๐๐๐ฅ๐ฌ from the paper ๐๐๐ซ๐ ๐๐๐๐ฌ ๐๐ฅ๐ฅ ๐๐ ๐๐๐๐๐๐?
๐ฅ I have integrated ๐ง๐๐ฑ๐ญ-๐ ๐๐ง๐๐ซ๐๐ญ๐ข๐จ๐ง ๐๐๐๐ฌ, 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 "๐ญ๐ซ๐๐ง๐ฌ๐๐จ๐ซ๐ฆ๐๐ซ๐ฌ" ๐ฅ๐ข๐๐ซ๐๐ซ๐ฒ for both usage and training.
๐ป I integrated two main tasks: ๐๐ข๐ง๐๐๐๐ ๐จ๐ซ๐๐๐ช๐ฎ๐๐ง๐๐๐๐ฅ๐๐ฌ๐ฌ๐ข๐๐ข๐๐๐ญ๐ข๐จ๐ง and ๐๐ข๐ง๐๐๐๐ ๐จ๐ซ๐๐๐ฎ๐ฌ๐๐ฅ๐๐.
๐๐ข๐ง๐๐๐๐ ๐จ๐ซ๐๐๐ช๐ฎ๐๐ง๐๐๐๐ฅ๐๐ฌ๐ฌ๐ข๐๐ข๐๐๐ญ๐ข๐จ๐ง: You can use this class for ๐๐๐ช๐ฎ๐๐ง๐๐ ๐๐ฅ๐๐ฌ๐ฌ๐ข๐๐ข๐๐๐ญ๐ข๐จ๐ง tasks. I also trained a Sentiment Analysis model with stanfordnlp/imdb dataset.
๐๐ข๐ง๐๐๐๐ ๐จ๐ซ๐๐๐ฎ๐ฌ๐๐ฅ๐๐: You can use this class for ๐๐๐ฎ๐ฌ๐๐ฅ ๐๐๐ง๐ ๐ฎ๐๐ ๐ ๐๐จ๐๐๐ฅ tasks such as GPT, Llama. I also trained an example model with roneneldan/TinyStories dataset. You can fine-tune and use it!
~75% on the challenging GPQA with only 40M parameters ๐ฅ๐ฅณ
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, itโs apparently possible to (intentionally or unintentionally) leak test data through this method.
