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A compact model can achieve nearly 70% accuracy on complex reasoning tasks, closing the performance gap with larger models while running on consumer-grade hardware.
Transformer deployment just got a whole lot faster: Forge-UGC slashes compilation time by up to 9x and inference latency by 35% compared to OpenVINO and ONNX Runtime.
Forget painstakingly tuning quantization for each LLM – RAMP learns a quantization policy that generalizes across architectures, often outperforming target-specific training.