SpaceQwen2.5-VL-3B-Instruct Open-source Vision-Language Model - Supports Spatial Reasoning and Multimodal Tasks

Spaceqwen2.5 VL 3B Instruct I1 GGUF

Developed by mradermacher

SpaceQwen2.5-VL-3B-Instruct is a 3B-parameter vision-language model focused on spatial reasoning and multimodal tasks.

Text-to-Image EnglishOpen Source License:Apache-2.0 #Spatial Distance Estimation #Multimodal Vision-Language #Robot Navigation

Downloads 459

Release Time : 4/11/2025

Model Overview

This is a vision-language model based on the Qwen architecture, specifically optimized for spatial reasoning capabilities, suitable for applications in robotics, distance estimation, and embodied artificial intelligence.

Model Features

Spatial Reasoning Capability

Specifically optimized for quantitative spatial reasoning, suitable for distance estimation and spatial relationship understanding

Multimodal Understanding

Capable of processing both visual and language inputs to achieve cross-modal understanding

Efficient Quantization

Provides multiple quantized versions to meet deployment requirements under different hardware conditions

Model Capabilities

Visual question answering

Spatial relationship understanding

Distance estimation

Multimodal reasoning

Robot navigation assistance

Use Cases

Robotics

Robot Navigation

Assists robots in understanding environmental spatial relationships for path planning and obstacle avoidance

Embodied AI

Virtual Agent Environment Interaction

Enables virtual agents to understand and respond to objects and relationships in spatial environments

🚀 SpaceQwen2.5-VL-3B-Instruct Quantized Model

This project provides quantized versions of the SpaceQwen2.5-VL-3B-Instruct model, offering various options for different usage scenarios.

📚 Documentation

Model Information

Property	Details
Base Model	remyxai/SpaceQwen2.5-VL-3B-Instruct
Datasets	remyxai/OpenSpaces
Language	en
Library Name	transformers
License	apache-2.0
Quantized By	mradermacher
Tags	remyx, vqasynth, spatial-reasoning, multimodal, vlm, vision-language, robotics, distance-estimation, embodied-ai, quantitative-spatial-reasoning

About

Weighted/imatrix quants of https://huggingface.co/remyxai/SpaceQwen2.5-VL-3B-Instruct. Static quants are available at https://huggingface.co/mradermacher/SpaceQwen2.5-VL-3B-Instruct-GGUF.

Usage

If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files.

Provided Quants

(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)

Link	Type	Size/GB	Notes
GGUF	i1-IQ1_S	0.9	for the desperate
GGUF	i1-IQ1_M	1.0	mostly desperate
GGUF	i1-IQ2_XXS	1.0
GGUF	i1-IQ2_XS	1.1
GGUF	i1-IQ2_S	1.2
GGUF	i1-IQ2_M	1.2
GGUF	i1-Q2_K_S	1.3	very low quality
GGUF	i1-Q2_K	1.4	IQ3_XXS probably better
GGUF	i1-IQ3_XXS	1.4	lower quality
GGUF	i1-IQ3_XS	1.5
GGUF	i1-Q3_K_S	1.6	IQ3_XS probably better
GGUF	i1-IQ3_S	1.6	beats Q3_K*
GGUF	i1-IQ3_M	1.6
GGUF	i1-Q3_K_M	1.7	IQ3_S probably better
GGUF	i1-Q3_K_L	1.8	IQ3_M probably better
GGUF	i1-IQ4_XS	1.8
GGUF	i1-IQ4_NL	1.9	prefer IQ4_XS
GGUF	i1-Q4_0	1.9	fast, low quality
GGUF	i1-Q4_K_S	1.9	optimal size/speed/quality
GGUF	i1-Q4_K_M	2.0	fast, recommended
GGUF	i1-Q4_1	2.1
GGUF	i1-Q5_K_S	2.3
GGUF	i1-Q5_K_M	2.3
GGUF	i1-Q6_K	2.6	practically like static Q6_K

Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):

And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9

FAQ / Model Request

See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized.

Thanks

I thank my company, nethype GmbH, for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to @nicoboss for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.

📄 License

This project is licensed under the apache-2.0 license.

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