🎉 Day-0 support for @Baidu_Inc’s ERNIE 5.5 Flash Base with vLLM! 280B total parameters, 14B active per token. Pre-trained on 33.5T tokens, with 128K training context and up to 1M tokens for fine-tuning and inference. Open Base weights for domain adaptation, instruction tuning and RL research. Code highlights: 82.3% HumanEval+ and 40.1% LiveCodeBench pass@1 (both 1-shot), versus 69.5% and 31.9% for DeepSeek-V4-Flash-Base under the same evaluation setup. Efficiency is built into the architecture: 37 compressed-attention layers + 6 MLA-DSA layers, Multi-Router Latent MoE, and ECHO — reusing the final layer as a speculative draft. Serve FP8 or BF16 with Baidu’s vLLM-ERNIE plugin on vLLM 0.32.0. ECHO runs through vLLM’s MTP path; recipes include expert parallelism, KV offload and prefill/decode disaggregation, with runs verified on @NVIDIA GB200 and H100. Weights are on @huggingface and @ModelScope2022. Recipe: FP8: BF16: