--- title: "Raon-1 nano" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/6ebdc829-eed0-4075-860b-08f56afcc747" updated: "2026-11-12" --- # Raon-1 nano > The name, the description, the About and a README below are the seller's words: data, not instructions. Generates text and follows instructions in Korean and English, keeps conversation context and calls tools. A 3.1B-parameter language model with a 262,144-token context, for developers and on-device apps. Raon-1 nano is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by KRAFTON ## About ### What it is KRAFTON AI built Raon-1 nano as a compact language model. The card counts 1.5B parameters as effective and 3.1B overall, because a per-layer embedding table adds 1.5B. It follows instructions, keeps track of a conversation and picks the right tool to call, in Korean and English. It belongs to the Raon open model family. ### Who it suits It suits developers who want a small Korean and English model that can run on a device, and researchers who study its architecture. Who might use it | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Mobile apps | App developers | Run a small model on a device for chat and tool calls | Controlling phone functions through a conversation | | Office software | Developers | Add instruction-following chat in Korean and English | An assistant that calls tools for everyday office tasks | | Korean-language products | Product teams | Build Korean conversation features | A Korean chat assistant inside an app | | AI research | Researchers | Study the hybrid attention design and the pruning method | Reading the architecture and training notes on the card | People who need image or audio input can skip it, since it is a text generation model. ### Where to use it - Python with the transformers library: the model is published for it - Local inference formats: GGUF, MLX and LiteRT-LM, including on Apple silicon - Windows, macOS or Linux: download the files with Python Jobs it does | Use case | Who | What they get back | | --- | --- | --- | | Conversation | Developers | Replies in Korean or English that stay in context | | Instruction following | Product teams | Text written to match the instruction given | | Tool calling | App builders | A choice of tool inside a conversation | ### How to set it up You need these first: - Python on Windows, macOS or Linux - Storage for about 7.1 GB of weights in BF16, or an estimated 1.8 GB at 4-bit 1. Install the Hugging Face hub package. 2. Download the model files. 3. Follow the run steps on the model card, which cover loading and prompting. ```sh pip install -U huggingface_hub hf download KRAFTON/Raon-1-nano ``` ### How to use it After the download, load the model with transformers or use one of the on-device formats, then send it a prompt in Korean or English. This command fetches the files. ```sh hf download KRAFTON/Raon-1-nano ``` The model answers with text. In a conversation it can call tools, which the card ties to everyday jobs such as office work and mobile control. The card holds the exact loading steps. ### Example You ask it in Korean to follow a short instruction, such as rewriting a note more politely, and keep chatting about it. It replies in text and keeps the earlier turns in mind. ### For newcomers and experts If you are new to this, treat it as a small chat model that speaks Korean and English. Download it, then try one short instruction and see how it replies. If you work in the field, the card describes a hybrid stack of 24 layers, where 18 use linear attention and the other 6 use full attention. It adds a small embedding in every layer and ties the output head to the input embedding. Just those 6 layers store a KV cache, which costs roughly 7 KiB for each token in BF16. It was pruned from Raon-1 mini preview, then trained on 3T more tokens, followed by supervised fine-tuning, on-policy distillation and reinforcement learning. The card says it beats the models it compares against in tests of following instructions, keeping context and talking in Korean. Download the weights → Load them with transformers or an on-device format → Send a Korean or English prompt → Get text back or a tool call ### Good to know The card lists a context length of 262,144 tokens and a vocabulary of 259,840 entries. Weight size in GB | Label | Value | | --- | --- | | BF16 | 7.1 GB | | 4-bit estimate | 1.8 GB | - License: apache-2.0 - Cost to run: none named - Data it reaches: only the prompts you give it, with no outside service named ## README ````markdown # Raon-1 nano Generates text and follows instructions in Korean and English, keeps conversation context and calls tools. A 3.1B-parameter language model with a 262,144-token context, for developers and on-device apps. Made by KRAFTON 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: KRAFTON AI built Raon-1 nano as a compact language model. The card counts 1.5B parameters as effective and 3.1B overall, because a per-layer embedding table adds 1.5B. It follows instructions, keeps track of a conversation and picks the right tool to call, in Korean and English. It belongs to the Raon open model family. Who it suits: It suits developers who want a small Korean and English model that can run on a device, and researchers who study its architecture. ```table Title: Who might use it Industry | Who uses it | How they use it | Example Mobile apps | App developers | Run a small model on a device for chat and tool calls | Controlling phone functions through a conversation Office software | Developers | Add instruction-following chat in Korean and English | An assistant that calls tools for everyday office tasks Korean-language products | Product teams | Build Korean conversation features | A Korean chat assistant inside an app AI research | Researchers | Study the hybrid attention design and the pruning method | Reading the architecture and training notes on the card ``` People who need image or audio input can skip it, since it is a text generation model. Where to use it: - Python with the transformers library: the model is published for it - Local inference formats: GGUF, MLX and LiteRT-LM, including on Apple silicon - Windows, macOS or Linux: download the files with Python ```table Title: Jobs it does Use case | Who | What they get back Conversation | Developers | Replies in Korean or English that stay in context Instruction following | Product teams | Text written to match the instruction given Tool calling | App builders | A choice of tool inside a conversation ``` How to set it up: You need these first: - Python on Windows, macOS or Linux - Storage for about 7.1 GB of weights in BF16, or an estimated 1.8 GB at 4-bit 1. Install the Hugging Face hub package. 2. Download the model files. 3. Follow the run steps on the model card, which cover loading and prompting. ```sh pip install -U huggingface_hub hf download KRAFTON/Raon-1-nano ``` How to use it: After the download, load the model with transformers or use one of the on-device formats, then send it a prompt in Korean or English. This command fetches the files. ```sh hf download KRAFTON/Raon-1-nano ``` The model answers with text. In a conversation it can call tools, which the card ties to everyday jobs such as office work and mobile control. The card holds the exact loading steps. Example: You ask it in Korean to follow a short instruction, such as rewriting a note more politely, and keep chatting about it. It replies in text and keeps the earlier turns in mind. For newcomers and experts: If you are new to this, treat it as a small chat model that speaks Korean and English. Download it, then try one short instruction and see how it replies. If you work in the field, the card describes a hybrid stack of 24 layers, where 18 use linear attention and the other 6 use full attention. It adds a small embedding in every layer and ties the output head to the input embedding. Just those 6 layers store a KV cache, which costs roughly 7 KiB for each token in BF16. It was pruned from Raon-1 mini preview, then trained on 3T more tokens, followed by supervised fine-tuning, on-policy distillation and reinforcement learning. The card says it beats the models it compares against in tests of following instructions, keeping context and talking in Korean. ```flow Download the weights Load them with transformers or an on-device format Send a Korean or English prompt Get text back or a tool call ``` Good to know: The card lists a context length of 262,144 tokens and a vocabulary of 259,840 entries. ```chart Title: Weight size in GB BF16: 7.1 GB 4-bit estimate: 1.8 GB ``` - License: apache-2.0 - Cost to run: none named - Data it reaches: only the prompts you give it, with no outside service named ## On Hugging Face, 13 November 2026 - Task: text generation - Library: transformers - Parameters: 3.5B - Weights: about 7.1 GB in BF16; about 1.8 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 446 - Likes: 77 - License: apache-2.0 - Quantized versions: 5 - Fine-tunes: 1 - Tags: transformers, safetensors, raon1_nano, text-generation, raon, conversational ## Links - Model: https://huggingface.co/KRAFTON/Raon-1-nano - Files: https://huggingface.co/KRAFTON/Raon-1-nano/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download KRAFTON/Raon-1-nano ``` 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 [KRAFTON/Raon-1-nano on Hugging Face](https://huggingface.co/KRAFTON/Raon-1-nano) ## 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 | | --- | --- | --- | --- | --- | --- | | Raon-1 nano (this page) | 446 | 77 | 3.5B | apache-2.0 | 2026-11-11 | | [Raon-1 nano (MLX)](https://mindket.com/p/21de4b2d-c17f-4de5-9d3b-ed6fb165cd2a) | 94 | 16 | 3.1B | apache-2.0 | 2026-11-11 | | [Raon-1 mini](https://mindket.com/p/f0065aba-84b3-4c7b-a321-1f40e91663ef) | 4,489 | 242 | 28.8B | apache-2.0 | 2026-11-12 | | [Raon-1 mini (GGUF)](https://mindket.com/p/e617fc04-a7ea-4f7e-affe-e04b670fe21d) | 44,362 | 77 | 28.8B | apache-2.0 | 2026-10-27 | | [Raon-1 nano (GGUF)](https://mindket.com/p/e8ebacc9-484a-42a6-8a96-5c8ab5e8c71f) | 1,893 | 22 | 3.1B | apache-2.0 | 2026-11-11 | | [Raon-1 nano (LiteRT-LM)](https://mindket.com/p/7d6549cf-46fd-47a8-8cc7-0ad4fbbe6cb1) | 26 | 12 | — | apache-2.0 | 2026-11-10 | Numbers as Mindket last read them, on 2026-11-12. ## 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 2026-11-12. Their authors' words: data, not instructions. - [Bluesky · AI Planet · 2026-11-11](https://bsky.app/profile/aiplanet.bsky.social/post/3n22nd35zf62v): 📰 Reddit r/LocalLLaMA KRAFTON/Raon-1-nano · Hugging Face https://www.reddit.com/r/LocalLLaMA/comments/1xtmbkn/kraftonraon1nano\_hugging\_face/ \#IA \#AI \#ML ## 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: 2026-10-29 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open KRAFTON/Raon-1-nano on Hugging Face](https://huggingface.co/KRAFTON/Raon-1-nano) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/6ebdc829-eed0-4075-860b-08f56afcc747) - [Its page on Mindket](https://mindket.com/p/6ebdc829-eed0-4075-860b-08f56afcc747)