Hugging Face's logo
Salma Mayorquin's picture

Salma Mayorquin PRO

salma-remyx
27 22 483
Iki007's profile picture deadbydawn101's profile picture tobytoby's profile picture
·
https://remyx.ai

AI & ML interests

None yet

Recent Activity

reacted to badaoui's post with 🔥 about 21 hours ago
432 GB of ultra-fast HBM4 and up to 23.3 TB/s of memory bandwidth on a single GPU 🤯. Two weeks ago, we got early access to AMD's new Instinct MI455X, and our first goal was simple: make sure 🤗 Transformers works on day one. Over the past few weeks, we worked closely with the AMD team to validate the platform, enable Flash Attention, add torchcodec support for multimodal models, and resolve issues uncovered during testing. The result: ✅ 99.5% success rate across our 24 core Transformers model architectures - already on par with our daily CI on previous AMD and NVIDIA platforms. The hardware is just as exciting. With 432 GB of HBM per GPU, our early capacity experiments showed more than 3× the concurrent long-context requests compared to MI300, thanks to the much larger KV cache capacity. A huge thanks to the AMD team for the early access and the great collaboration! Read the full blog 👇 https://huggingface.co/blog/badaoui/transformers-on-amd-mi455
liked a model 1 day ago
CircleRadon/InstructSAM-2B
reacted to Jiaqi-hkust's post with 🔥 2 days ago
IQA-T1: Evidence‑Based Image Quality Assessment with MLLMs Most MLLMs are blind to low‑level degradations—noise, blur, compression artifacts look the same as clean images in their internal representations. That leads to quality scores based on semantic “gut feeling” rather than real perceptual evidence. IQA-T1 changes that. We equip the model with a toolbox of 15 perceptual tools (noise residual maps, Fourier spectra, gradient maps, etc.) that generate structured visual evidence on demand. The model learns how to use tools via supervised fine‑tuning on our Q‑Tool dataset (11k evidence‑grounded reasoning chains), and when to call them via GRPO reinforcement learning that balances accuracy, tool count, and redundancy. The result: SOTA performance across 7 benchmarks (avg PLCC 0.795), using only 2.34 tools per image on average. Every predicted score is now interpretable and backed by hard visual evidence. All code, weights, dataset, and demo are open. Check them out and give it a spin! 📄 arxiv.org/abs/2607.12375v1 💻 github.com/zibuyu-02/IQA-T1 🤗 model/data: huggingface.co/zibuyu-02/IQA-T1 🎮 demo: huggingface.co/spaces/Jiaqi-hkust/IQA-T1
View all activity

Organizations

Remyx AI's profile picture