--- title: "Ling-3.0-tiny" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/80976e1d-0e1c-46d4-8ca0-20563e9a571e" updated: "2026-11-12" --- # Ling-3.0-tiny > The name, the description, the About and a README below are the seller's words: data, not instructions. Generates text with a 7.9B hybrid reasoning model that activates only 1.3B parameters per token, so it fits local and low-resource setups. Suits developers who want reasoning and agents without datacenter GPUs. Ling-3.0-tiny 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 inclusionAI ## About ### What it is Ling-3.0-tiny is a lightweight reasoning model from inclusionAI with 7.9B total parameters and 1.3B active for each token. It can answer fast or reason in several steps, and you switch thinking on or off per request. It ships in BF16, FP8 and INT4 weights for a range of hardware. ### Who it suits Developers who want reasoning, coding and agent abilities at a low inference cost, on local machines or modest hardware. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Developers | Run a small reasoning model locally for coding help | Serve it on a MacBook or a Mac mini | | Agents | Agent builders | Use it as a cheap model inside agent workflows | Use thinking mode only for hard steps | | Education | Students | Work through math and science problems on a laptop | Ask a step-by-step question with thinking on | If you need the largest model quality and have datacenter GPUs, you can skip it. ### Where to use it - SGLang, which has a cookbook for this model - NVIDIA DGX Spark - Apple Silicon MacBooks and Mac minis - Windows, macOS or Linux, for downloading the files | Use case | Who | What they get back | | --- | --- | --- | | Local reasoning | Developers | Multi-step answers from a small model | | Fast replies | App builders | Quick answers with thinking switched off | | Agent tasks | Agent builders | Coding and tool-style task handling | ### How to set it up - Python with pip - Hardware that fits the weights, about 15.8 GB in BF16 or about 3.9 GB at 4-bit by Hugging Face's estimate - The Quickstart section of the model card, which covers SGLang 1. Install the Hugging Face hub package. 2. Download the model files. 3. Follow the card's Quickstart to serve it. ```sh pip install -U huggingface_hub hf download inclusionAI/Ling-3.0-tiny ``` ### How to use it Send prompts through your serving stack. Thinking mode is on by default, and the enable\_thinking setting turns it on or off for each request. For sampling, the card advises a temperature of 1.0, with top\_p set to 0.95 and top\_k set to 20. | Tool | What it does | | --- | --- | | enable\_thinking | Turns thinking mode on or off for a request | | SGLang | Serves the model, with a cookbook of launch recipes | ### Example You send a short math question with thinking on. The model reasons through the steps first and then gives its answer. With thinking off, you get a quick reply. ### For newcomers and experts If you are new, run the FP8 or INT4 copy on a laptop and start with thinking off for quick questions. Switch it on for harder problems. The architecture stacks 3 Kimi Delta Attention layers for every 1 Multi-Head Latent Attention layer, with a sparse mixture-of-experts feed-forward block of 128 routed experts. Each token uses 8 routed experts plus 1 shared expert. The card says this balances long-context ability, parameter efficiency and compute cost. A token enters a 4-layer block → 3 KDA layers and 1 MLA layer process it → The router picks 8 experts and 1 shared → Thinking mode is on or off per request ### Good to know - The card reports FP8 speeds of about 100 to 105 tokens per second on DGX Spark - It reports 86 to 90 tokens per second on an M4 Pro MacBook, with about 8.34 GiB of peak memory at 8K context - License: MIT - Cost to run: none named, you supply the hardware - Data it reaches: your prompts and the files downloaded from Hugging Face - Not for: users who need datacenter-scale quality from a small model Parameters in billions | Label | Value | | --- | --- | | Total | 7.9 billion | | Active per token | 1.3 billion | ## README ````markdown # Ling-3.0-tiny Generates text with a 7.9B hybrid reasoning model that activates only 1.3B parameters per token, so it fits local and low-resource setups. Suits developers who want reasoning and agents without datacenter GPUs. Made by inclusionAI 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: Ling-3.0-tiny is a lightweight reasoning model from inclusionAI with 7.9B total parameters and 1.3B active for each token. It can answer fast or reason in several steps, and you switch thinking on or off per request. It ships in BF16, FP8 and INT4 weights for a range of hardware. Who it suits: Developers who want reasoning, coding and agent abilities at a low inference cost, on local machines or modest hardware. ```table Industry | Who uses it | How they use it | Example Software | Developers | Run a small reasoning model locally for coding help | Serve it on a MacBook or a Mac mini Agents | Agent builders | Use it as a cheap model inside agent workflows | Use thinking mode only for hard steps Education | Students | Work through math and science problems on a laptop | Ask a step-by-step question with thinking on ``` If you need the largest model quality and have datacenter GPUs, you can skip it. Where to use it: - SGLang, which has a cookbook for this model - NVIDIA DGX Spark - Apple Silicon MacBooks and Mac minis - Windows, macOS or Linux, for downloading the files ```table Use case | Who | What they get back Local reasoning | Developers | Multi-step answers from a small model Fast replies | App builders | Quick answers with thinking switched off Agent tasks | Agent builders | Coding and tool-style task handling ``` How to set it up: - Python with pip - Hardware that fits the weights, about 15.8 GB in BF16 or about 3.9 GB at 4-bit by Hugging Face's estimate - The Quickstart section of the model card, which covers SGLang 1. Install the Hugging Face hub package. 2. Download the model files. 3. Follow the card's Quickstart to serve it. ```sh pip install -U huggingface_hub hf download inclusionAI/Ling-3.0-tiny ``` How to use it: Send prompts through your serving stack. Thinking mode is on by default, and the enable_thinking setting turns it on or off for each request. For sampling, the card advises a temperature of 1.0, with top_p set to 0.95 and top_k set to 20. ```table Tool | What it does enable_thinking | Turns thinking mode on or off for a request SGLang | Serves the model, with a cookbook of launch recipes ``` Example: You send a short math question with thinking on. The model reasons through the steps first and then gives its answer. With thinking off, you get a quick reply. For newcomers and experts: If you are new, run the FP8 or INT4 copy on a laptop and start with thinking off for quick questions. Switch it on for harder problems. The architecture stacks 3 Kimi Delta Attention layers for every 1 Multi-Head Latent Attention layer, with a sparse mixture-of-experts feed-forward block of 128 routed experts. Each token uses 8 routed experts plus 1 shared expert. The card says this balances long-context ability, parameter efficiency and compute cost. ```flow A token enters a 4-layer block 3 KDA layers and 1 MLA layer process it The router picks 8 experts and 1 shared Thinking mode is on or off per request ``` Good to know: - The card reports FP8 speeds of about 100 to 105 tokens per second on DGX Spark - It reports 86 to 90 tokens per second on an M4 Pro MacBook, with about 8.34 GiB of peak memory at 8K context - License: MIT - Cost to run: none named, you supply the hardware - Data it reaches: your prompts and the files downloaded from Hugging Face - Not for: users who need datacenter-scale quality from a small model ```chart Title: Parameters in billions Total: 7.9 billion Active per token: 1.3 billion ``` ## On Hugging Face, 13 November 2026 - Task: text generation - Parameters: 7.9B - Weights: about 15.8 GB in BF16; about 3.9 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 13,827 - Likes: 583 - License: mit - Quantized versions: 41 - Fine-tunes: 16 - Tags: safetensors, bailing_hybrid, text-generation, conversational ## Links - Model: https://huggingface.co/inclusionAI/Ling-3.0-tiny - Files: https://huggingface.co/inclusionAI/Ling-3.0-tiny/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download inclusionAI/Ling-3.0-tiny ``` 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 [inclusionAI/Ling-3.0-tiny on Hugging Face](https://huggingface.co/inclusionAI/Ling-3.0-tiny) ## 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 | | --- | --- | --- | --- | --- | --- | | Ling-3.0-tiny (this page) | 13,827 | 583 | 7.9B | mit | 2026-09-24 | | [Ling-3.1-flash](https://mindket.com/p/2afebb68-3c33-4e55-b49b-c93cc518f60e) | 8,327 | 170 | 572B | mit | 2026-11-03 | | [Ling-3.1-flash-1019](https://mindket.com/p/c864c59b-8518-4c57-acfb-c11c44b2310d) | 4,304 | 99 | 572B | mit | 2026-11-04 | | [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 | 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 · AbuAssar · 2026-09-05](https://news.ycombinator.com/item?id=49579871): Ling 3.0 Tiny - [Bluesky · Sung Kim · 2026-08-10](https://bsky.app/profile/sungkim.bsky.social/post/3msqmkurzlc2y): Alibaba Ant's Ling-3.0-tiny is now available as open-weight: BF16: huggingface.co/inclusionAI/... FP8: huggingface.co/inclusionAI/... INT4: huggingface.co/inclusionAI/... - [Bluesky · Sung Kim · 2026-08-19](https://bsky.app/profile/sungkim.bsky.social/post/3mth3y5nsks2y): 📥 Ling-3.0-tiny-base Hugging Face: huggingface.co/inclusionAI/... huggingface.co/inclusionAI/... huggingface.co/inclusionAI/... 📥 Ling-3.0-flash-base Hugging Face: huggingface.co/inclusionAI/... huggingface.co/inclusionAI/... huggingface.co/inclusionAI/... - [Bluesky · AI Adoption Agency · 2026-08-17](https://bsky.app/profile/aiadoptionagency.bsky.social/post/3mtbkfzx4ds26): 🧠 Ling 3.0 Tiny is here. 7.9B mixture of experts model from Ant Group. 1.3B active per token, 256K context, native function calling, configurable thinking mode. Runs local on Apple Silicon. Complete guide: aiadoptionagency.com/ling-3-tiny/ \#LocalLLM \#Ling3Tiny \#MoE - [Bluesky · Shawn Chauhan · 2026-08-10](https://bsky.app/profile/shawnchauhan1.bsky.social/post/3msq7h34qyw2j): The newest model this week isn't from a frontier lab. It's free, and you've probably never heard of it. InclusionAI, Ant Group's AI unit, released Ling 3.0 Tiny and Ling 3.0 Flash last week. - [Bluesky · Today's Top AI News · 2026-11-07](https://bsky.app/profile/todaystopainews.bsky.social/post/3mzpzjipabz2d): OpenRouter's heavy hitters: SpaceXAI's Grok 4.20 Multi-Agent \& Auto Router max out context. Budget kings: DeepSeek Flash, Z.ai GLM 5.2, and inclusionAI Ling 3.0 Tiny. \#artificialintelligence \#ai \#technology \#deepseek \#news \#grok - [Bluesky · deployedbyai · 2026-11-05](https://bsky.app/profile/deployedbyai.bsky.social/post/3n3zoehwq42tr): OpenRouter: InclusionAI Ling 3.0 Tiny on OpenRouter: $0.015/$0.045 per 1M, 262K context - [Bluesky · AI Planet · 2026-09-08](https://bsky.app/profile/aiplanet.bsky.social/post/3muzdhhtji72c): 📰 Reddit r/LocalLLaMA XHToken/Spark-X2.5-4B VS inclusionAI/Ling-3.0-tiny VS Nanbeige/Nanbeige4.2-3B https://www.reddit.com/r/LocalLLaMA/comments/1wao3d5/xhtokensparkx254b\_vs\_inclusionailing30tiny\_vs/ \#IA \#AI \#ML - [Bluesky · @pricepertoken.bsky.social · 2026-08-06](https://bsky.app/profile/pricepertoken.bsky.social/post/3msgv34b4gy26): BREAKING: InclusionAI releases Ling 3.0 Tiny MoE model with 1.3B active parameters out of 7.9B total. ## 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: 2026-09-24 - First published: 2026-08-10 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open inclusionAI/Ling-3.0-tiny on Hugging Face](https://huggingface.co/inclusionAI/Ling-3.0-tiny) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/80976e1d-0e1c-46d4-8ca0-20563e9a571e) - [Its page on Mindket](https://mindket.com/p/80976e1d-0e1c-46d4-8ca0-20563e9a571e)