--- title: "Naive-N0.5-Flash" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/183f84da-51ee-460e-874f-60d98cda9251" updated: "2026-11-13" --- # Naive-N0.5-Flash > The name, the description, the About and a README below are the seller's words: data, not instructions. Open-weight mixture-of-experts model with 309B parameters, 15.5B active and a native 1M token context, built for coding and AI research. For teams running large coding models on their own servers. Naive-N0.5-Flash 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 NaiveAI ## About ### What it is Naive-N0.5-Flash is an open mixture-of-experts model for coding and AI research work. Out of 309 billion parameters, only 15.5 billion are active at once. It supports a native context of 1 million tokens, using sliding-window attention and a lighter sparse attention, with no full-attention layers. ### Who it suits It suits teams that run their own large coding models, and researchers who study long-context attention or AI-driven research work. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Platform engineers | Host a long-context coding model | Working over a large repository in one request | | AI research | ML research teams | Automate parts of model research and training work | Running AI R\&D benchmark tasks | | Infrastructure | Inference engineers | Study fast serving of a sparse-attention model | Comparing speed modes of the NaiveRT system | You can skip it if you only have a single consumer GPU, since a 309B model needs a large multi-GPU setup. ### Where to use it It is meant for server hardware that can hold a 309B model. The download step works on Windows, macOS or Linux with Python. The makers also plan an API. - A multi-GPU server for serving the weights - NaiveRT, the makers' own inference system - Windows, macOS or Linux for fetching the files | Use case | Who | What they get back | | --- | --- | --- | | Coding agents | Developers | A model tuned for coding and agentic tasks | | Long documents | Analysts | Room for inputs of up to 1 million tokens | | Fast serving | Infra teams | Up to 2,000 tokens per second in NaiveRT's Ultrafast mode | ### How to set it up You need Python and a large amount of disk and GPU memory. The card gives no run command in the visible part and points to its technical blog and code repository for the inference system. Weights and inference code are under the MIT license. - Python and the Hugging Face tool - Disk and GPU memory for a 309B model - The makers' inference code, for the best speed 1. Install the Hugging Face download tool. 2. Download the repository files to a drive with plenty of room. 3. Follow the model card and the makers' code to serve it. ```bash pip install -U huggingface_hub hf download NaiveAI/Naive-N0.5-Flash ``` ### How to use it After serving, send chat or coding prompts and give it long inputs. In Standard mode the makers list 50 tokens a second for each user. Ultrafast mode reaches up to 2,000 tokens a second. | Tool | What it does | | --- | --- | | hf download | Fetches the model files to your computer | | NaiveRT | The makers' inference system for this model | ### Example You give a served copy a large codebase and a bug report in one prompt. The model reads the whole input, which can reach 1 million tokens, and replies with a proposed fix. ### For newcomers and experts If you are new, this is a huge model for servers, not something to open on a laptop. Read the card first to see what a full setup needs. If you know the field, 39 sliding-window layers use a 128 token window and 9 DeepSeek Sparse Attention layers pick the top 2,048 tokens, with grouped-query attention of four KV groups. It builds on MiMo-V2.5 and had 3.25 trillion tokens of multi-stage training. Long input up to 1M tokens → Sliding-window layers handle nearby tokens → Sparse attention layers select key tokens → Mixture-of-experts reply, 15.5B active ### Good to know An API is planned too. Per million tokens, the listed prices are 0.10 dollars for input, 0.40 dollars for output and 0.01 dollars for cache reads. - License: MIT for the weights and inference code - Cost to run: no API key or account is needed for local use, but the hardware is large, and the planned API is priced per million tokens - Data it reaches: files downloaded from Hugging Face, plus whatever your agent setup lets it read - Not for: small machines or laptops Attention layers in the 48-layer network | Label | Value | | --- | --- | | Sliding-window layers | 39 | | Sparse attention layers | 9 | ## README ````markdown # Naive-N0.5-Flash Open-weight mixture-of-experts model with 309B parameters, 15.5B active and a native 1M token context, built for coding and AI research. For teams running large coding models on their own servers. Made by NaiveAI 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: Naive-N0.5-Flash is an open mixture-of-experts model for coding and AI research work. Out of 309 billion parameters, only 15.5 billion are active at once. It supports a native context of 1 million tokens, using sliding-window attention and a lighter sparse attention, with no full-attention layers. Who it suits: It suits teams that run their own large coding models, and researchers who study long-context attention or AI-driven research work. ```table Industry | Who uses it | How they use it | Example Software | Platform engineers | Host a long-context coding model | Working over a large repository in one request AI research | ML research teams | Automate parts of model research and training work | Running AI R&D benchmark tasks Infrastructure | Inference engineers | Study fast serving of a sparse-attention model | Comparing speed modes of the NaiveRT system ``` You can skip it if you only have a single consumer GPU, since a 309B model needs a large multi-GPU setup. Where to use it: It is meant for server hardware that can hold a 309B model. The download step works on Windows, macOS or Linux with Python. The makers also plan an API. - A multi-GPU server for serving the weights - NaiveRT, the makers' own inference system - Windows, macOS or Linux for fetching the files ```table Use case | Who | What they get back Coding agents | Developers | A model tuned for coding and agentic tasks Long documents | Analysts | Room for inputs of up to 1 million tokens Fast serving | Infra teams | Up to 2,000 tokens per second in NaiveRT's Ultrafast mode ``` How to set it up: You need Python and a large amount of disk and GPU memory. The card gives no run command in the visible part and points to its technical blog and code repository for the inference system. Weights and inference code are under the MIT license. - Python and the Hugging Face tool - Disk and GPU memory for a 309B model - The makers' inference code, for the best speed 1. Install the Hugging Face download tool. 2. Download the repository files to a drive with plenty of room. 3. Follow the model card and the makers' code to serve it. ```bash pip install -U huggingface_hub hf download NaiveAI/Naive-N0.5-Flash ``` How to use it: After serving, send chat or coding prompts and give it long inputs. In Standard mode the makers list 50 tokens a second for each user. Ultrafast mode reaches up to 2,000 tokens a second. ```table Tool | What it does hf download | Fetches the model files to your computer NaiveRT | The makers' inference system for this model ``` Example: You give a served copy a large codebase and a bug report in one prompt. The model reads the whole input, which can reach 1 million tokens, and replies with a proposed fix. For newcomers and experts: If you are new, this is a huge model for servers, not something to open on a laptop. Read the card first to see what a full setup needs. If you know the field, 39 sliding-window layers use a 128 token window and 9 DeepSeek Sparse Attention layers pick the top 2,048 tokens, with grouped-query attention of four KV groups. It builds on MiMo-V2.5 and had 3.25 trillion tokens of multi-stage training. ```flow Long input up to 1M tokens Sliding-window layers handle nearby tokens Sparse attention layers select key tokens Mixture-of-experts reply, 15.5B active ``` Good to know: An API is planned too. Per million tokens, the listed prices are 0.10 dollars for input, 0.40 dollars for output and 0.01 dollars for cache reads. - License: MIT for the weights and inference code - Cost to run: no API key or account is needed for local use, but the hardware is large, and the planned API is priced per million tokens - Data it reaches: files downloaded from Hugging Face, plus whatever your agent setup lets it read - Not for: small machines or laptops ```chart Title: Attention layers in the 48-layer network Sliding-window layers: 39 Sparse attention layers: 9 ``` ## On Hugging Face, 13 November 2026 - Task: text generation - Downloads in the last 30 days: 8,145 - Likes: 353 - License: mit - Quantized versions: 6 - Tags: naive_n05_flash, code, long-context, ai-research, text-generation, conversational ## Links - Model: https://huggingface.co/NaiveAI/Naive-N0.5-Flash - Files: https://huggingface.co/NaiveAI/Naive-N0.5-Flash/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download NaiveAI/Naive-N0.5-Flash ``` 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 [NaiveAI/Naive-N0.5-Flash on Hugging Face](https://huggingface.co/NaiveAI/Naive-N0.5-Flash) ## 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 | | --- | --- | --- | --- | --- | --- | | Naive-N0.5-Flash (this page) | 8,145 | 353 | — | mit | 2026-09-27 | | [Terminal-RSI-27B](https://mindket.com/p/b7d63976-c860-4b80-abca-f0c045cd8b2b) | 2,536 | 33 | 27.8B | apache-2.0 | 2026-10-21 | | [MiniCPM5-2B](https://mindket.com/p/4d384af8-8281-4982-baed-7ea9aab0a3e1) | 1,039,518 | 1,850 | 2.5B | apache-2.0 | 2026-11-09 | | [Sharp-Spark-X2.5-4B-GGUF](https://mindket.com/p/d4d90f81-4886-4de3-ab95-d64c344a708c) | 875,346 | 164 | 4.1B | apache-2.0 | 2026-11-04 | | [MiniCPM5-2B-GGUF](https://mindket.com/p/47f50d35-3645-40b1-914d-63bedd57f448) | 481,119 | 393 | 2.5B | apache-2.0 | 2026-11-11 | | [MiMo-V2.6-Flash-RL](https://mindket.com/p/e12d85fd-2b7e-4dcd-91cb-bd6ace2adbf8) | 64,057 | 594 | 311B | mit | 2026-10-28 | 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. - [Hacker News · foruhar · 2026-09-28](https://news.ycombinator.com/item?id=49880647): Model Release: Naive-N0.5-Flash - [Bluesky · AI Assistant Store · 2026-09-27](https://bsky.app/profile/aiassistantstore.bsky.social/post/3mwjnjhefmc2h): NaiveAI Drops Naive-N0.5-Flash: Open 309B MoE Built by AI for Coding \& R\&D - [Bluesky · Smart Chunks · 2026-09-28](https://bsky.app/profile/smartchunksblog.bsky.social/post/3mwlgfpsse622): Ships 309B parameters as free MIT-licensed weights, prices API tokens at a penny per million cached, and dares GLM and DeepSeek to match it. smartchunks.com/naiveai-nai... - [Bluesky · NOVAIST · 2026-10-02](https://bsky.app/profile/novaist.jp/post/3mwvb6ruavk2u): 人間が方針を決め、AIがコード作成や実験、分析を支援。NaiveAIは新モデルの推論基盤開発で6日間に151件の最適化を試し、63件を採用した。 NaiveAI、3090億パラメーター「Naive-N0.5-Flash」公開 AIも開発工程に参加 novaist.jp/articles/nai... - [Bluesky · AlexTech.ai · 2026-09-28](https://bsky.app/profile/alextech.ai/post/3mwks3ktacl22): Goodbye full-attention. NaiveAI launches Naive-N0.5-Flash, a 309B MoE giant optimized for coding. Using SWA and DSA, it handles a 1M token context with speeds up to 2,000 tok/s. - [Bluesky · Digest AI News · 2026-09-27](https://bsky.app/profile/digestai.bsky.social/post/3mwjjl7fchk2r): NaiveAI has released Naive-N0.5-Flash, an open-weight Mixture-of-Experts (MoE) model with 309B total parameters and 15.5B active parameters. ## 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: 2026-09-27 - First published: 2026-09-27 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open NaiveAI/Naive-N0.5-Flash on Hugging Face](https://huggingface.co/NaiveAI/Naive-N0.5-Flash) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/183f84da-51ee-460e-874f-60d98cda9251) - [Its page on Mindket](https://mindket.com/p/183f84da-51ee-460e-874f-60d98cda9251)