Model Detail
omni-embed-nemotron-3b
—omni-embed-nemotron-3b is a code generation model with 3B parameters released by NVIDIA. The model is registered under the sentence-similarity pipeline tag on Hugging Face, distributed under a other license.
omni-embed-nemotron-3b ships with 3B parameters. Total weight footprint is approximately 4.7 GB, which is the relevant figure when planning local-inference VRAM. Distribution is governed by the other license — review the exact terms before commercial deployment.
omni-embed-nemotron-3b 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.
Omni-Woodpecker: Hallucination Correction via Evidence-Prior Rebalancing for Omni LLMs
arXiv:2610.27103v1 Announce Type: new Abstract: Recent Omni LLMs (OLLMs) have demonstrated remarkable capabilities in joint audio-visual reasoning, yet cross-modal hallucinations remain a major challenge. Existing mitigation methods mainly operate at the output level, overlooking the entanglement of
PW-Pruning: Phase-Aware Expert Selection for Speech-Centric Omni-Modal Models
arXiv:2610.26891v1 Announce Type: cross Abstract: Sparse routing reduces per-token computation but leaves the full expert bank resident in memory. We study expert pruning for speech-centric omni-modal models, where pooling calibration statistics can obscure the different expert demands of acoustic-i
OmniTrace: A Unified Framework for Generation-Time Source Attribution in Omni-Modal LLMs
arXiv:2604.13073v2 Announce Type: replace-cross Abstract: An omni-modal language model can answer a question about a multimodal document, a recording, or a video, but it cannot show which part of the input the answer came from. Asking the model to cite its evidence yields what it says it used; measu
OmniCam: Omni-Camera Trajectory Generation via Geometry-Grounded Pose Token Learning
arXiv:2610.09513v2 Announce Type: replace-cross Abstract: Camera trajectories control viewpoint changes in video generation, scene reconstruction, and robotic perception. Generating them from language requires both scene geometry and target-aware framing. We introduce OmniCam, an autoregressive mode
Omni-Modal LLMs Know More Than They Say
arXiv:2610.16135v1 Announce Type: cross Abstract: Omni-modal large language models (Omni-LLMs) extend LLMs to process heterogeneous modalities (e.g., vision, audio, and IMU signals) alongside language, enabling a broader range of multimodal tasks. Yet we find that these models often fail to express
Omni-Embed-Audio: Leveraging Multimodal LLMs for Robust Audio-Text Retrieval
arXiv:2604.18360v4 Announce Type: replace-cross Abstract: Audio-text retrieval systems based on Contrastive Language-Audio Pretraining (CLAP) achieve strong performance on traditional benchmarks; however, these benchmarks rely on caption-style queries that differ substantially from real-world search
