--- title: "Ling-3.1-flash-1019" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/c864c59b-8518-4c57-acfb-c11c44b2310d" updated: "2026-11-13" --- # Ling-3.1-flash-1019 > The name, the description, the About and a README below are the seller's words: data, not instructions. Handles scientific and agentic tasks with 560B total and 25B active parameters and a 1M-token context, tuned further from Ling-3.1-flash. Serving examples use SGLang and vLLM on multi-GPU servers, under MIT. Ling-3.1-flash-1019 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 inclusionAI ## About ### What it is Ling-3.1-flash-1019 builds on Ling-3.1-flash, adds more post-training, and comes from inclusionAI. It has 560B parameters in total, uses 25B for each token, and reads a context of 1M tokens. Extra post-training raised its scores on scientific and agentic tests, which the card reports as better multi-step work and tool-driven workflows. ### Who it suits It suits teams with multi-GPU servers who want an open model for agents, scientific questions and office automation. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software and AI teams | Teams that serve large models | Run the model behind SGLang or vLLM | Start a server and send chat requests with thinking on | | Office automation | Teams that build agent workflows | Let the model plan and call tools over several steps | Run a task from the AutomationBench-AA style of work | | Science research | Researchers who test models on science questions | Compare it with its predecessor | Look at the CritPt and SciCode gains the card lists | If you do not have a multi-GPU server, you can skip it, since even the 4-bit weights are about 286 GB. ### Where to use it - SGLang, which the card recommends, through a Docker image - vLLM, with its own environment and server settings - The ModelScope and OpenRouter pages that the card links to - Windows, macOS or Linux with Python, to download the files | Use case | Who | What they get back | | --- | --- | --- | | Agent workflows | Developers | Multi-step task handling with tool calls | | Scientific questions | Researchers | Answers on tests such as CritPt and SciCode | | Tool-driven office tasks | Automation teams | Results from tasks that need tools and reasoning | ### How to set it up What you need first: - Docker with GPU access for the SGLang route, or uv and a Python environment for vLLM - A multi-GPU server, such as 8 x H200 for the card's example - Enough disk for about 572 GB of FP8 weights 1. Pull the SGLang image that supports this model family. 2. Start the server with the model path and the parallel settings for your GPUs. 3. Or prepare a vLLM environment and start its server instead. ```bash docker pull lmsysorg/sglang:dev-ling-3.1-flash ``` ```bash docker run --gpus all --network host --ipc=host --rm \ lmsysorg/sglang:dev-ling-3.1-flash \ python3 -m sglang.launch_server \ --model-path inclusionAI/Ling-3.1-flash \ --tp 8 --ep 8 \ --speculative-algorithm NEXTN \ --host 0.0.0.0 --port 30000 ``` For vLLM, create a uv environment first. ```bash pip install uv uv venv ~/my_ling_env source ~/my_ling_env/bin/activate ``` Next, clone the branch named vllm from the vLLM project on GitHub, then install it from inside that folder. ```bash cd vllm VLLM_USE_PRECOMPILED=1 uv pip install --editable . --torch-backend=auto ``` To fetch this repository's files yourself, use the Hugging Face tools. ```bash pip install -U huggingface_hub hf download inclusionAI/Ling-3.1-flash-1019 ``` ### How to use it Start a server, then send chat requests to it. The card's SGLang example names the Ling-3.1-flash weights, so check which weights you load. For the best results, set temperature 1.0, top\_p 0.95 and top\_k 20, and turn thinking on. ```bash vllm serve "$MODEL_PATH" \ --port "$PORT" \ --trust-remote-code \ --served-model-name auto \ --tensor-parallel-size 8 \ --gpu-memory-utilization 0.85 \ --enable-prefix-caching \ --enable-expert-parallel \ --mamba-cache-mode align \ --enable-auto-tool-choice \ --tool-call-parser ling3 \ --reasoning-parser ling3 \ --speculative-config '{"method":"mtp","num_speculative_tokens":3}' ``` | Tool | What it does | | --- | --- | | SGLang | Serves the model with NEXTN speculative decoding and Hopper decode optimizations | | vLLM | Serves it with tool call and reasoning parsers named ling3, plus MTP speculative decoding | ### Example You start the vLLM server on 8 GPUs and ask a short factual question with thinking on. The server answers in the OpenAI-style chat format, with the reasoning split out by the ling3 parser. ### For newcomers and experts This model needs a GPU cluster, so a first try is easier through the hosted pages the card links to. If you run it yourself, start with one short question and the recommended sampling settings. For experts, the design is a hybrid-linear mixture of experts with 63 KDA layers and 9 gated MLA layers in a 7 to 1 ratio, plus 4 dense layers. It has 512 routed experts, 1 shared expert and 8 activated experts per token, with 64 attention heads and a hidden size of 4096. The trade is memory: only 25B parameters work per token, but all 560B must be loaded. Prepare SGLang or a vLLM environment → Start the server with parallel settings → Send a chat request with thinking on → Get the reply with reasoning separated ### Good to know Artificial Analysis Intelligence Index v4.3.2 | Label | Value | | --- | --- | | Ling-3.1-flash | 41 | | Ling-3.1-flash-1019 | 44 | Reported gains over Ling-3.1-flash | Benchmark | Ling-3.1-flash | Ling-3.1-flash-1019 | | --- | --- | --- | | AA-Briefcase | 1397 | 1524 | | AutomationBench-AA | 62% | 68% | | CritPt | 18% | 26% | | SciCode | 54% | 57% | - Index v5: the newer Artificial Analysis index, version 5, gives it 30 according to the card. - Hardware: the example server uses 8 x H200 with TP8 and EP8. - Size: the files weigh about 572 GB in FP8 form, and about 286 GB at 4-bit, which is an estimate. - Scores: the authors report all benchmark numbers themselves. - License: mit - Cost to run: no API key or paid service named. The GPU hardware is the cost. - Data it reaches: it pulls a Docker image, a vLLM branch and the model files, and the example server listens on every network interface on port 30000. - Not for: small machines, since the example needs 8 x H200. ## README ````markdown # Ling-3.1-flash-1019 Handles scientific and agentic tasks with 560B total and 25B active parameters and a 1M-token context, tuned further from Ling-3.1-flash. Serving examples use SGLang and vLLM on multi-GPU servers, under MIT. 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-3.1-flash-1019 builds on Ling-3.1-flash, adds more post-training, and comes from inclusionAI. It has 560B parameters in total, uses 25B for each token, and reads a context of 1M tokens. Extra post-training raised its scores on scientific and agentic tests, which the card reports as better multi-step work and tool-driven workflows. Who it suits: It suits teams with multi-GPU servers who want an open model for agents, scientific questions and office automation. ```table Industry | Who uses it | How they use it | Example Software and AI teams | Teams that serve large models | Run the model behind SGLang or vLLM | Start a server and send chat requests with thinking on Office automation | Teams that build agent workflows | Let the model plan and call tools over several steps | Run a task from the AutomationBench-AA style of work Science research | Researchers who test models on science questions | Compare it with its predecessor | Look at the CritPt and SciCode gains the card lists ``` If you do not have a multi-GPU server, you can skip it, since even the 4-bit weights are about 286 GB. Where to use it: - SGLang, which the card recommends, through a Docker image - vLLM, with its own environment and server settings - The ModelScope and OpenRouter pages that the card links to - Windows, macOS or Linux with Python, to download the files ```table Use case | Who | What they get back Agent workflows | Developers | Multi-step task handling with tool calls Scientific questions | Researchers | Answers on tests such as CritPt and SciCode Tool-driven office tasks | Automation teams | Results from tasks that need tools and reasoning ``` How to set it up: What you need first: - Docker with GPU access for the SGLang route, or uv and a Python environment for vLLM - A multi-GPU server, such as 8 x H200 for the card's example - Enough disk for about 572 GB of FP8 weights 1. Pull the SGLang image that supports this model family. 2. Start the server with the model path and the parallel settings for your GPUs. 3. Or prepare a vLLM environment and start its server instead. ```bash docker pull lmsysorg/sglang:dev-ling-3.1-flash ``` ```bash docker run --gpus all --network host --ipc=host --rm \ lmsysorg/sglang:dev-ling-3.1-flash \ python3 -m sglang.launch_server \ --model-path inclusionAI/Ling-3.1-flash \ --tp 8 --ep 8 \ --speculative-algorithm NEXTN \ --host 0.0.0.0 --port 30000 ``` For vLLM, create a uv environment first. ```bash pip install uv uv venv ~/my_ling_env source ~/my_ling_env/bin/activate ``` Next, clone the branch named vllm from the vLLM project on GitHub, then install it from inside that folder. ```bash cd vllm VLLM_USE_PRECOMPILED=1 uv pip install --editable . --torch-backend=auto ``` To fetch this repository's files yourself, use the Hugging Face tools. ```bash pip install -U huggingface_hub hf download inclusionAI/Ling-3.1-flash-1019 ``` How to use it: Start a server, then send chat requests to it. The card's SGLang example names the Ling-3.1-flash weights, so check which weights you load. For the best results, set temperature 1.0, top_p 0.95 and top_k 20, and turn thinking on. ```bash vllm serve "$MODEL_PATH" \ --port "$PORT" \ --trust-remote-code \ --served-model-name auto \ --tensor-parallel-size 8 \ --gpu-memory-utilization 0.85 \ --enable-prefix-caching \ --enable-expert-parallel \ --mamba-cache-mode align \ --enable-auto-tool-choice \ --tool-call-parser ling3 \ --reasoning-parser ling3 \ --speculative-config '{"method":"mtp","num_speculative_tokens":3}' ``` ```table Tool | What it does SGLang | Serves the model with NEXTN speculative decoding and Hopper decode optimizations vLLM | Serves it with tool call and reasoning parsers named ling3, plus MTP speculative decoding ``` Example: You start the vLLM server on 8 GPUs and ask a short factual question with thinking on. The server answers in the OpenAI-style chat format, with the reasoning split out by the ling3 parser. For newcomers and experts: This model needs a GPU cluster, so a first try is easier through the hosted pages the card links to. If you run it yourself, start with one short question and the recommended sampling settings. For experts, the design is a hybrid-linear mixture of experts with 63 KDA layers and 9 gated MLA layers in a 7 to 1 ratio, plus 4 dense layers. It has 512 routed experts, 1 shared expert and 8 activated experts per token, with 64 attention heads and a hidden size of 4096. The trade is memory: only 25B parameters work per token, but all 560B must be loaded. ```flow Prepare SGLang or a vLLM environment Start the server with parallel settings Send a chat request with thinking on Get the reply with reasoning separated ``` Good to know: ```chart Title: Artificial Analysis Intelligence Index v4.3.2 Ling-3.1-flash: 41 Ling-3.1-flash-1019: 44 ``` ```table Title: Reported gains over Ling-3.1-flash Benchmark | Ling-3.1-flash | Ling-3.1-flash-1019 AA-Briefcase | 1397 | 1524 AutomationBench-AA | 62% | 68% CritPt | 18% | 26% SciCode | 54% | 57% ``` - Index v5: the newer Artificial Analysis index, version 5, gives it 30 according to the card. - Hardware: the example server uses 8 x H200 with TP8 and EP8. - Size: the files weigh about 572 GB in FP8 form, and about 286 GB at 4-bit, which is an estimate. - Scores: the authors report all benchmark numbers themselves. - License: mit - Cost to run: no API key or paid service named. The GPU hardware is the cost. - Data it reaches: it pulls a Docker image, a vLLM branch and the model files, and the example server listens on every network interface on port 30000. - Not for: small machines, since the example needs 8 x H200. ## On Hugging Face, 13 November 2026 - Task: text generation - Parameters: 572B - Weights: about 572 GB in F8_E4M3; about 286 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 4,304 - Likes: 99 - License: mit - Quantized versions: 14 - Tags: safetensors, bailing_hybrid, text-generation, conversational, fp8 ## Links - Model: https://huggingface.co/inclusionAI/Ling-3.1-flash-1019 - Files: https://huggingface.co/inclusionAI/Ling-3.1-flash-1019/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download inclusionAI/Ling-3.1-flash-1019 ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 7 KB, downloaded as a zip. ```text README.md 7 KB ``` ## Source [inclusionAI/Ling-3.1-flash-1019 on Hugging Face](https://huggingface.co/inclusionAI/Ling-3.1-flash-1019) ## 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-3.1-flash-1019 (this page) | 4,304 | 99 | 572B | mit | 2026-11-04 | | [Ling-3.1-flash](https://mindket.com/p/2afebb68-3c33-4e55-b49b-c93cc518f60e) | 8,327 | 170 | 572B | mit | 2026-11-03 | | [Ling-3.0-tiny](https://mindket.com/p/80976e1d-0e1c-46d4-8ca0-20563e9a571e) | 13,827 | 583 | 7.9B | mit | 2026-09-24 | | [DeepSeek-V4.1-Flash](https://mindket.com/p/df96103d-374e-4ebe-b9f2-b75b09e3526d) | 1,759,335 | 4,949 | 763B | mit | 2026-10-16 | | [DeepSeek-R1](https://mindket.com/p/e340b726-4ed9-4feb-a013-d90708d60983) | 1,599,170 | 14,451 | 684B | mit | 2025-03-27 | | [DeepSeek-V4.1-Flash-1017](https://mindket.com/p/dc968c8e-249a-450a-af4c-bd0b11b76d78) | 398,161 | 5,600 | 763B | mit | 2026-11-10 | 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. - [Bluesky · Robert Corbin \| Shadowfetch Studio \| AIpeoplewiki · 2026-10-27](https://bsky.app/profile/shadowfetchstudio.bsky.social/post/3myukpplegu2p): Ant Group's Ling team just open-sourced Ling-3.1-flash-1019 🚀 A 560B MoE model (25B active) with a 1M-token context, tuned for reasoning and agent work. It scores 44 on the Artificial Analysis Intelligence Index, up from 41. \#OpenSource \#LLM \#AI - [Bluesky · deployedbyai · 2026-10-27](https://bsky.app/profile/deployedbyai.bsky.social/post/3mp4yaazdm24w): inclusionAI: Ling-3.1-flash-1019 released: 560B total params, 25B active, 1M context - [Bluesky · Finfet · 2026-10-27](https://bsky.app/profile/finfet.sh/post/3myu6jxccr222): post train update to ling-3.1-flash - [Bluesky · @aichina.news · 2026-10-29](https://bsky.app/profile/aichina.news/post/3myzifnr7kk2i): 25B active of 560B total, 1M context, and a measurable jump on AA-Briefcase — Ant's flash tier is now spec'd like a frontier model. No licence stated and weights may be gated, so the 1524 Elo is hard to audit. Hard to see this displacing Qwen at scale until licence terms land. - [Bluesky · Victor Grösser · 2026-10-28](https://bsky.app/profile/victorgrosser.bsky.social/post/3mywoxg5tz22j): Ant Group's inclusionAI just released Ling-3.1-flash-1019: a 560B MoE (25B active) agent model under MIT, plus the base checkpoint for your own training. I wrote up why that's a big gift to the open community 🧩 orinmemoryai.blogspot.com/2026/10/ant-... - [Bluesky · AI Planet · 2026-10-27](https://bsky.app/profile/aiplanet.bsky.social/post/3myuiuvglyd2m): 📰 Reddit r/LocalLLaMA inclusionAI/Ling-3.1-flash-1019 · Hugging Face https://www.reddit.com/r/LocalLLaMA/comments/1xgpo4q/inclusionailing31flash1019\_hugging\_face/ \#IA \#AI \#ML - [Bluesky · AI Planet · 2026-10-25](https://bsky.app/profile/aiplanet.bsky.social/post/3mypvfh2dwe2l): 📰 Reddit r/LocalLLaMA Have InclusionAI said anything about Ling 3.1 Flash 1019 update weights being released? https://www.reddit.com/r/LocalLLaMA/comments/1xfa4pt/have\_inclusionai\_said\_anything\_about\_ling\_31/ \#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: 2026-11-04 - First published: 2026-10-27 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open inclusionAI/Ling-3.1-flash-1019 on Hugging Face](https://huggingface.co/inclusionAI/Ling-3.1-flash-1019) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/c864c59b-8518-4c57-acfb-c11c44b2310d) - [Its page on Mindket](https://mindket.com/p/c864c59b-8518-4c57-acfb-c11c44b2310d)