Model Detail
MiMo-V2.5-Pro-FP4-DFlash
▲ 1.6%MiMo-V2.5-Pro-FP4-DFlash is a code generation model with 277.2B parameters released by XiaomiMiMo. The model is registered under the text-generation pipeline tag on Hugging Face, distributed under the permissive mit license.
MiMo-V2.5-Pro-FP4-DFlash ships with 277.2B parameters. Total weight footprint is approximately 554.3 GB, which is the relevant figure when planning local-inference VRAM. The mit license is permissive, allowing commercial deployment and derivative work without per-seat fees, though attribution requirements still apply.
Downloads of MiMo-V2.5-Pro-FP4-DFlash have moved +1.6% over the past 24 hours, +90.6% over the trailing seven days. That puts the model in active uptrend territory; a sustained move of this size usually reflects a recent release, a viral integration, or a benchmark surprise rather than steady-state demand. These numbers are signal, not guarantee — week-over-week download counts on Hugging Face also reflect mirror traffic, CI scrapes, and one-off benchmarking runs.
MiMo-V2.5-Pro-FP4-DFlash is best fit for code completion, repository-scale Q&A, and pair-programming integrations. It is a less obvious choice for one-shot generation of security-critical code without review. Treat this as a starting matrix rather than a benchmark verdict — the right deployment usually depends on the specific evaluation suite that mirrors your workload.
Beamforming Design Via GNN in mmWave Cell-Free Massive MIMO Using Sub-6 GHz CSI
arXiv:2608.30524v1 Announce Type: cross Abstract: Beamforming methods in millimeter-wave (mmWave) cell-free massive multiple-input multiple-output (CFmMIMO) systems require accurate channel state information (CSI), whose acquisition entails significant training overhead. This paper shows that fully
MIMO: Multilingual Information Retrieval via Monolingual Objectives
arXiv:2605.31171v2 Announce Type: replace-cross Abstract: Multilingual Information Retrieval (MLIR) reflects real-world search environments in which queries and relevant documents may appear in different languages within a mixed-language corpus. However, existing embedding models are primarily optim
Restoration Flow Matching-Based Channel Refinement and Equalization Correction for MIMO Semantic Communications
arXiv:2607.23615v1 Announce Type: new Abstract: In multiple-input multiple-output (MIMO) semantic communication, imperfect channel state information (CSI) and equalization mismatch can seriously degrade semantic reconstruction quality. To address this issue, we propose a unified restoration flow mat
Multi-layer MIMO Relay as Deep Physical Neural Networks: Power Amplifiers as Activation Functions
arXiv:2607.18354v1 Announce Type: cross Abstract: Wireless physical neural networks (WPNNs) embed neural computation directly into analog hardware, offering lower energy consumption and latency than conventional digital implementations. In this paper, we propose a deep WPNN in which nonlinear activa
DeeperRadar: End-to-End MIMO Radar Design and Multi-Modal Fusion for Autonomous Vehicle Perception
arXiv:2607.17351v1 Announce Type: new Abstract: DeeperRadar is a radar-centric, sensor-stack-conditioned framework that co-designs radar sensing and multi-modal 3D detection for autonomous mobility by learning a sparse acquisition pattern end-to-end with the fusion model. A learnable MIMO design mod
Full-Pipeline Inference Optimization for MiMo-V2.5 Series: Pushing Hybrid SWA Efficiency to the Limit
arXiv:2607.13095v1 Announce Type: cross Abstract: We present a full-pipeline inference optimization for the MiMo-V2.5 model family, which combines Hybrid Sliding Window Attention (Hybrid SWA), sparse Mixture-of-Experts (MoE), and multimodal encoders. While Hybrid SWA can ideally reduce both attentio
