GitHub Repo Technical Report 👋 Join us on Discord and WeChat Introduction BitCPM CANN is the first end to end 1.58 bit (ternary) large language model training system natively built on Huawei Ascend NPU. The system integrates quantization aware training (QAT) into the Megatron LM framework with MindSpeed acceleration, covering the full training stack from custom ternary operators to distributed parallel training on Ascend 910B. We train a family of four models—BitCPM CANN 0.5B/1B/3B/8B—and evaluate them against their full precision MiniCPM4 counterparts across 11 benchmarks. The 1B/3B/8B models retain 95.7%–97.2% of full precision performance, while enabling approximately 6× memory reduction at inference time. QAT introduces only 5% training throughput overhead (148 vs. 155 TFLOP/s per NPU). Key Features 🔬 1.58 Bit Ternary Quantization : Compresses model weights to ternary values { 1, 0, 1}, achieving ~90% bit width reduction compared to BF16. 🖥️ Native Ascend NPU Training : First publicly reported 1.58 bit training effort on domestic NPU platform at 8B scale, establishing reusable low bit training infrastructure for the Ascend ecosystem. ⚡ Minimal Training Overhead : Only 5% thr…
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