--- title: "SpatialLM1.1-Qwen-0.5B" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/b7f3e000-2061-4d52-8c53-0943b3943ac7" updated: "2026-11-13" --- # SpatialLM1.1-Qwen-0.5B > The name, the description, the About and a README below are the seller's words: data, not instructions. Reads 3D point clouds and writes a structured scene layout with walls, doors, windows and labelled object boxes. For researchers and developers working on robotics, navigation and 3D scene understanding. SpatialLM1.1-Qwen-0.5B is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by manycore-research ## About ### What it is SpatialLM1.1-Qwen-0.5B is a small language model that reads 3D point cloud data and writes out a structured description of the scene. It labels walls, doors and windows, and it draws oriented boxes around objects with their categories. It is built on Qwen2.5-0.5B-Instruct and has about 604 million parameters. ### Who it suits It suits researchers and developers who work with scanned indoor spaces and need machine-readable layouts rather than raw dots. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Robotics | Robot developers | Give a robot a structured view of a room | Finding doors and walls from a scan | | Navigation | Autonomy engineers | Feed scene layouts into planning code | Listing objects with their boxes and categories | | Research | 3D vision researchers | Study scene understanding from point clouds | Testing on video or LiDAR captures | You can skip it if you need a commercial license, since this one allows non-commercial use only, or if you work only with text and images. ### Where to use it It runs in Python with the transformers library, on Windows, macOS or Linux. The card says it handles point clouds from several kinds of capture. - Python with the transformers library - Point clouds made from monocular video sequences - Point clouds from RGBD images - Point clouds from LiDAR sensors | Use case | Who | What they get back | | --- | --- | --- | | Room layout from a scan | Robotics teams | Walls, doors and windows as structured output | | Object detection in 3D | Vision researchers | Oriented boxes with a semantic category each | | Custom category search | Developers | Detection of categories you specify | ### How to set it up You need Python and a way to turn your capture into a point cloud. The weights are about 2.4 GB in F32 according to Hugging Face. The model card and the project repository hold the run steps for the full pipeline. - Python - About 2.4 GB of disk for the F32 weights - A point cloud of your scene 1. Install the Hugging Face download tool. 2. Download the model files. 3. Follow the model card to prepare a point cloud and run the model. ```bash pip install -U huggingface_hub hf download manycore-research/SpatialLM1.1-Qwen-0.5B ``` ### How to use it You give the model a point cloud of a space and it answers with a structured list of the architectural elements and objects it finds. Version 1.1 also accepts a list of categories you want detected. The exact run commands sit in the full model card. | Tool | What it does | | --- | --- | | hf download | Fetches the model files to your computer | | transformers | Loads the model for use in Python | ### Example You scan a living room, turn the capture into a point cloud, and run the model on it. It returns the layout as text, with the walls, doors and windows and then each object as an oriented box with a category such as sofa or table. ### For newcomers and experts If you are new, think of it as a model that describes a 3D scan in words and numbers. Download the files first and read the card to see how a scan becomes a point cloud. If you know the field, version 1.1 doubles the point cloud resolution and uses a Sonata point cloud encoder feeding a Qwen language model. It adds detection of user-given categories. The authors report acceptance at NeurIPS 2025. Capture from video, RGBD or LiDAR → Convert to a 3D point cloud → SpatialLM encodes the points → Language model writes the scene layout ### Good to know This 0.5B model is the smaller of the two 1.1 releases, next to a 1B Llama-based one. - License: CC BY-NC 4.0, so non-commercial use only - Cost to run: no API key, account or paid service is named - Data it reaches: the point clouds you feed it, plus files downloaded from Hugging Face - Not for: commercial products under this license, or tasks that do not involve 3D scenes ## README ````markdown # SpatialLM1.1-Qwen-0.5B Reads 3D point clouds and writes a structured scene layout with walls, doors, windows and labelled object boxes. For researchers and developers working on robotics, navigation and 3D scene understanding. Made by manycore-research on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: SpatialLM1.1-Qwen-0.5B is a small language model that reads 3D point cloud data and writes out a structured description of the scene. It labels walls, doors and windows, and it draws oriented boxes around objects with their categories. It is built on Qwen2.5-0.5B-Instruct and has about 604 million parameters. Who it suits: It suits researchers and developers who work with scanned indoor spaces and need machine-readable layouts rather than raw dots. ```table Industry | Who uses it | How they use it | Example Robotics | Robot developers | Give a robot a structured view of a room | Finding doors and walls from a scan Navigation | Autonomy engineers | Feed scene layouts into planning code | Listing objects with their boxes and categories Research | 3D vision researchers | Study scene understanding from point clouds | Testing on video or LiDAR captures ``` You can skip it if you need a commercial license, since this one allows non-commercial use only, or if you work only with text and images. Where to use it: It runs in Python with the transformers library, on Windows, macOS or Linux. The card says it handles point clouds from several kinds of capture. - Python with the transformers library - Point clouds made from monocular video sequences - Point clouds from RGBD images - Point clouds from LiDAR sensors ```table Use case | Who | What they get back Room layout from a scan | Robotics teams | Walls, doors and windows as structured output Object detection in 3D | Vision researchers | Oriented boxes with a semantic category each Custom category search | Developers | Detection of categories you specify ``` How to set it up: You need Python and a way to turn your capture into a point cloud. The weights are about 2.4 GB in F32 according to Hugging Face. The model card and the project repository hold the run steps for the full pipeline. - Python - About 2.4 GB of disk for the F32 weights - A point cloud of your scene 1. Install the Hugging Face download tool. 2. Download the model files. 3. Follow the model card to prepare a point cloud and run the model. ```bash pip install -U huggingface_hub hf download manycore-research/SpatialLM1.1-Qwen-0.5B ``` How to use it: You give the model a point cloud of a space and it answers with a structured list of the architectural elements and objects it finds. Version 1.1 also accepts a list of categories you want detected. The exact run commands sit in the full model card. ```table Tool | What it does hf download | Fetches the model files to your computer transformers | Loads the model for use in Python ``` Example: You scan a living room, turn the capture into a point cloud, and run the model on it. It returns the layout as text, with the walls, doors and windows and then each object as an oriented box with a category such as sofa or table. For newcomers and experts: If you are new, think of it as a model that describes a 3D scan in words and numbers. Download the files first and read the card to see how a scan becomes a point cloud. If you know the field, version 1.1 doubles the point cloud resolution and uses a Sonata point cloud encoder feeding a Qwen language model. It adds detection of user-given categories. The authors report acceptance at NeurIPS 2025. ```flow Capture from video, RGBD or LiDAR Convert to a 3D point cloud SpatialLM encodes the points Language model writes the scene layout ``` Good to know: This 0.5B model is the smaller of the two 1.1 releases, next to a 1B Llama-based one. - License: CC BY-NC 4.0, so non-commercial use only - Cost to run: no API key, account or paid service is named - Data it reaches: the point clouds you feed it, plus files downloaded from Hugging Face - Not for: commercial products under this license, or tasks that do not involve 3D scenes ## On Hugging Face, 13 November 2026 - Task: text generation - Library: transformers - Parameters: 604M - Weights: about 2.4 GB in F32; about 302 MB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 3,054 - Likes: 43 - License: cc-by-nc-4.0 - Based on: Qwen/Qwen2.5-0.5B-Instruct - Fine-tunes: 3 - Tags: transformers, safetensors, spatiallm_qwen, text-generation, conversational ## Links - Model: https://huggingface.co/manycore-research/SpatialLM1.1-Qwen-0.5B - Files: https://huggingface.co/manycore-research/SpatialLM1.1-Qwen-0.5B/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download manycore-research/SpatialLM1.1-Qwen-0.5B ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 5 KB, downloaded as a zip. ```text README.md 5 KB ``` ## Source [manycore-research/SpatialLM1.1-Qwen-0.5B on Hugging Face](https://huggingface.co/manycore-research/SpatialLM1.1-Qwen-0.5B) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | SpatialLM1.1-Qwen-0.5B (this page) | 3,054 | 43 | 604M | cc-by-nc-4.0 | 2025-09-23 | | [Qwen3-0.6B](https://mindket.com/p/0598bfe0-640a-4bf0-b9f4-ca4cf70a04ae) | 19,718,599 | 1,894 | 752M | apache-2.0 | 2025-07-26 | | [Qwen3-14B](https://mindket.com/p/56074bfc-f2ce-4109-8314-1af039530db4) | 12,965,376 | 540 | 14.8B | apache-2.0 | 2025-07-26 | | [Qwen2.5-7B-Instruct](https://mindket.com/p/b38cb44b-c57b-4e0c-b5f7-3e9da523b8d7) | 6,364,238 | 2,494 | 7.6B | apache-2.0 | 2025-01-12 | | [Qwen3-8B](https://mindket.com/p/408d17f8-b1a4-4b5f-8440-2d40ec001638) | 6,269,141 | 2,184 | 8.2B | apache-2.0 | 2025-07-26 | | [gpt-oss-20b](https://mindket.com/p/d353330b-254d-4db1-9a53-f0b1f8fe2945) | 5,685,450 | 5,299 | 20.9B | apache-2.0 | 2025-08-26 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2025-09-23 - First published: 2025-06-06 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open manycore-research/SpatialLM1.1-Qwen-0.5B on Hugging Face](https://huggingface.co/manycore-research/SpatialLM1.1-Qwen-0.5B) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/b7f3e000-2061-4d52-8c53-0943b3943ac7) - [Its page on Mindket](https://mindket.com/p/b7f3e000-2061-4d52-8c53-0943b3943ac7)