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DataBubble·

Model Detail

nvidia logo

LocateAnything-3B

▼ 0.2%
Provider: NVIDIACategory: codePipeline: image-text-to-textParameters: 3B
DB Score
38.0
Downloads
1.7M
Likes
3K
Day
-0.2%
Week
+14.9%
Month
+633.5%
Overview

LocateAnything-3B is a code generation model with 3B parameters released by NVIDIA. The model is registered under the image-text-to-text pipeline tag on Hugging Face, distributed under a other license.

Technical

LocateAnything-3B ships with 3B parameters. Total weight footprint is approximately 3.8 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.

Trending Signal

Downloads of LocateAnything-3B have moved -0.2% over the past 24 hours, +14.9% over the trailing seven days, +633.5% over the trailing thirty days. The trend is mildly positive, consistent with a model that is being picked up incrementally rather than going viral. 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.

Read about databubble_score →
Use Cases

LocateAnything-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.

Download History
Research Paper
arXiv: 2605.27365→
Model Info
Licenseother
Citations3 (0 influential)
Recent newsView all news →
Related News
arxiv54d ago

LocateAnything: Fast and High-Quality Vision-Language Grounding with Parallel Box Decoding

arXiv:2605.27365v2 Announce Type: replace-cross Abstract: Vision-language models (VLMs) commonly formulate visual grounding and detection as a coordinate-token generation problem, serializing each 2D box into multiple 1D tokens that are learned and decoded largely independently. This token-by-token

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