ShahzebKhoso/LocateAnything-3B-GGUF
1250
LocateAnything-3B - GGUF Quantized Versions
This repository provides GGUF quantized versions of nvidia/LocateAnything-3B, converted with llama.cpp.
The purpose of this repository is to provide fast, easy-to-use local inference files for llama.cpp, Ollama, LM Studio, Jan, Open WebUI, and llama-cpp-python users.
Model Details
- Base model:
nvidia/LocateAnything-3B - Architecture: Transformer
- Format: GGUF
- Source license:
other - Conversion tool:
convert_hf_to_gguf.pyfrom llama.cpp - Quantization tool:
llama-quantize - Recommended file:
LocateAnything-3B-Q4_K_M.gguf
Quantized Files
Validation
Each file was tested with llama-cli for basic load + generation.
Usage
llama.cpp
llama-cli -m LocateAnything-3B-Q4_K_M.gguf -p "Hello! Introduce yourself briefly."Older builds may use:
./main -m LocateAnything-3B-Q4_K_M.gguf -p "Hello! Introduce yourself briefly."llama.cpp directly from Hugging Face
llama-cli -hf ShahzebKhoso/LocateAnything-3B-GGUF:Q4_K_M -p "Hello! Introduce yourself briefly."llama-cpp-python
from huggingface_hub import hf_hub_download
from llama_cpp import Llama
model_path = hf_hub_download(
repo_id="ShahzebKhoso/LocateAnything-3B-GGUF",
filename="LocateAnything-3B-Q4_K_M.gguf",
)
llm = Llama(model_path=model_path)
out = llm.create_chat_completion(
messages=[
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Hello! Introduce yourself briefly."},
],
max_tokens=128,
)
print(out["choices"][0]["message"]["content"])Which file should I use?
- Use
Q4_K_Mfor the best default balance. - Use
Q5_K_Mfor better quality. - Use
Q8_0if you want near-original quality and have more memory. - Use
Q2_KorQ3_K_Monly when memory is very limited.
Provenance
This repository is a quantized derivative of:
nvidia/LocateAnything-3BBase model metadata:
revision: 7a81d810571dc5f244b2f0b6868128f24b1cbd85
pipeline_tag: image-text-to-text
tags: transformers, safetensors, locateanything, feature-extraction, nvidia, eagle, vision, object-detection, grounding, arxiv:2605.27365, image-text-to-text, conversational, custom_code, en, arxiv:2504.07491, arxiv:2109.10852, arxiv:2510.12798, arxiv:2303.05499, arxiv:1405.0312, arxiv:1908.03195