Naive-N0.5-Flash

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.

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.

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

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:

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.