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SigLIP 2: A better multilingual vision language encoder
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Shyam Sunder Kumar
theainerd
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Natural Language Processing
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SigLIP 2: A better multilingual vision language encoder
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Now the Hugging Face agent course is getting real! With frameworks like smolagents, LlamaIndex, and LangChain.
🔗 Follow the org for updates https://huggingface.co./agents-course
This week we are releasing the first framework unit in the course and it’s on smolagents. This is what the unit covers:
- why should you use smolagents vs another library?
- how to build agents that use code
- build multiagents systems
- use vision language models for browser use
The team has been working flat out on this for a few weeks. Led by @sergiopaniego and supported by smolagents author @m-ric.
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Wan2.1 🔥📹 new OPEN video model by Alibaba Wan team!
Model: Wan-AI/Wan2.1-T2V-14B
Demo: Wan-AI/Wan2.1
✨Apache 2.0
✨8.19GB VRAM, runs on most GPUs
✨Multi-Tasking: T2V, I2V, Video Editing, T2I, V2A
✨Text Generation: Supports Chinese & English
✨Powerful Video VAE: Encode/decode 1080P w/ temporal precision
Model: Wan-AI/Wan2.1-T2V-14B
Demo: Wan-AI/Wan2.1
✨Apache 2.0
✨8.19GB VRAM, runs on most GPUs
✨Multi-Tasking: T2V, I2V, Video Editing, T2I, V2A
✨Text Generation: Supports Chinese & English
✨Powerful Video VAE: Encode/decode 1080P w/ temporal precision
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Now the Hugging Face agent course is getting real! With frameworks like smolagents, LlamaIndex, and LangChain.
🔗 Follow the org for updates https://huggingface.co./agents-course
This week we are releasing the first framework unit in the course and it’s on smolagents. This is what the unit covers:
- why should you use smolagents vs another library?
- how to build agents that use code
- build multiagents systems
- use vision language models for browser use
The team has been working flat out on this for a few weeks. Led by @sergiopaniego and supported by smolagents author @m-ric .
🔗 Follow the org for updates https://huggingface.co./agents-course
This week we are releasing the first framework unit in the course and it’s on smolagents. This is what the unit covers:
- why should you use smolagents vs another library?
- how to build agents that use code
- build multiagents systems
- use vision language models for browser use
The team has been working flat out on this for a few weeks. Led by @sergiopaniego and supported by smolagents author @m-ric .
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She arrived 😍
[Expect more models soon...]
[Expect more models soon...]
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Me and my team have performed an in-depth investigation comparing o1 to R1 (and other reasoning models)
Link: https://toloka.ai/blog/r1-is-not-on-par-with-o1-and-the-difference-is-qualitative-not-quantitative
It started with us evaluating them on our own university-math benchmarks: U-MATH for problem-solving and μ-MATH for judging solution correctness (see the HF leaderboard: toloka/u-math-leaderboard)
tl;dr: R1 sure is amazing, but what we find is that it lags behind in novelty adaptation and reliability:
* performance drops when updating benchmarks with fresh unseen tasks (e.g. AIME 2024 -> 2025)
* R1-o1 gap widens when evaluating niche subdomains (e.g. university-specific math instead of the more common Olympiad-style contests)
* same with going into altogether unconventional domains (e.g. chess) or skills (e.g. judgment instead of problem-solving)
* R1 also runs into failure modes way more often (e.g. making illegal chess moves or falling into endless generation loops)
Our point here is not to bash on DeepSeek — they've done exceptional work, R1 is a game-changer, and we have no intention to downplay that. R1's release is a perfect opportunity to study where all these models differ and gain understanding on how to move forward from here
Link: https://toloka.ai/blog/r1-is-not-on-par-with-o1-and-the-difference-is-qualitative-not-quantitative
It started with us evaluating them on our own university-math benchmarks: U-MATH for problem-solving and μ-MATH for judging solution correctness (see the HF leaderboard: toloka/u-math-leaderboard)
tl;dr: R1 sure is amazing, but what we find is that it lags behind in novelty adaptation and reliability:
* performance drops when updating benchmarks with fresh unseen tasks (e.g. AIME 2024 -> 2025)
* R1-o1 gap widens when evaluating niche subdomains (e.g. university-specific math instead of the more common Olympiad-style contests)
* same with going into altogether unconventional domains (e.g. chess) or skills (e.g. judgment instead of problem-solving)
* R1 also runs into failure modes way more often (e.g. making illegal chess moves or falling into endless generation loops)
Our point here is not to bash on DeepSeek — they've done exceptional work, R1 is a game-changer, and we have no intention to downplay that. R1's release is a perfect opportunity to study where all these models differ and gain understanding on how to move forward from here
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🚀 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.
🧭 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:
💡 More precise outputs: Reduced redundancy in model outputs through refined post-training data and optimization techniques.
🌈 Handling longer texts: Expanded to handle up to 128K context length in Southeast Asian languages through long-text training.
⚡️ Faster inference: Achieved 2.5x faster inference speed with speculative decoding.
🌪️ More model sizes: Introduced new sizes of 3B and 14B through model pruning.
🌟 All models are Apache-licensed for commercial use; development tools (code, resources) are open-source.
📚 Technical report: Sailor2: Sailing in South-East Asia with Inclusive Multilingual LLMs (2502.12982)
🤖️ Models: sail/sailor2-language-models-674d7c9e6b4dbbd9a869906b
💬 Demo: sail/Sailor2-20B-Chat
📣 Sailor2 community: https://huggingface.co./sailor2
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.
🧭 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:
💡 More precise outputs: Reduced redundancy in model outputs through refined post-training data and optimization techniques.
🌈 Handling longer texts: Expanded to handle up to 128K context length in Southeast Asian languages through long-text training.
⚡️ Faster inference: Achieved 2.5x faster inference speed with speculative decoding.
🌪️ More model sizes: Introduced new sizes of 3B and 14B through model pruning.
🌟 All models are Apache-licensed for commercial use; development tools (code, resources) are open-source.
📚 Technical report: Sailor2: Sailing in South-East Asia with Inclusive Multilingual LLMs (2502.12982)
🤖️ Models: sail/sailor2-language-models-674d7c9e6b4dbbd9a869906b
💬 Demo: sail/Sailor2-20B-Chat
📣 Sailor2 community: https://huggingface.co./sailor2
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