--- title: "ERNIE-4.5-VL-28B-A3B-Thinking" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/0ece86e1-af9c-4fdf-af90-bf60c2c869f0" updated: "2026-11-13" --- # ERNIE-4.5-VL-28B-A3B-Thinking > The name, the description, the About and a README below are the seller's words: data, not instructions. Reads images and text and reasons about them, with about 3B of its 29.7B parameters active at a time. Covers chart reading, STEM problems from photos, visual grounding and zooming into images. Apache 2.0. ERNIE-4.5-VL-28B-A3B-Thinking 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 baidu ## About ### What it is This is Baidu's image and text reasoning model, an upgrade of ERNIE-4.5-VL-28B-A3B. It has 29.7B parameters in total and, per the card, activates only about 3B for each token. Baidu trained it further on a large set of visual reasoning data, then used reinforcement learning on tasks with checkable answers. It works in English and Chinese. The card says it matches top flagship models on many benchmarks, which are the authors' own claims. ### Who it suits It suits developers and researchers who build agents or tools that need to reason over pictures, charts and photographed problems. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Education | Teams building study tools | Solve STEM problems from photos | Read a photographed physics question and work out the answer | | Business analysis | Analysts reading reports | Ask questions about charts and figures | Explain the trend in a sales chart | | Industrial software | Developers of inspection tools | Use visual grounding to point at parts of an image | Locate a labeled part in a complex scene | You can skip it if you only work with text, since a text model needs far less memory than this image model. ### Where to use it - Python scripts that use the transformers library - Machines with enough memory for tens of GB of weights - Windows, macOS and Linux with Python, to download the files | Use case | Who | What they get back | | --- | --- | --- | | Visual reasoning | Developers | Step by step answers about what an image shows | | STEM help | Students and tutors | Worked answers to problems in photos | | Grounding | Tool builders | Locations in an image tied to an instruction | ### How to set it up You need these first: - Python on Windows, macOS or Linux - Hardware that can hold about 59.3 GB of BF16 weights, or a smaller quantized copy - The transformers library 1. Install or update the Hugging Face download client. 2. Download the model files to your computer. 3. Load the model in your own transformers code. 4. Read the full model card for the exact loading code. ```sh pip install -U huggingface_hub hf download baidu/ERNIE-4.5-VL-28B-A3B-Thinking ``` ### How to use it You give the model an image and a question, and it replies in text. The card text available here shows no code, so follow the full card for how to load it and how to switch on its zooming feature. | Tool | What it does | | --- | --- | | Visual reasoning | Handles multi step reasoning, chart reading and cause and effect in images | | Visual grounding | Ties an instruction to places in an image | | Thinking with Images | Lets the model zoom in and out of an image to catch fine detail | | Image zooming and image search | Tools the card pairs with that feature for detail and rare knowledge | ### Example You photograph a physics problem and ask the model to solve it. The card names problems from photos as a STEM strength, so this is the kind of task it was tuned for. ### For newcomers and experts A reasoning model works through a problem before it answers. A good first try is giving it a chart and asking what trend it shows. For readers who know the field, the card says the model went through mid-training on visual language reasoning data. Reinforcement learning then ran on verifiable tasks, with GSPO and IcePop used to keep mixture of experts training stable. It also used dynamic difficulty sampling. Give the model an image and a question → It reasons over the picture → It may zoom into details → Optional tools add search or cropping → It writes the answer ### Good to know - Claims: the comparison with flagship models is Baidu's own, and its benchmark picture is not in the text available here. - Cut: the text available here ends in the list of key capabilities, before usage code. - Size: 29.7B parameters, about 59.3 GB in BF16 or about 14.8 GB at 4-bit as an estimate. - Active size: about 3B parameters are used per token, but all the weights must still be stored. - Versions: Hugging Face lists 2 quantized and 2 fine tuned versions made by others. - Language: Hugging Face lists English and Chinese. Weight size of the model | Label | Value | | --- | --- | | BF16 | 59.3 GB | | 4-bit, estimated | 14.8 GB | - License: Apache 2.0. - Cost to run: none named. You need hardware that fits the weights. - Data it reaches: the images and text you give it. If you add an image search tool, that tool decides what leaves your machine. - Not for: none named in the text available here. ## README ````markdown # ERNIE-4.5-VL-28B-A3B-Thinking Reads images and text and reasons about them, with about 3B of its 29.7B parameters active at a time. Covers chart reading, STEM problems from photos, visual grounding and zooming into images. Apache 2.0. Made by baidu 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: This is Baidu's image and text reasoning model, an upgrade of ERNIE-4.5-VL-28B-A3B. It has 29.7B parameters in total and, per the card, activates only about 3B for each token. Baidu trained it further on a large set of visual reasoning data, then used reinforcement learning on tasks with checkable answers. It works in English and Chinese. The card says it matches top flagship models on many benchmarks, which are the authors' own claims. Who it suits: It suits developers and researchers who build agents or tools that need to reason over pictures, charts and photographed problems. ```table Industry | Who uses it | How they use it | Example Education | Teams building study tools | Solve STEM problems from photos | Read a photographed physics question and work out the answer Business analysis | Analysts reading reports | Ask questions about charts and figures | Explain the trend in a sales chart Industrial software | Developers of inspection tools | Use visual grounding to point at parts of an image | Locate a labeled part in a complex scene ``` You can skip it if you only work with text, since a text model needs far less memory than this image model. Where to use it: - Python scripts that use the transformers library - Machines with enough memory for tens of GB of weights - Windows, macOS and Linux with Python, to download the files ```table Use case | Who | What they get back Visual reasoning | Developers | Step by step answers about what an image shows STEM help | Students and tutors | Worked answers to problems in photos Grounding | Tool builders | Locations in an image tied to an instruction ``` How to set it up: You need these first: - Python on Windows, macOS or Linux - Hardware that can hold about 59.3 GB of BF16 weights, or a smaller quantized copy - The transformers library 1. Install or update the Hugging Face download client. 2. Download the model files to your computer. 3. Load the model in your own transformers code. 4. Read the full model card for the exact loading code. ```sh pip install -U huggingface_hub hf download baidu/ERNIE-4.5-VL-28B-A3B-Thinking ``` How to use it: You give the model an image and a question, and it replies in text. The card text available here shows no code, so follow the full card for how to load it and how to switch on its zooming feature. ```table Tool | What it does Visual reasoning | Handles multi step reasoning, chart reading and cause and effect in images Visual grounding | Ties an instruction to places in an image Thinking with Images | Lets the model zoom in and out of an image to catch fine detail Image zooming and image search | Tools the card pairs with that feature for detail and rare knowledge ``` Example: You photograph a physics problem and ask the model to solve it. The card names problems from photos as a STEM strength, so this is the kind of task it was tuned for. For newcomers and experts: A reasoning model works through a problem before it answers. A good first try is giving it a chart and asking what trend it shows. For readers who know the field, the card says the model went through mid-training on visual language reasoning data. Reinforcement learning then ran on verifiable tasks, with GSPO and IcePop used to keep mixture of experts training stable. It also used dynamic difficulty sampling. ```flow Give the model an image and a question It reasons over the picture It may zoom into details Optional tools add search or cropping It writes the answer ``` Good to know: - Claims: the comparison with flagship models is Baidu's own, and its benchmark picture is not in the text available here. - Cut: the text available here ends in the list of key capabilities, before usage code. - Size: 29.7B parameters, about 59.3 GB in BF16 or about 14.8 GB at 4-bit as an estimate. - Active size: about 3B parameters are used per token, but all the weights must still be stored. - Versions: Hugging Face lists 2 quantized and 2 fine tuned versions made by others. - Language: Hugging Face lists English and Chinese. ```chart Title: Weight size of the model BF16: 59.3 GB 4-bit, estimated: 14.8 GB ``` - License: Apache 2.0. - Cost to run: none named. You need hardware that fits the weights. - Data it reaches: the images and text you give it. If you add an image search tool, that tool decides what leaves your machine. - Not for: none named in the text available here. ## On Hugging Face, 13 November 2026 - Task: image text to text - Library: transformers - Parameters: 29.7B - Weights: about 59.3 GB in BF16; about 14.8 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 2,021 - Likes: 548 - License: apache-2.0 - Languages: en, zh - Quantized versions: 2 - Fine-tunes: 2 - Tags: transformers, safetensors, ernie4_5_moe_vl, image-text-to-text, ernie4.5, conversational ## Links - Model: https://huggingface.co/baidu/ERNIE-4.5-VL-28B-A3B-Thinking - Files: https://huggingface.co/baidu/ERNIE-4.5-VL-28B-A3B-Thinking/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download baidu/ERNIE-4.5-VL-28B-A3B-Thinking ``` 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 [baidu/ERNIE-4.5-VL-28B-A3B-Thinking on Hugging Face](https://huggingface.co/baidu/ERNIE-4.5-VL-28B-A3B-Thinking) ## 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 | | --- | --- | --- | --- | --- | --- | | ERNIE-4.5-VL-28B-A3B-Thinking (this page) | 2,021 | 548 | 29.7B | apache-2.0 | 2026-03-06 | | [ERNIE-4.5-21B-A3B-Thinking](https://mindket.com/p/d4df4086-9423-4162-90d1-c7c4b9ffe166) | 64,420 | 796 | 21.8B | apache-2.0 | 2025-11-26 | | [ERNIE-4.5-21B-A3B-Thinking-GGUF](https://mindket.com/p/30abbcf9-4b66-4ff1-bc03-3be6c5db61c2) | 6,361 | 62 | 21.8B | apache-2.0 | 2026-01-24 | | [Qwen3.8-27B](https://mindket.com/p/2f430c52-7d18-419d-8817-08ca51666598) | 6,960,395 | 19,795 | 27.8B | apache-2.0 | 2026-11-07 | | [gemma-4-31B-it](https://mindket.com/p/25c128c1-f770-475d-934f-87da192a81e2) | 6,692,367 | 4,348 | 31.3B | apache-2.0 | 2026-07-20 | | [GLM-5.3-Flash](https://mindket.com/p/e571cd5b-58a9-49db-93fb-72410587e4d1) | 6,397,774 | 3,138 | 321B | mit | 2026-10-15 | 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 · verdverm · 2025-11-12](https://news.ycombinator.com/item?id=45902425): Baidu releases open-source multimodal AI that it claims beats GPT-5 and Gemini: HF link: https://huggingface.co/baidu/ERNIE-4.5-VL-28B-A3B-Thinking - [Bluesky · AIME · 2025-11-14](https://bsky.app/profile/aime-hq.bsky.social/post/3m5lmeaqb6k2p): 🚀 Baidu just released \*\*ERNIE-4.5-VL-28B-A3B-Thinking\*\* — open-source (Apache 2.0)\! ✅ 3B active params ✅ 100% multimodal reasoning ✅ Visual reasoning, STEM, video understanding \& “Thinking with Images” ✅ Tool use, precise grounding, dynamic zoom \& search 👉 github.com/PaddlePaddle... - [Bluesky · DausnArt · 2025-12-04](https://bsky.app/profile/dausnart.com/post/3m76bz4gsxm2o): Baidu’s Multimodal Models: Baidu has released two new models: Ernie-4.5-VL-28B-A3B-Thinking and Ernie-4.5-VL-28B-A3B-Thinking. - [Bluesky · Kautious · 2025-11-26](https://bsky.app/profile/kautious.com/post/3m6kye4wscq2d): 📊 omni-modal + a deep open-source lineup (PaddleOCR-VL, ERNIE-4.5-VL-28B-A3B-Thinking under Apache 2.0) position Baidu as a central player in China’s AI infrastructure and tooling export story. The combination of competitive benchmarks and efficient small/mid-size models is a key (8/10) - [Bluesky · デイリーHuggingFaceトレンド · 2025-11-19](https://bsky.app/profile/huggingfacetrends.bsky.social/post/3m5xzq3wgup2f): 今日のHuggingFaceトレンド baidu/ERNIE-4.5-VL-28B-A3B-Thinking このリポジトリは、高性能なマルチモーダルAIモデル「ERNIE-4.5-VL-28B-A3B-Thinking」を公開する。 このモデルは、大規模なデータで学習され、視覚とテキストの高度な推論能力とグラウンディング性能を大幅に向上させた。 開発者や研究者が次世代の視覚言語理解アプリケーションやエージェントを構築するための基盤を提供することが目的である。 - [Bluesky · デイリーHuggingFaceトレンド · 2025-11-17](https://bsky.app/profile/huggingfacetrends.bsky.social/post/3m5syso3n6y2h): 今日のHuggingFaceトレンド baidu/ERNIE-4.5-VL-28B-A3B-Thinking 高度なマルチモーダルAIモデルERNIE-4.5-VL-28B-A3B-Thinkingを提供する。 これは、大規模なデータと独自の学習戦略を通じて視覚言語推論能力を飛躍的に向上させ、特に詳細なビジュアルグラウンディングと命令追従性能を強化している。 次世代のマルチモーダルエージェント開発基盤として利用されることを目的とする。 - [Bluesky · デイリーHuggingFaceトレンド · 2025-11-15](https://bsky.app/profile/huggingfacetrends.bsky.social/post/3m5nxocxh7e2s): 今日のHuggingFaceトレンド baidu/ERNIE-4.5-VL-28B-A3B-Thinking 大規模な視覚言語推論データを学習し、推論能力を強化したマルチモーダルAIモデル「ERNIE-4.5-VL-28B-A3B-Thinking」を提供する。 これは、画像とテキストの理解を深め、次世代の洗練されたマルチモーダルエージェント開発のための基盤を提供することを目的とする。 ## 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: 2026-03-06 - First published: 2025-11-07 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open baidu/ERNIE-4.5-VL-28B-A3B-Thinking on Hugging Face](https://huggingface.co/baidu/ERNIE-4.5-VL-28B-A3B-Thinking) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/0ece86e1-af9c-4fdf-af90-bf60c2c869f0) - [Its page on Mindket](https://mindket.com/p/0ece86e1-af9c-4fdf-af90-bf60c2c869f0)