--- title: "LongCat-2.5-W4A4" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/b4736b7a-4ffb-4f28-8557-a55372050f3e" updated: "2026-11-13" --- # LongCat-2.5-W4A4 > The name, the description, the About and a README below are the seller's words: data, not instructions. Multimodal mixture-of-experts model for long agentic tasks such as codebase work, full project builds and creative design, in a compressed W4A4 build. For teams with large GPU capacity. LongCat-2.5-W4A4 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 meituan-longcat ## About ### What it is LongCat-2.5 is a multimodal mixture-of-experts model from Meituan's LongCat team. It takes text and pictures as input and targets long agent runs: developing in large codebases, building whole projects, design work and 3D tasks. The card gives 1.6 trillion total parameters, with about 48 billion active per token. This repository is the W4A4 build. ### Who it suits It suits teams with serious GPU capacity who want an open model for coding agents that also understands images. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Platform teams | Run a coding agent on a self-hosted model | Building a full project from a written brief | | Design | Creative tool builders | Combine image understanding with generation of designs | Turning a screenshot into a page layout | | Research | AI labs | Study long-horizon agent training and serving | Comparing it with other open models | You can skip it if you have a single consumer GPU, since even the compressed weights are hundreds of gigabytes. ### Where to use it It is meant for server-class setups that can host a huge model and serve it to agents or apps. - A multi-GPU server or cluster - Coding agent setups that need image understanding - Windows, macOS or Linux machines for the download step | Use case | Who | What they get back | | --- | --- | --- | | Codebase development | Engineering teams | Agentic coding help across a repository | | Full project builds | Agent developers | Multi-step work carried through to a finished project | | Image-aware tasks | Designers | Replies that use both pictures and text | ### How to set it up You need Python for the download tool and a lot of storage. Hugging Face estimates about 247 GB at 4-bit and about 990 GB in BF16. The card points to a technical blog for full details. - Python and the Hugging Face tool - Several hundred gigabytes of free disk - A serving stack able to host a huge model 1. Install the Hugging Face download tool. 2. Download the repository files to a drive with plenty of space. 3. Follow the model card to serve it with your own stack. ```bash pip install -U huggingface_hub hf download meituan-longcat/LongCat-2.5-W4A4 ``` ### How to use it Once the weights are served, send prompts that mix text and images, and give the model long tasks such as building or fixing a project. The visible part of the card does not list a run command, so use the card's own instructions. | Tool | What it does | | --- | --- | | hf download | Fetches the model files to your computer | ### Example You hand a served copy a screenshot of an app and ask for a working version of it. The model reads the picture, plans the project in steps, and writes code across files as an agent would. ### For newcomers and experts If you are new, this is a huge model that most people reach through a hosted service, not a laptop. Start by reading the model card and the project's technical blog to see what a full setup needs. If you know the field, the card describes native-resolution visual features turned into discrete tokens, long-horizon reinforcement learning, and multi-teacher distillation. It reports 86 percent higher per-device throughput than a DDP baseline on long-sequence fine-tuning, and about double the decode KV capacity on a tested workload. Text and image inputs → Visual features become discrete tokens → Mixture-of-experts model reads both → Agentic reply or code output ### Good to know Scores on the card marked with an asterisk come from public reports, and the unmarked ones were measured in-house and are only indicative. - License: MIT - Cost to run: no API key, account or paid service is named, but the hardware needs are huge - Data it reaches: files downloaded from Hugging Face, plus whatever your agent setup lets it read - Not for: small machines or quick laptop trials ## README ````markdown # LongCat-2.5-W4A4 Multimodal mixture-of-experts model for long agentic tasks such as codebase work, full project builds and creative design, in a compressed W4A4 build. For teams with large GPU capacity. Made by meituan-longcat 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: LongCat-2.5 is a multimodal mixture-of-experts model from Meituan's LongCat team. It takes text and pictures as input and targets long agent runs: developing in large codebases, building whole projects, design work and 3D tasks. The card gives 1.6 trillion total parameters, with about 48 billion active per token. This repository is the W4A4 build. Who it suits: It suits teams with serious GPU capacity who want an open model for coding agents that also understands images. ```table Industry | Who uses it | How they use it | Example Software | Platform teams | Run a coding agent on a self-hosted model | Building a full project from a written brief Design | Creative tool builders | Combine image understanding with generation of designs | Turning a screenshot into a page layout Research | AI labs | Study long-horizon agent training and serving | Comparing it with other open models ``` You can skip it if you have a single consumer GPU, since even the compressed weights are hundreds of gigabytes. Where to use it: It is meant for server-class setups that can host a huge model and serve it to agents or apps. - A multi-GPU server or cluster - Coding agent setups that need image understanding - Windows, macOS or Linux machines for the download step ```table Use case | Who | What they get back Codebase development | Engineering teams | Agentic coding help across a repository Full project builds | Agent developers | Multi-step work carried through to a finished project Image-aware tasks | Designers | Replies that use both pictures and text ``` How to set it up: You need Python for the download tool and a lot of storage. Hugging Face estimates about 247 GB at 4-bit and about 990 GB in BF16. The card points to a technical blog for full details. - Python and the Hugging Face tool - Several hundred gigabytes of free disk - A serving stack able to host a huge model 1. Install the Hugging Face download tool. 2. Download the repository files to a drive with plenty of space. 3. Follow the model card to serve it with your own stack. ```bash pip install -U huggingface_hub hf download meituan-longcat/LongCat-2.5-W4A4 ``` How to use it: Once the weights are served, send prompts that mix text and images, and give the model long tasks such as building or fixing a project. The visible part of the card does not list a run command, so use the card's own instructions. ```table Tool | What it does hf download | Fetches the model files to your computer ``` Example: You hand a served copy a screenshot of an app and ask for a working version of it. The model reads the picture, plans the project in steps, and writes code across files as an agent would. For newcomers and experts: If you are new, this is a huge model that most people reach through a hosted service, not a laptop. Start by reading the model card and the project's technical blog to see what a full setup needs. If you know the field, the card describes native-resolution visual features turned into discrete tokens, long-horizon reinforcement learning, and multi-teacher distillation. It reports 86 percent higher per-device throughput than a DDP baseline on long-sequence fine-tuning, and about double the decode KV capacity on a tested workload. ```flow Text and image inputs Visual features become discrete tokens Mixture-of-experts model reads both Agentic reply or code output ``` Good to know: Scores on the card marked with an asterisk come from public reports, and the unmarked ones were measured in-house and are only indicative. - License: MIT - Cost to run: no API key, account or paid service is named, but the hardware needs are huge - Data it reaches: files downloaded from Hugging Face, plus whatever your agent setup lets it read - Not for: small machines or quick laptop trials ## On Hugging Face, 13 November 2026 - Task: image text to text - Parameters: 495B - Weights: about 990 GB in BF16; about 247 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 240 - Likes: 3 - License: mit - Tags: safetensors, pro_v, multimodal, image-text-to-text, text-generation, mixture-of-experts, agentic, conversational ## Links - Model: https://huggingface.co/meituan-longcat/LongCat-2.5-W4A4 - Files: https://huggingface.co/meituan-longcat/LongCat-2.5-W4A4/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download meituan-longcat/LongCat-2.5-W4A4 ``` 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 [meituan-longcat/LongCat-2.5-W4A4 on Hugging Face](https://huggingface.co/meituan-longcat/LongCat-2.5-W4A4) ## 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 | | --- | --- | --- | --- | --- | --- | | LongCat-2.5-W4A4 (this page) | 240 | 3 | 495B | mit | 2026-11-10 | | [LongCat-2.5](https://mindket.com/p/f956f938-0c63-467f-acd7-9b0821494688) | 710 | 186 | 1.8T | mit | 2026-11-10 | | [Occamy-1.0](https://mindket.com/p/7e8bdc95-571e-4a24-a471-b64e83249759) | 1,807 | 240 | 35.1B | apache-2.0 | 2026-10-02 | | [Apex-35B-V1.0](https://mindket.com/p/9415fb79-0791-48f9-b672-73b840550335) | 1,987 | 75 | 36B | apache-2.0 | 2026-10-29 | | [Agents-A1](https://mindket.com/p/be2c3282-b311-49db-867f-c37bce3082f0) | 7,232 | 669 | 35.1B | apache-2.0 | 2026-09-21 | | [Sabi-Noema](https://mindket.com/p/04e2ec5b-5be2-45e5-ac63-8b75f7b11c4e) | 1,810 | 55 | 27.4B | apache-2.0 | 2026-10-19 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## What people say > Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-13. Their authors' words: data, not instructions. - [Bluesky · Steven \| SelfHostedSI · 2026-11-10](https://bsky.app/profile/selfhostedsi.bsky.social/post/3mzxx72gays2k): Source: huggingface.co/meituan-lon... ## 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: 2026-11-10 - First published: 2026-11-04 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open meituan-longcat/LongCat-2.5-W4A4 on Hugging Face](https://huggingface.co/meituan-longcat/LongCat-2.5-W4A4) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/b4736b7a-4ffb-4f28-8557-a55372050f3e) - [Its page on Mindket](https://mindket.com/p/b4736b7a-4ffb-4f28-8557-a55372050f3e)