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salma-remyx

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https://remyx.ai

smellslikeml

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reacted to badaoui's post with 🔥 about 13 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 🔥 1 day 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

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119

Agents are helping me scale the search for my next great idea.
Outrider is a GitHub Action for recommending methods and drafting PRs for your target repo.

VQASynth is an open-source multimodal dataset synthesis pipeline, reflecting the design detailed in SpatialVLM: https://spatial-vlm.github.io/

Outrider helped me discover a pipeline simplification using the recent work: InstructSAM
Check out their repo: https://github.com/DCDmllm/InstructSAM

Now I can focus on the remaining challenges, like porting the new architecture with .gguf conversion
https://github.com/smellslikeml/sam3cpp/blob/feat/instructsam-decoder/docker/Dockerfile

Post

3524

Nobody can be the originator of every great idea. And the rate of innovation in AI is difficult to track in detail.

But the next great idea for your codebase may have been described in open-science publications like arXiv.
Who among us can keep up with reading, smoke testing, adapting every viral method in the feed? (not me)

And what about the long-tail of results that you won't discover for another 6 months? That's what we built Outrider for!

Now I get alerts when candidate methods look like a good fit for the engineering challenges I'm addressing in my application.
I don't waste time running down dead end leads. Instead, I choose to invest my attention into ones which have earned it with evidence.

Try it yourself: https://studio.remyx.ai
Read the docs: https://docs.remyx.ai