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LocateAnything-3B-imatrix-GGUF
liodon-ai/LocateAnything-3B-imatrix-GGUF
LocateAnything-3B-imatrix-GGUF is a text generation model from liodon-ai. Use it when you need the model to write or continue text. It is set up for gguf. The card lists the license as other.
iMatrix GGUF quantizations of nvidia/LocateAnything-3B — the first GGUF available for this model.
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18.9 GB
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.gguf18.9 GB · 100%
At a glance
- Task
- Text Generation
- Library
- gguf
- License
- other
- Access
- Public
- Created
- Jun 22, 2026
- Updated
- Jul 13, 2026
- SHA
- a34961dc
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Base models
Model card
From the Hugging Face model README
LocateAnything-3B — iMatrix GGUF
iMatrix GGUF quantizations of nvidia/LocateAnything-3B — the first GGUF available for this model.
LocateAnything-3B is NVIDIA's 3B visual grounding model — it locates and identifies objects in images given natural language descriptions. Designed for on-device deployment and robotics applications.
These GGUFs use importance matrix (iMatrix) calibration on 2M tokens of wikitext-103: iMatrix runs calibration text through the model, measures which weights activate most, and protects them during quantization. Result: noticeably better coherence at Q2/Q3/Q4 — same file size, better output.
Quick Start
llama.cpp
llama-cli -hf liodon-ai/LocateAnything-3B-imatrix-GGUF:Q4_K_M
LM Studio / Jan
Search liodon-ai/LocateAnything-3B-imatrix-GGUF and pick your quant.
Available Quants
| Quant | Size | VRAM | Notes |
|---|---|---|---|
IQ2_M | 1.28 GB | 2 GB | ultra-tiny + iMatrix — better than standard Q2 |
IQ3_M | 1.62 GB | 2.5 GB | tiny + iMatrix — sharper than standard Q3 |
IQ4_XS | 1.91 GB | 3 GB | small + iMatrix — rivals Q5 at Q4 size |
Q2_K | 1.38 GB | 2 GB | tiniest standard — runs almost anywhere, iMatrix-improved |
Q3_K_M | 1.73 GB | 2.5 GB | great for 4GB VRAM, iMatrix-improved |
Q4_K_M | 2.11 GB | 3 GB | sweet spot (recommended), iMatrix-improved |
Q5_K_M | 2.44 GB | 4 GB | high quality, iMatrix-improved |
Q6_K | 2.80 GB | 4 GB | near-lossless, iMatrix-improved |
Q8_0 | 3.62 GB | 5 GB | basically full quality |
What is iMatrix?
Standard quantization rounds all weights equally. iMatrix:
- Runs calibration text through the full-precision model
- Measures which weights activate most (the "importance matrix")
- Allocates more precision to important weights, less to unimportant ones
Same file size. Better output. Most noticeable at Q2/Q3/Q4.
Calibration
Importance matrix computed from 2M tokens of wikitext-103 — 128 calibration chunks.
Source Model
- Original: nvidia/LocateAnything-3B
- Architecture: 3B visual grounding model
- Strengths: Object localization, visual grounding, on-device robotics
- License: NVIDIA Open Model License
- Task
- Text Generation
- Library
- gguf
- License
- other
- Created
- Jun 22, 2026
- Updated
- Jul 13, 2026