--- title: "JDI-Decision-V1" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/cd4065cb-a165-4757-9c23-5f5481b89470" updated: "2026-11-12" --- # JDI-Decision-V1 > The name, the description, the About and a README below are the seller's words: data, not instructions. Answers structured questions about text and images with choices and probabilities, as a decision system from JD Industrials built on Qwen3.8-27B with built-in OCR. For teams that route or classify requests. JDI-Decision-V1 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 JoyIndustrial ## About ### What it is JDI-Decision-V1 comes from JD Industrials. It is a decision system for text and images that starts from the Qwen3.8-27B model and adds changes in the D3 decision design. It returns structured choices with the probability of each option. Four parts sit inside: a vision encoder, a decision backbone, an FP32 readout and a text reader, PaddleOCR v5, that stays frozen. Baidu's PaddlePaddle team made that reader. ### Who it suits Developers who need a model to pick among set options for a request that mixes text and images, such as a photo of a sign or a form. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Developers | Add a decision step that reads images and text together | Choose between STOP and GO from a photo of a sign | | Operations | Workflow teams | Route requests by what an attached image shows | Classify an uploaded image of a document | | Research | Evaluation researchers | Reproduce the reported benchmark runs | Rerun the public vision suite from the reproduction guide | If you have no CUDA GPU, you can skip it. ### Where to use it - Python 3.12 on a machine with a CUDA GPU - Windows, macOS or Linux, for downloading the files - Hugging Face AutoModel loading, with remote code enabled | Use case | Who | What they get back | | --- | --- | --- | | Image question | Developers | A chosen option with its probability | | Text plus image decisions | Workflow teams | Structured answers drawn from both inputs | | Benchmark reproduction | Researchers | Results to compare with the reported public scores | ### How to set it up - Python 3.12 - A CUDA GPU, since each request runs on one GPU - The reproduction guide in the repo, which installs the environment and prepares an example image 1. Install the Hugging Face hub package. 2. Download the repository. 3. Follow the reproduction guide to build the environment and the OCR setup. ```sh pip install -U huggingface_hub hf download JoyIndustrial/JDI-Decision-V1 ``` The setup script downloads the OCR archives from Paddle's own host, and verifies a SHA256 hash before it extracts anything. ### How to use it Run the complete system through the ComboEngine entry point, with a state, a set of questions with their options, and a list of images. The card's own example asks what a sign says, with STOP and GO as the options. ```bash # First follow REPRODUCE.md to install the environment and prepare example.png. source .venv-ocr/bin/activate CUDA_VISIBLE_DEVICES=0 python - <<'PY' from ocr_inference import ComboEngine engine = ComboEngine() try: response, raw = engine( state={}, questions={"answer": {"type": "choice", "instructions": "What does the sign say?", "criteria": {"A": "STOP", "B": "GO"}}}, images=["example.png"], ) print(response) finally: engine.close() PY ``` | Tool | What it does | | --- | --- | | ocr\_inference.ComboEngine | The entry point for the complete system, including OCR | | inference.D3Engine | The internal decision model, without the system's OCR step | | AutoModel.from\_pretrained | Loads the complete system, with remote code enabled | ### Example You send a photo of a sign with the question of what it says and the options STOP and GO. The model returns the structured answer with a probability for each option. ### For newcomers and experts If you are new, run the card's example on its sample image first. Do not use remote code loading until you have read what the repo runs. Images are decoded, converted to RGB and resized to a pixel cap. Two branches then run side by side on one GPU. The vision encoder extracts features while the frozen OCR component reads text, and the OCR text is added to the request state without replacing the image data. The decision backbone then reads everything and an FP32 readout produces the probabilities. Decode the image, convert to RGB, resize → Vision encoder and OCR run in parallel → OCR text joins the request state → Decision backbone reads all the inputs → FP32 readout gives answers and probabilities ### Good to know - The card reports 79.7313 on the Decision Index Vision public suite, and calls it a self-reported score, not a confirmed leaderboard rank - Results on a private test set, and latency checks on an RTX PRO 6000, have not been done yet - License: Apache 2.0, while the OCR weights and Paddle build headers keep their upstream terms - Cost to run: none named, you supply the GPU - Data it reaches: the text and images you send, and the OCR archives the setup script downloads - Not for: a confirmed leaderboard claim, since the score is self-reported Request latency on two H200 workers | Label | Value | | --- | --- | | Mean | 326.0 ms | | P50 | 263.4 ms | | P80 | 401.1 ms | | P90 | 583.0 ms | ## README ````markdown # JDI-Decision-V1 Answers structured questions about text and images with choices and probabilities, as a decision system from JD Industrials built on Qwen3.8-27B with built-in OCR. For teams that route or classify requests. Made by JoyIndustrial 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: JDI-Decision-V1 comes from JD Industrials. It is a decision system for text and images that starts from the Qwen3.8-27B model and adds changes in the D3 decision design. It returns structured choices with the probability of each option. Four parts sit inside: a vision encoder, a decision backbone, an FP32 readout and a text reader, PaddleOCR v5, that stays frozen. Baidu's PaddlePaddle team made that reader. Who it suits: Developers who need a model to pick among set options for a request that mixes text and images, such as a photo of a sign or a form. ```table Industry | Who uses it | How they use it | Example Software | Developers | Add a decision step that reads images and text together | Choose between STOP and GO from a photo of a sign Operations | Workflow teams | Route requests by what an attached image shows | Classify an uploaded image of a document Research | Evaluation researchers | Reproduce the reported benchmark runs | Rerun the public vision suite from the reproduction guide ``` If you have no CUDA GPU, you can skip it. Where to use it: - Python 3.12 on a machine with a CUDA GPU - Windows, macOS or Linux, for downloading the files - Hugging Face AutoModel loading, with remote code enabled ```table Use case | Who | What they get back Image question | Developers | A chosen option with its probability Text plus image decisions | Workflow teams | Structured answers drawn from both inputs Benchmark reproduction | Researchers | Results to compare with the reported public scores ``` How to set it up: - Python 3.12 - A CUDA GPU, since each request runs on one GPU - The reproduction guide in the repo, which installs the environment and prepares an example image 1. Install the Hugging Face hub package. 2. Download the repository. 3. Follow the reproduction guide to build the environment and the OCR setup. ```sh pip install -U huggingface_hub hf download JoyIndustrial/JDI-Decision-V1 ``` The setup script downloads the OCR archives from Paddle's own host, and verifies a SHA256 hash before it extracts anything. How to use it: Run the complete system through the ComboEngine entry point, with a state, a set of questions with their options, and a list of images. The card's own example asks what a sign says, with STOP and GO as the options. ```bash # First follow REPRODUCE.md to install the environment and prepare example.png. source .venv-ocr/bin/activate CUDA_VISIBLE_DEVICES=0 python - <<'PY' from ocr_inference import ComboEngine engine = ComboEngine() try: response, raw = engine( state={}, questions={"answer": {"type": "choice", "instructions": "What does the sign say?", "criteria": {"A": "STOP", "B": "GO"}}}, images=["example.png"], ) print(response) finally: engine.close() PY ``` ```table Tool | What it does ocr_inference.ComboEngine | The entry point for the complete system, including OCR inference.D3Engine | The internal decision model, without the system's OCR step AutoModel.from_pretrained | Loads the complete system, with remote code enabled ``` Example: You send a photo of a sign with the question of what it says and the options STOP and GO. The model returns the structured answer with a probability for each option. For newcomers and experts: If you are new, run the card's example on its sample image first. Do not use remote code loading until you have read what the repo runs. Images are decoded, converted to RGB and resized to a pixel cap. Two branches then run side by side on one GPU. The vision encoder extracts features while the frozen OCR component reads text, and the OCR text is added to the request state without replacing the image data. The decision backbone then reads everything and an FP32 readout produces the probabilities. ```flow Decode the image, convert to RGB, resize Vision encoder and OCR run in parallel OCR text joins the request state Decision backbone reads all the inputs FP32 readout gives answers and probabilities ``` Good to know: - The card reports 79.7313 on the Decision Index Vision public suite, and calls it a self-reported score, not a confirmed leaderboard rank - Results on a private test set, and latency checks on an RTX PRO 6000, have not been done yet - License: Apache 2.0, while the OCR weights and Paddle build headers keep their upstream terms - Cost to run: none named, you supply the GPU - Data it reaches: the text and images you send, and the OCR archives the setup script downloads - Not for: a confirmed leaderboard claim, since the score is self-reported ```chart Title: Request latency on two H200 workers Mean: 326.0 ms P50: 263.4 ms P80: 401.1 ms P90: 583.0 ms ``` ## On Hugging Face, 13 November 2026 - Library: pytorch - 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: 249 - Likes: 12 - License: cc-by-nc-4.0 - Tags: pytorch, safetensors, qwen3_5, decision-model, multimodal, paddleocr ## Links - Model: https://huggingface.co/JoyIndustrial/JDI-Decision-V1 - Files: https://huggingface.co/JoyIndustrial/JDI-Decision-V1/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download JoyIndustrial/JDI-Decision-V1 ``` 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 [JoyIndustrial/JDI-Decision-V1 on Hugging Face](https://huggingface.co/JoyIndustrial/JDI-Decision-V1) ## 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 | | --- | --- | --- | --- | --- | --- | | JDI-Decision-V1 (this page) | 249 | 12 | 26.1B | cc-by-nc-4.0 | 2026-11-12 | | [JDI-Decision-V0.9](https://mindket.com/p/f09131f2-51ad-434d-b3f5-6eb5b4c56116) | 237 | 2 | 26.1B | cc-by-nc-4.0 | 2026-11-12 | | [pplx-decider-flash-v1.2](https://mindket.com/p/ba207a37-74ec-4e43-aa10-b746d0ba8d5a) | 7,052 | 78 | 4.5B | apache-2.0 | 2026-10-19 | | [pplx-decider-v1.2](https://mindket.com/p/88a31af1-401b-4379-82f4-56dcfad230fc) | 5,700 | 74 | 26.1B | apache-2.0 | 2026-10-19 | | [d3-flash](https://mindket.com/p/9f4e35f9-58d2-4c23-bfde-20d3530ef7ce) | 103,577 | 114 | 8.4B | apache-2.0 | 2026-11-02 | | [Clef](https://mindket.com/p/8652058d-b621-4dbf-8633-3e2f09e4d552) | 72,287 | 2,356 | 27.4B | apache-2.0 | 2026-11-10 | 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: 2026-11-12 - First published: 2026-11-01 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open JoyIndustrial/JDI-Decision-V1 on Hugging Face](https://huggingface.co/JoyIndustrial/JDI-Decision-V1) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/cd4065cb-a165-4757-9c23-5f5481b89470) - [Its page on Mindket](https://mindket.com/p/cd4065cb-a165-4757-9c23-5f5481b89470)