--- title: "d3-edge" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/33cf2059-d4c7-48c1-ae2e-04d63a9ada68" updated: "2026-11-13" --- # d3-edge > The name, the description, the About and a README below are the seller's words: data, not instructions. Answers choice, yes-or-no and score questions about text, images or video in one call, returning a probability for each answer. A small decision model for routing and triage engineers. d3-edge 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 vllm-sr ## About ### What it is d3-edge is a small decision model of about 0.59B parameters, including a 0.10B vision encoder, built on Qwen3.5-0.8B and published by vllm-sr as part of Decision 3.0 for vLLM Semantic Router. You feed it text or JSON, plus pictures or clips if you have them, along with the questions it should answer. It does not write text: it returns a probability for each possible answer, all in one call. ### Who it suits It suits engineers who build routing, moderation or triage steps and want one fast model to classify an input, and teams that already run vLLM Semantic Router. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Customer support | Support teams | Route a message to the right team and rate how urgent it is | Choose returns, billing or technical for a request | | Retail | Operations teams | Check an uploaded photo against a question | Ask whether a receipt image lists a product | | Platform engineering | Routing engineers | Add a decision step to a model router | Score a prompt before choosing which model gets it | Anyone who needs a model that writes text or chats can skip it, since it only scores answers. ### Where to use it - Python with Transformers, loading the model by its name - vLLM Semantic Router, whose decision models it belongs to - Hugging Face, where an official Space offers a free try - Windows, macOS or Linux, where the files download with Python | Use case | Who | What they get back | | --- | --- | --- | | Route a request | Support teams | A probability for each team option | | Yes or no check | Developers | A probability for yes and for no | | Rate an input | Analysts | A probability for each point on a scale | ### How to set it up - Python with pip - Transformers 5.17.0 plus torch, torchvision, pillow, opencv-python-headless, safetensors and accelerate - Optionally flash-linear-attention, for faster GPU kernels in the linear-attention layers - A GPU for good speed, with room for about 1.2 GB of BF16 weights 1. Install the packages with pip. 2. Optionally add flash-linear-attention. 3. Load the model with Transformers, allowing its remote code. 4. Call the model with your input and your questions. ```bash pip install "transformers==5.17.0" torch torchvision pillow opencv-python-headless safetensors accelerate pip install flash-linear-attention # optional: fast GPU kernels for the linear-attention layers ``` ```sh pip install -U huggingface_hub hf download vllm-sr/d3-edge ``` ### How to use it Load the model, then call its system\_one method with your input as the state and a set of questions. Each question has a type, its instructions and, for choices and scores, the criteria to pick from. The answers come back as probabilities. ```python from transformers import AutoModel model = AutoModel.from_pretrained("vllm-sr/d3-edge", trust_remote_code=True) ``` | Question type | What it answers | | --- | --- | | Choice | Picks among the options you list | | Yes / No | Says yes or no to a question | | Score | Rates the input on a scale you define | ### Example You send a support message about a cracked blender with a photo of the receipt. You ask which team should handle it, whether the receipt lists the item and how urgent it is. The model returns a probability for each team, for yes and no, and for each urgency level. ### For newcomers and experts Zero-shot classification means you name the categories when you ask, instead of training a classifier first. Start with a text-only example, then add an image. Questions about one input are answered in a single call, though each runs its own forward pass over the input. Images can be PNG, JPEG or WebP, and videos MP4, WebM, MOV or MKV, read at 2 frames per second. With vLLM Semantic Router, the card measured 861 requests per second, roughly 6.1 times plain AutoModel, using a single AMD Instinct MI325X GPU. Give text, JSON, images or video → Add your questions → One call runs the model → A probability comes back for each answer → Your app acts on the likeliest one Median response time in milliseconds | Label | Value | | --- | --- | | Text request | 6.6 ms | | Request with an image | 58.6 ms | | Request with a 10-second video | 274 ms | Jev Decision Index on the official text board | Label | Value | | --- | --- | | Decision 2.0 0.6B | 13.6 | | d3-edge | 20.2 | ### Good to know - Size: about 0.59B parameters, which the record puts at about 1.2 GB in BF16 - Scores: 20.2 on the Jev Decision Index 0.3.2 text board, second among entries of similar size, and 46.6 on a video test from the authors' own evaluation - Speed: all timings were taken on a single AMD Instinct MI325X GPU, handling requests individually - Remote code: the example loads the model with trust\_remote\_code, which runs code from the model repository - License: Apache 2.0 - Cost to run: none named - Data it reaches: the text, images and videos you pass in, while pip fetches packages from the web - Not for: writing or chatting, since it returns probabilities and not text ## README ````markdown # d3-edge Answers choice, yes-or-no and score questions about text, images or video in one call, returning a probability for each answer. A small decision model for routing and triage engineers. 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-edge is a small decision model of about 0.59B parameters, including a 0.10B vision encoder, built on Qwen3.5-0.8B and published by vllm-sr as part of Decision 3.0 for vLLM Semantic Router. You feed it text or JSON, plus pictures or clips if you have them, along with the questions it should answer. It does not write text: it returns a probability for each possible answer, all in one call. Who it suits: It suits engineers who build routing, moderation or triage steps and want one fast model to classify an input, and teams that already run vLLM Semantic Router. ```table Industry | Who uses it | How they use it | Example Customer support | Support teams | Route a message to the right team and rate how urgent it is | Choose returns, billing or technical for a request Retail | Operations teams | Check an uploaded photo against a question | Ask whether a receipt image lists a product Platform engineering | Routing engineers | Add a decision step to a model router | Score a prompt before choosing which model gets it ``` Anyone who needs a model that writes text or chats can skip it, since it only scores answers. Where to use it: - Python with Transformers, loading the model by its name - vLLM Semantic Router, whose decision models it belongs to - Hugging Face, where an official Space offers a free try - Windows, macOS or Linux, where the files download with Python ```table Use case | Who | What they get back Route a request | Support teams | A probability for each team option Yes or no check | Developers | A probability for yes and for no Rate an input | Analysts | A probability for each point on a scale ``` How to set it up: - Python with pip - Transformers 5.17.0 plus torch, torchvision, pillow, opencv-python-headless, safetensors and accelerate - Optionally flash-linear-attention, for faster GPU kernels in the linear-attention layers - A GPU for good speed, with room for about 1.2 GB of BF16 weights 1. Install the packages with pip. 2. Optionally add flash-linear-attention. 3. Load the model with Transformers, allowing its remote code. 4. Call the model with your input and your questions. ```bash pip install "transformers==5.17.0" torch torchvision pillow opencv-python-headless safetensors accelerate pip install flash-linear-attention # optional: fast GPU kernels for the linear-attention layers ``` ```sh pip install -U huggingface_hub hf download vllm-sr/d3-edge ``` How to use it: Load the model, then call its system_one method with your input as the state and a set of questions. Each question has a type, its instructions and, for choices and scores, the criteria to pick from. The answers come back as probabilities. ```python from transformers import AutoModel model = AutoModel.from_pretrained("vllm-sr/d3-edge", trust_remote_code=True) ``` ```table Question type | What it answers Choice | Picks among the options you list Yes / No | Says yes or no to a question Score | Rates the input on a scale you define ``` Example: You send a support message about a cracked blender with a photo of the receipt. You ask which team should handle it, whether the receipt lists the item and how urgent it is. The model returns a probability for each team, for yes and no, and for each urgency level. For newcomers and experts: Zero-shot classification means you name the categories when you ask, instead of training a classifier first. Start with a text-only example, then add an image. Questions about one input are answered in a single call, though each runs its own forward pass over the input. Images can be PNG, JPEG or WebP, and videos MP4, WebM, MOV or MKV, read at 2 frames per second. With vLLM Semantic Router, the card measured 861 requests per second, roughly 6.1 times plain AutoModel, using a single AMD Instinct MI325X GPU. ```flow Give text, JSON, images or video Add your questions One call runs the model A probability comes back for each answer Your app acts on the likeliest one ``` ```chart Title: Median response time in milliseconds Text request: 6.6 ms Request with an image: 58.6 ms Request with a 10-second video: 274 ms ``` ```chart Title: Jev Decision Index on the official text board Decision 2.0 0.6B: 13.6 d3-edge: 20.2 ``` Good to know: - Size: about 0.59B parameters, which the record puts at about 1.2 GB in BF16 - Scores: 20.2 on the Jev Decision Index 0.3.2 text board, second among entries of similar size, and 46.6 on a video test from the authors' own evaluation - Speed: all timings were taken on a single AMD Instinct MI325X GPU, handling requests individually - Remote code: the example loads the model with trust_remote_code, which runs code from the model repository - License: Apache 2.0 - Cost to run: none named - Data it reaches: the text, images and videos you pass in, while pip fetches packages from the web - Not for: writing or chatting, since it returns probabilities and not text ## On Hugging Face, 13 November 2026 - Task: zero shot classification - Library: transformers - Parameters: 594M - Weights: about 1.2 GB in BF16; about 297 MB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 5,937 - Likes: 59 - License: apache-2.0 - Based on: Qwen/Qwen3.5-0.8B - Quantized versions: 9 - Fine-tunes: 5 - Tags: transformers, safetensors, qwen3_5, feature-extraction, zero-shot-classification, decision-model, classification, system-one, multimodal, vision, video ## Links - Model: https://huggingface.co/vllm-sr/d3-edge - Files: https://huggingface.co/vllm-sr/d3-edge/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-edge ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 6 KB, downloaded as a zip. ```text README.md 6 KB ``` ## Source [vllm-sr/d3-edge on Hugging Face](https://huggingface.co/vllm-sr/d3-edge) ## 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-edge (this page) | 5,937 | 59 | 594M | 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 | | [d3-turbo](https://mindket.com/p/98d80d31-de45-4b9b-9ad7-ba14820f8211) | 4,186 | 133 | 26.1B | apache-2.0 | 2026-11-02 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## 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: 2026-10-10 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open vllm-sr/d3-edge on Hugging Face](https://huggingface.co/vllm-sr/d3-edge) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/33cf2059-d4c7-48c1-ae2e-04d63a9ada68) - [Its page on Mindket](https://mindket.com/p/33cf2059-d4c7-48c1-ae2e-04d63a9ada68)