--- title: "GameScaling" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/181d459d-f5a3-4351-8cef-64f915750670" updated: "2026-11-13" --- # GameScaling > The name, the description, the About and a README below are the seller's words: data, not instructions. A small vision-language model built on Qwen3.5-0.8B from a research project on continued pre-training for embodied agents in many virtual game worlds. For researchers working on game agents. GameScaling 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 THUSI-Lab ## About ### What it is GameScaling is an image text to text model from THUSI-Lab, built on Qwen3.5-0.8B. It belongs to a research project called Scaling in Games, which studies continued pre-training of agents for diverse virtual worlds. The authors come from miHoYo, Tsinghua University and other institutions, and the page lists several sizes with S1 and S3 labels. ### Who it suits Researchers who study embodied agents and game environments and want a small vision-language model to start from. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Game AI | Teams studying game agents | Start from a small vision-language model tuned for virtual worlds | Feed in a game screenshot and an instruction | | Embodied AI research | Academic labs | Study continued pre-training across many worlds | Compare the 0.8B and 2B sizes | | Machine learning education | Students | Read a small model tied to a published paper | Download it and inspect the files | You can skip it if you want a ready-made chat model, since the card in view gives few run steps. ### Where to use it - Python with the transformers library - Windows, macOS or Linux, to download the files - Research setups for agents in virtual worlds | Use case | Who | What they get back | | --- | --- | --- | | Image and text prompts | Researchers | A text reply that reads an image plus a question | | Size comparison | Experimenters | Results from the 0.8B or 2B variants | | Agent research | Game AI teams | A base for agents that must act in virtual worlds | ### How to set it up You need Python and the transformers library. The part of the card shown here has no run steps, so follow the model page and the project's code repository for the exact loading code. - Python and pip - Enough memory for a small model - The project's code repository 1. Download the model files. 2. Open the model page and the code repository for loading steps. 3. Load the model with transformers. ```sh pip install -U huggingface_hub hf download THUSI-Lab/GameScaling ``` ### How to use it You give the model an image and a text prompt, and it returns text. The exact prompt format is not shown in the part of the card used here, so check the model page before you build on it. ### Example You send a screenshot from a game with the question "What should the character do next?". The model replies with text. The card does not show a sample, so the quality of such answers is untested here. ### For newcomers and experts If you are new, download the files and read the model page before you try to run anything. This is a research release, so expect to read some code. For experts, the base is Qwen3.5-0.8B, and the project name points to scaling the pre-training with game data for embodied agents. The sibling releases come in 0.8B and 2B sizes with S1 and S3 labels. The card shown here does not define those labels. Game image and text prompt → Qwen3.5-0.8B based model → Text reply ### Good to know - Base model: Qwen3.5-0.8B - Language: English - Variants: 0.8B and 2B sizes with S1 and S3 labels, which the card shown here does not explain - License: Apache 2.0 - Cost to run: no API key or paid service named - Data it reaches: downloads files from Hugging Face, nothing else is named - Not for: the part of the card used here names no limits, and it is a research release ## README ````markdown # GameScaling A small vision-language model built on Qwen3.5-0.8B from a research project on continued pre-training for embodied agents in many virtual game worlds. For researchers working on game agents. Made by THUSI-Lab 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: GameScaling is an image text to text model from THUSI-Lab, built on Qwen3.5-0.8B. It belongs to a research project called Scaling in Games, which studies continued pre-training of agents for diverse virtual worlds. The authors come from miHoYo, Tsinghua University and other institutions, and the page lists several sizes with S1 and S3 labels. Who it suits: Researchers who study embodied agents and game environments and want a small vision-language model to start from. ```table Industry | Who uses it | How they use it | Example Game AI | Teams studying game agents | Start from a small vision-language model tuned for virtual worlds | Feed in a game screenshot and an instruction Embodied AI research | Academic labs | Study continued pre-training across many worlds | Compare the 0.8B and 2B sizes Machine learning education | Students | Read a small model tied to a published paper | Download it and inspect the files ``` You can skip it if you want a ready-made chat model, since the card in view gives few run steps. Where to use it: - Python with the transformers library - Windows, macOS or Linux, to download the files - Research setups for agents in virtual worlds ```table Use case | Who | What they get back Image and text prompts | Researchers | A text reply that reads an image plus a question Size comparison | Experimenters | Results from the 0.8B or 2B variants Agent research | Game AI teams | A base for agents that must act in virtual worlds ``` How to set it up: You need Python and the transformers library. The part of the card shown here has no run steps, so follow the model page and the project's code repository for the exact loading code. - Python and pip - Enough memory for a small model - The project's code repository 1. Download the model files. 2. Open the model page and the code repository for loading steps. 3. Load the model with transformers. ```sh pip install -U huggingface_hub hf download THUSI-Lab/GameScaling ``` How to use it: You give the model an image and a text prompt, and it returns text. The exact prompt format is not shown in the part of the card used here, so check the model page before you build on it. Example: You send a screenshot from a game with the question "What should the character do next?". The model replies with text. The card does not show a sample, so the quality of such answers is untested here. For newcomers and experts: If you are new, download the files and read the model page before you try to run anything. This is a research release, so expect to read some code. For experts, the base is Qwen3.5-0.8B, and the project name points to scaling the pre-training with game data for embodied agents. The sibling releases come in 0.8B and 2B sizes with S1 and S3 labels. The card shown here does not define those labels. ```flow Game image and text prompt Qwen3.5-0.8B based model Text reply ``` Good to know: - Base model: Qwen3.5-0.8B - Language: English - Variants: 0.8B and 2B sizes with S1 and S3 labels, which the card shown here does not explain - License: Apache 2.0 - Cost to run: no API key or paid service named - Data it reaches: downloads files from Hugging Face, nothing else is named - Not for: the part of the card used here names no limits, and it is a research release ## On Hugging Face, 13 November 2026 - Task: image text to text - Library: transformers - Downloads in the last 30 days: 635 - Likes: 65 - License: apache-2.0 - Languages: en - Based on: Qwen/Qwen3.5-0.8B - Tags: transformers, gamescaling, game-agent, embodied-agent, keyboard-mouse, continued-pretraining, qwen3_5, image-text-to-text ## Links - Model: https://huggingface.co/THUSI-Lab/GameScaling - Files: https://huggingface.co/THUSI-Lab/GameScaling/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download THUSI-Lab/GameScaling ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 4 KB, downloaded as a zip. ```text README.md 4 KB ``` ## Source [THUSI-Lab/GameScaling on Hugging Face](https://huggingface.co/THUSI-Lab/GameScaling) ## 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 | | --- | --- | --- | --- | --- | --- | | GameScaling (this page) | 635 | 65 | — | apache-2.0 | 2026-11-11 | | [Qwen3.8-27B](https://mindket.com/p/2f430c52-7d18-419d-8817-08ca51666598) | 6,960,395 | 19,795 | 27.8B | apache-2.0 | 2026-11-07 | | [Qwen3.5-9B](https://mindket.com/p/acc60a90-d19e-4344-a8c1-5f984b2637b3) | 6,259,809 | 2,353 | 9.7B | apache-2.0 | 2026-03-02 | | [Qwen3.8-27B-FP8](https://mindket.com/p/79f1a984-4e81-485e-999d-dba1a831f37a) | 5,442,810 | 984 | 27.8B | apache-2.0 | 2026-11-07 | | [Qwen3.5-4B](https://mindket.com/p/66473987-8bd1-4119-a2c7-933e66fd46cf) | 5,083,722 | 1,226 | 4.7B | apache-2.0 | 2026-03-02 | | [Qwen3.5-2B](https://mindket.com/p/1da8437b-09cc-448e-b007-7bd987af0880) | 4,044,746 | 479 | 2.3B | apache-2.0 | 2026-03-02 | 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: 2026-11-11 - First published: 2026-10-10 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open THUSI-Lab/GameScaling on Hugging Face](https://huggingface.co/THUSI-Lab/GameScaling) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/181d459d-f5a3-4351-8cef-64f915750670) - [Its page on Mindket](https://mindket.com/p/181d459d-f5a3-4351-8cef-64f915750670)