--- title: "d3-ultra" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/1e92f78a-2d4d-4abb-99b4-e7c0449b9fdc" updated: "2026-11-12" --- # d3-ultra > The name, the description, the About and a README below are the seller's words: data, not instructions. Answers many questions about one text, image or video input at once, with a probability for each answer and no generated text. A 27B multimodal decision model from vLLM Semantic Router. d3-ultra 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 vllm-sr ## About ### What it is d3-ultra is a multimodal decision model from vllm-sr, part of the decision models used by vLLM Semantic Router. You send text or JSON, and may add pictures or video clips, together with your questions. The model returns a probability for each answer instead of writing text. ### Who it suits Developers who build routers, filters or review steps that need quick, scored decisions about text, pictures or video clips. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Platform engineers | Route or label requests by asking several questions of one input | Is this message urgent, yes or no | | Media | Content reviewers | Ask where something appears in an image or a video | Find a cat in a painting | | Research | ML researchers | Study a decision model that returns probabilities | Compare options with scores | | Analytics | Data teams | Rate items on a scale from one shared read | Score a photo on several traits | If you want free-form writing or chat replies, you can skip it, since it does not generate text. ### Where to use it - Windows, macOS or Linux with Python, for downloading the files - Python programs that use the transformers library - The free hosted API and the official Space the card points to | Use case | Who | What they get back | | --- | --- | --- | | Choice | Developers | The probability of each option you list | | Yes or no | Reviewers | A probability for each yes or no question | | Box | Image and video teams | One box and a confidence that the target is there | | Score | Analysts | A rating on a scale for the input | ### How to set it up - Python, to run the Hugging Face download tool - About 52.2 GB of disk for the BF16 weights, or about 13 GB at 4-bit as an estimate - A GPU able to serve a 27B model, since the card quotes one AMD Instinct MI325X 1. Install the Hugging Face hub tools with pip. 2. Download the model files with the hf command. 3. Follow the card's quickstart to send your first request. ```bash pip install -U huggingface_hub hf download vllm-sr/d3-ultra ``` ### How to use it You send one request holding the input and your questions. The card lists five decision types, so each question is a choice, a yes or no, a score, a box in an image or video moment, or a track over a whole video. | Tool | What it does | | --- | --- | | Choice | Picks one of several options you give | | Yes / No | Returns a probability for a yes or no question | | Score | Rates the input on a scale | | Box | Points to a place in an image or a video moment | | Track | Follows a target over a whole video | ### Example You send one painting and ask ten questions about it in a single request. The card shows this with Seurat's A Sunday on La Grande Jatte. Every option comes back with its probability, and the painting is read only once. ### For newcomers and experts Start with the free hosted API the card mentions, or download the files and run the card's quickstart. Ask one yes or no question about a picture first, and read the probability it returns. The model has 26.09B parameters, including a 0.46B vision encoder, and takes up to 131,072 tokens per question. A single read of the input serves every question. In the card's median timing on one photo, between 1 and 255 questions took 304 ms up to 2.16 s. Send text, images or video → Add your questions → Model reads the input once → Every question gets answered → Probabilities come back ### Good to know - License: Apache 2.0 - Cost to run: none named, though a free hosted API exists and you need your own hardware to run the files - Data it reaches: whatever text, JSON, images and videos you send, and Hugging Face for the download - Not for: writing or chatting, because it returns probabilities and does not generate text ## README ````markdown # d3-ultra Answers many questions about one text, image or video input at once, with a probability for each answer and no generated text. A 27B multimodal decision model from vLLM Semantic Router. Made by vllm-sr 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: d3-ultra is a multimodal decision model from vllm-sr, part of the decision models used by vLLM Semantic Router. You send text or JSON, and may add pictures or video clips, together with your questions. The model returns a probability for each answer instead of writing text. Who it suits: Developers who build routers, filters or review steps that need quick, scored decisions about text, pictures or video clips. ```table Industry | Who uses it | How they use it | Example Software | Platform engineers | Route or label requests by asking several questions of one input | Is this message urgent, yes or no Media | Content reviewers | Ask where something appears in an image or a video | Find a cat in a painting Research | ML researchers | Study a decision model that returns probabilities | Compare options with scores Analytics | Data teams | Rate items on a scale from one shared read | Score a photo on several traits ``` If you want free-form writing or chat replies, you can skip it, since it does not generate text. Where to use it: - Windows, macOS or Linux with Python, for downloading the files - Python programs that use the transformers library - The free hosted API and the official Space the card points to ```table Use case | Who | What they get back Choice | Developers | The probability of each option you list Yes or no | Reviewers | A probability for each yes or no question Box | Image and video teams | One box and a confidence that the target is there Score | Analysts | A rating on a scale for the input ``` How to set it up: - Python, to run the Hugging Face download tool - About 52.2 GB of disk for the BF16 weights, or about 13 GB at 4-bit as an estimate - A GPU able to serve a 27B model, since the card quotes one AMD Instinct MI325X 1. Install the Hugging Face hub tools with pip. 2. Download the model files with the hf command. 3. Follow the card's quickstart to send your first request. ```bash pip install -U huggingface_hub hf download vllm-sr/d3-ultra ``` How to use it: You send one request holding the input and your questions. The card lists five decision types, so each question is a choice, a yes or no, a score, a box in an image or video moment, or a track over a whole video. ```table Tool | What it does Choice | Picks one of several options you give Yes / No | Returns a probability for a yes or no question Score | Rates the input on a scale Box | Points to a place in an image or a video moment Track | Follows a target over a whole video ``` Example: You send one painting and ask ten questions about it in a single request. The card shows this with Seurat's A Sunday on La Grande Jatte. Every option comes back with its probability, and the painting is read only once. For newcomers and experts: Start with the free hosted API the card mentions, or download the files and run the card's quickstart. Ask one yes or no question about a picture first, and read the probability it returns. The model has 26.09B parameters, including a 0.46B vision encoder, and takes up to 131,072 tokens per question. A single read of the input serves every question. In the card's median timing on one photo, between 1 and 255 questions took 304 ms up to 2.16 s. ```flow Send text, images or video Add your questions Model reads the input once Every question gets answered Probabilities come back ``` Good to know: - License: Apache 2.0 - Cost to run: none named, though a free hosted API exists and you need your own hardware to run the files - Data it reaches: whatever text, JSON, images and videos you send, and Hugging Face for the download - Not for: writing or chatting, because it returns probabilities and does not generate text ## On Hugging Face, 13 November 2026 - Task: zero shot classification - Library: transformers - Parameters: 26.1B - Weights: about 52.2 GB in BF16; about 13 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 2,398 - Likes: 61 - License: apache-2.0 - Quantized versions: 8 - Fine-tunes: 1 - Tags: transformers, safetensors, qwen3_5, feature-extraction, zero-shot-classification, decision-model, classification, system-one, multimodal, vision, video, object-detection ## Links - Model: https://huggingface.co/vllm-sr/d3-ultra - Files: https://huggingface.co/vllm-sr/d3-ultra/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download vllm-sr/d3-ultra ``` 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 [vllm-sr/d3-ultra on Hugging Face](https://huggingface.co/vllm-sr/d3-ultra) ## 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 | | --- | --- | --- | --- | --- | --- | | d3-ultra (this page) | 2,398 | 61 | 26.1B | apache-2.0 | 2026-11-02 | | [d3-turbo](https://mindket.com/p/98d80d31-de45-4b9b-9ad7-ba14820f8211) | 4,186 | 133 | 26.1B | apache-2.0 | 2026-11-02 | | [d3-flash](https://mindket.com/p/9f4e35f9-58d2-4c23-bfde-20d3530ef7ce) | 103,577 | 114 | 8.4B | apache-2.0 | 2026-11-02 | | [d3-mini](https://mindket.com/p/4e08c56c-92f6-4e07-8884-2cd16598fc8b) | 53,115 | 75 | 4.5B | apache-2.0 | 2026-10-30 | | [d3](https://mindket.com/p/df1392ef-31ed-47ea-9dd4-d4148bdc8625) | 10,037 | 293 | 26.1B | apache-2.0 | 2026-11-02 | | [d3-nano](https://mindket.com/p/28987981-5b6c-44a7-86e5-287f8ec4336b) | 7,779 | 49 | 2.2B | apache-2.0 | 2026-11-02 | 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 · input · 2026-10-29](https://bsky.app/profile/feed.igeek.gamer-geek-news.com.ap.brid.gy/post/3myyqf3xmcj72): 📰 \*\*vLLM Semantic Router Releases d3-ultra: A 27B Open Multimodal Decision Model\*\* vLLM Semantic Router released d3-ultra, a 27B open-weight multimodal decision model. It reads text, images and video once, then returns probabilities for every answer, scoring 87.2 on the team’s SR... 📰 Source … - [Bluesky · gen-ai-news · 2026-10-28](https://bsky.app/profile/gen-ai.news/post/3mywqxtesph2a): d3-ultra is a 27B open-weight model that processes text, image, and video together in one pass, outputting probability distributions instead of single answers. It scored 87.2 on the SR-Text benchmark. Does open-weight multimodal routing make specialized routing infrastructure ... - [Bluesky · input · 2026-10-27](https://bsky.app/profile/feed.igeek.gamer-geek-news.com.ap.brid.gy/post/3mytoyughc7e2): 📰 \*\*vLLM Semantic Router Releases d3-ultra: A 27B Open Multimodal Decision Model\*\* vLLM Semantic Router released d3-ultra, a 27B open-weight multimodal decision model. It reads text, images and video once, then returns probabilities for every answer, scoring 87.2 on the team’s SR... 📰 Source … ## 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: 2026-11-02 - First published: 2026-10-24 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open vllm-sr/d3-ultra on Hugging Face](https://huggingface.co/vllm-sr/d3-ultra) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/1e92f78a-2d4d-4abb-99b4-e7c0449b9fdc) - [Its page on Mindket](https://mindket.com/p/1e92f78a-2d4d-4abb-99b4-e7c0449b9fdc)