After some heated discussion ๐ฅ, we clarify our intent re. storage limits on the Hub
TL;DR: - public storage is free, and (unless blatant abuse) unlimited. We do ask that you consider upgrading to PRO and/or Enterprise Hub if possible - private storage is paid above a significant free tier (1TB if you have a paid account, 100GB otherwise)
We optimize our infrastructure continuously to scale our storage for the coming years of growth in Machine learning, to the benefit of the community ๐ฅ
Six predictions for AI in 2025 (and a review of how my 2024 predictions turned out):
- There will be the first major public protest related to AI - A big company will see its market cap divided by two or more because of AI - At least 100,000 personal AI robots will be pre-ordered - China will start to lead the AI race (as a consequence of leading the open-source AI race). - There will be big breakthroughs in AI for biology and chemistry. - We will begin to see the economic and employment growth potential of AI, with 15M AI builders on Hugging Face.
How my predictions for 2024 turned out:
- A hyped AI company will go bankrupt or get acquired for a ridiculously low price โ (Inflexion, AdeptAI,...)
- Open-source LLMs will reach the level of the best closed-source LLMs โ with QwQ and dozens of others
- Big breakthroughs in AI for video, time-series, biology and chemistry โ for video ๐ดfor time-series, biology and chemistry
- We will talk much more about the cost (monetary and environmental) of AI โ Monetary ๐ดEnvironmental (๐ข)
- A popular media will be mostly AI-generated โ with NotebookLM by Google
- 10 millions AI builders on Hugging Face leading to no increase of unemployment ๐currently 7M of AI builders on Hugging Face
Let's go! We are releasing SmolVLM, a smol 2B VLM built for on-device inference that outperforms all models at similar GPU RAM usage and tokens throughputs.
- SmolVLM generates tokens 7.5 to 16 times faster than Qwen2-VL! ๐คฏ - Other models at this size crash a laptop, but SmolVLM comfortably generates 17 tokens/sec on a macbook! ๐ - SmolVLM can be fine-tuned on a Google collab! Or process millions of documents with a consumer GPU! - SmolVLM even outperforms larger models in video benchmarks, despite not even being trained on videos!
This is no Woodstock AI but will be fun nonetheless haha. Iโll be hosting a live workshop with team members next week about the Enterprise Hugging Face hub.
1,000 spots available first-come first serve with some surprises during the stream!
Maybe like me you have always wanted a super easy way to compare llama3.2-1B vs. llama3.2-3B? or the same model with different temperatures?
Trying and comparing warm Inference API models has never been easier! Just go to https://hf.co/playground, set your token and you're ready to go. We'll keep improving, feedback welcome ๐
The Chinese LLM maker just dropped a flurry of different models, ensuring there will be a Qwen SOTA model for every application out there: Qwen2.5: 0.5B, 1.5B, 3B, 7B, 14B, 32B, and 72B Qwen2.5-Coder: 1.5B, 7B, and 32B on the way Qwen2.5-Math: 1.5B, 7B, and 72B.
And they didn't sleep: the performance is top of the game for each weight category!
๐๐๐ฒ ๐ข๐ง๐ฌ๐ข๐ ๐ก๐ญ๐ฌ:
๐ All models have ๐ญ๐ฎ๐ด๐ธ ๐๐ผ๐ธ๐ฒ๐ป ๐ฐ๐ผ๐ป๐๐ฒ๐ ๐ ๐น๐ฒ๐ป๐ด๐๐ต
๐ Models pre-trained on 18T tokens, even longer than the 15T of Llama-3
๐ซ๐ท On top of this, it ๐๐ฎ๐ธ๐ฒ๐ ๐๐ต๐ฒ #๐ญ ๐๐ฝ๐ผ๐ ๐ผ๐ป ๐บ๐๐น๐๐ถ๐น๐ถ๐ป๐ด๐๐ฎ๐น ๐๐ฎ๐๐ธ๐ so it might become my standard for French
๐ป Qwen2.5-Coder is only 7B but beats competing models up to 33B (DeeSeek-Coder 33B-Instruct). Let's wait for their 32B to come out!
๐งฎ Qwen2.5-Math sets a new high in the ratio of MATH benchmark score to # of parameters. They trained it by "aggregating more high-quality mathematical data, particularly in Chinese, from web sources, books, and codes across multiple recall cycles."
๐ Technical report to be released "very soon"
๐ All models have the most permissive license apache2.0, except the 72B models that have a custom license mentioning "you can use it for free EXCEPT if your product has over 100M users"