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reacted to kalyan-ks's post with 🔥 about 3 hours ago
Tiny Specialized Models beat LLMs at PII Entity Extraction Ettin-68M-Nemotron-PII model - achieves strong F1-score of 96.27% with just 68M parameters. - beats popular LLMs like GPT-4o-mini (78.69%) and DeepSeek-v4-flash (84.89%). More details in the blog post - https://huggingface.co/blog/kalyan-ks/tiny-pii-entity-detection-models
reacted to rajkumarrawal's post with 🚀 about 3 hours ago
LLMs aren’t just answering questions anymore, they’re learning to evolve. Self evolving AI is the true endgame. AI has shifted from short tasks to long missions. The breakthrough isn’t just automation, it’s machines learning human methods and applying them at machine speed. From cybersecurity to finance, from OPCs to NPCs, the wave is irreversible. Read the full article: Self Evolving is the Endgame or final destiny https://huggingface.co/blog/rajkumarrawal/self-evolving-is-the-endgame-or-final-destiny What’s your definition of true AGI? Comment below.
reacted to Imosu's post with 🔥 about 3 hours ago
# ZeroGPU Hardware Mismatch: Why Am I Getting RTX PRO 6000 Blackwell MIG Instead of the Documented H200? I recently ran into a surprising issue while debugging a Hugging Face ZeroGPU Space. According to the Hugging Face ZeroGPU documentation, ZeroGPU is described as using NVIDIA H200-based resources, with configurations such as “large” and “xlarge” offering H200-class memory. However, when I printed the actual GPU information inside my Space, I got something different: ```txt GPU: NVIDIA RTX PRO 6000 Blackwell Server Edition MIG 2g.48gb Capability: (12, 0) Torch: 2.8.0+cu128 CUDA: 12.8 This is not an H200. It appears to be a MIG slice of an RTX PRO 6000 Blackwell Server Edition GPU, with 48GB VRAM. This difference matters. It is not just a cosmetic hardware-name issue. In my case, the Space was running Qwen3-TTS and failed with: CUDA error: no kernel image is available for execution on the device The issue appears related to GPU architecture compatibility. The app was using kernels-community/flash-attn3, which is generally aligned with Hopper-class GPUs such as H100/H200, but the actual device exposed to the Space was Blackwell with compute capability 12.0. As a result, CUDA kernels that might work on the expected H200 environment failed on the actual assigned GPU. To be clear, I am not saying the RTX PRO 6000 Blackwell is a bad GPU. It is a newer architecture and may be powerful in many workloads. But it is not the same as H200, and the software ecosystem compatibility is different. For ML workloads, especially those relying on custom CUDA kernels, the exact GPU architecture matters a lot. This raises a few questions: Is Hugging Face ZeroGPU now assigning RTX PRO 6000 Blackwell MIG instances instead of H200 instances? If yes, why is this not clearly documented?
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