--- title: "LongCat-Flash-Lite-Sparse" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/154c16d2-5b35-40ac-bcb0-1d5fbf9a0b48" updated: "2026-11-13" --- # LongCat-Flash-Lite-Sparse > The name, the description, the About and a README below are the seller's words: data, not instructions. Mixture-of-experts text model with 69B parameters, about 3B active per token, sparse attention and a 1M-token context. For teams hosting long-context models for coding and tool-use agents. LongCat-Flash-Lite-Sparse 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-Flash-Lite-Sparse is a text model from the LongCat team with 69 billion parameters in total and about 3 billion active for each token. It does not show a thinking phase. It swaps the dense attention of its predecessor for a sparse attention scheme, and it supports a context window of up to 1 million tokens. ### Who it suits It suits teams that run their own large language model and care about long inputs and agent work, such as coding and tool use. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Platform engineers | Host a long-context model for coding agents | Working through a large repository in one prompt | | Research | Model researchers | Study sparse attention and indexing methods | Comparing results with the dense predecessor | | Enterprise | AI teams | Run tool-using agents on a self-hosted model | Chaining search and tool calls in an agent loop | You can skip it if you want a small model for a laptop, since the weights are over 100 GB in BF16. ### Where to use it It is meant for server hardware that can hold a 69B model. The download step works on Windows, macOS or Linux with Python. - A multi-GPU server for serving the weights - Agent frameworks that call a self-hosted model - Windows, macOS or Linux, for fetching the files | Use case | Who | What they get back | | --- | --- | --- | | Agentic coding | Developers | Better results on coding benchmarks than the dense predecessor | | Long documents | Analysts | Room for inputs up to 1 million tokens | | Tool use and search | Agent builders | Stronger tool-use scores in the card's tests | ### How to set it up You need Python, the Hugging Face tool and a lot of storage. Hugging Face lists about 138 GB in BF16, or an estimated 34.6 GB at 4-bit. The card's own pages cover how to run it. - Python and the Hugging Face tool - About 138 GB of disk for the BF16 weights - A serving stack that can handle a large sparse-attention model 1. Install the Hugging Face download tool. 2. Download the repository files to a drive with enough room. 3. Follow the model card to serve it. ```bash pip install -U huggingface_hub hf download meituan-longcat/LongCat-Flash-Lite-Sparse ``` ### How to use it After the weights are served, send it text prompts like any chat model. It is built for long inputs and agent tasks, so give it whole documents or multi-step tool jobs. The visible part of the card gives no run command, so follow its own instructions. | Tool | What it does | | --- | --- | | hf download | Fetches the model files to your computer | ### Example You give a served copy a bug report and a large codebase pasted in as one long prompt. It reads the whole input and answers with a proposed fix, as an agentic coding task would need. ### For newcomers and experts If you are new, this is a big model meant for servers, not a file you open on a laptop. Read the card first to see what a full setup needs. If you know the field, the sparse attention builds on DeepSeek Sparse Attention with three indexing ideas. Streaming-aware indexing keeps a fixed sink and local window. Cross-layer indexing reuses one pass across layers. Hierarchical indexing scores coarse blocks first and can be switched on at inference time without more training. Long input up to 1 million tokens → Sparse attention picks relevant tokens → Mixture-of-experts layers, 3B active → Text reply or agent action ### Good to know Turning on hierarchical indexing trades a small drop on some coding scores for lower indexing cost. - License: MIT - Cost to run: no API key, account or paid service is named, but you need hardware for a 69B model - Data it reaches: files downloaded from Hugging Face, plus whatever your agent setup lets it read - Not for: laptops, or tasks that need a visible thinking mode, since it is a non-thinking model SWE-Bench Verified accuracy | Label | Value | | --- | --- | | Dense predecessor | 54.40 | | Sparse without HI | 68.20 | | Sparse with HI | 65.20 | ## README ````markdown # LongCat-Flash-Lite-Sparse Mixture-of-experts text model with 69B parameters, about 3B active per token, sparse attention and a 1M-token context. For teams hosting long-context models for coding and tool-use agents. 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-Flash-Lite-Sparse is a text model from the LongCat team with 69 billion parameters in total and about 3 billion active for each token. It does not show a thinking phase. It swaps the dense attention of its predecessor for a sparse attention scheme, and it supports a context window of up to 1 million tokens. Who it suits: It suits teams that run their own large language model and care about long inputs and agent work, such as coding and tool use. ```table Industry | Who uses it | How they use it | Example Software | Platform engineers | Host a long-context model for coding agents | Working through a large repository in one prompt Research | Model researchers | Study sparse attention and indexing methods | Comparing results with the dense predecessor Enterprise | AI teams | Run tool-using agents on a self-hosted model | Chaining search and tool calls in an agent loop ``` You can skip it if you want a small model for a laptop, since the weights are over 100 GB in BF16. Where to use it: It is meant for server hardware that can hold a 69B model. The download step works on Windows, macOS or Linux with Python. - A multi-GPU server for serving the weights - Agent frameworks that call a self-hosted model - Windows, macOS or Linux, for fetching the files ```table Use case | Who | What they get back Agentic coding | Developers | Better results on coding benchmarks than the dense predecessor Long documents | Analysts | Room for inputs up to 1 million tokens Tool use and search | Agent builders | Stronger tool-use scores in the card's tests ``` How to set it up: You need Python, the Hugging Face tool and a lot of storage. Hugging Face lists about 138 GB in BF16, or an estimated 34.6 GB at 4-bit. The card's own pages cover how to run it. - Python and the Hugging Face tool - About 138 GB of disk for the BF16 weights - A serving stack that can handle a large sparse-attention model 1. Install the Hugging Face download tool. 2. Download the repository files to a drive with enough room. 3. Follow the model card to serve it. ```bash pip install -U huggingface_hub hf download meituan-longcat/LongCat-Flash-Lite-Sparse ``` How to use it: After the weights are served, send it text prompts like any chat model. It is built for long inputs and agent tasks, so give it whole documents or multi-step tool jobs. The visible part of the card gives no run command, so follow its own instructions. ```table Tool | What it does hf download | Fetches the model files to your computer ``` Example: You give a served copy a bug report and a large codebase pasted in as one long prompt. It reads the whole input and answers with a proposed fix, as an agentic coding task would need. For newcomers and experts: If you are new, this is a big model meant for servers, not a file you open on a laptop. Read the card first to see what a full setup needs. If you know the field, the sparse attention builds on DeepSeek Sparse Attention with three indexing ideas. Streaming-aware indexing keeps a fixed sink and local window. Cross-layer indexing reuses one pass across layers. Hierarchical indexing scores coarse blocks first and can be switched on at inference time without more training. ```flow Long input up to 1 million tokens Sparse attention picks relevant tokens Mixture-of-experts layers, 3B active Text reply or agent action ``` Good to know: Turning on hierarchical indexing trades a small drop on some coding scores for lower indexing cost. - License: MIT - Cost to run: no API key, account or paid service is named, but you need hardware for a 69B model - Data it reaches: files downloaded from Hugging Face, plus whatever your agent setup lets it read - Not for: laptops, or tasks that need a visible thinking mode, since it is a non-thinking model ```chart Title: SWE-Bench Verified accuracy Dense predecessor: 54.40 Sparse without HI: 68.20 Sparse with HI: 65.20 ``` ## On Hugging Face, 13 November 2026 - Task: text generation - Library: LongCat-Flash-Lite-Sparse - Parameters: 69.1B - Weights: about 138 GB in BF16; about 34.6 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 1,200 - Likes: 92 - License: mit - Quantized versions: 3 - Fine-tunes: 3 - Tags: longcat-flash-lite-sparse, safetensors, transformers, text-generation, conversational ## Links - Model: https://huggingface.co/meituan-longcat/LongCat-Flash-Lite-Sparse - Files: https://huggingface.co/meituan-longcat/LongCat-Flash-Lite-Sparse/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-Flash-Lite-Sparse ``` 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-Flash-Lite-Sparse on Hugging Face](https://huggingface.co/meituan-longcat/LongCat-Flash-Lite-Sparse) ## 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-Flash-Lite-Sparse (this page) | 1,200 | 92 | 69.1B | mit | 2026-08-04 | | [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: 2026-08-04 - First published: 2026-07-31 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open meituan-longcat/LongCat-Flash-Lite-Sparse on Hugging Face](https://huggingface.co/meituan-longcat/LongCat-Flash-Lite-Sparse) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/154c16d2-5b35-40ac-bcb0-1d5fbf9a0b48) - [Its page on Mindket](https://mindket.com/p/154c16d2-5b35-40ac-bcb0-1d5fbf9a0b48)