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  "text": "New model architectures arrive weekly, but software compilation stacks often lag behind. Bringing up new hardware accelerators traditionally takes months of specialist work.\n\nIn our latest technical blog, the IBM Spyre team demonstrates how AI coding agents can bridge this gap by writing runtime adapters. By patching unsupported operations and resolving memory alignment constraints, HF-adapters connected stock HuggingFace Transformers directly to PyTorch via torch-spyre. \n\nKey results include 13 AI-written adapters successfully enabling 7,960 of the top 10,000 HuggingFace embedding models, and 6,804 models passing complete end-to-end device tests on the IBM Spyre accelerator.\n\nRead the full technical deep dive here 👉 https://t.co/elZSgeU7oO\n\n@IBMResearch",
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