--- title: "MiniCPM-V 4.6" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/99f4fc0e-2ce4-4a15-a13f-9a0d45246423" updated: "2026-11-13" --- # MiniCPM-V 4.6 > The name, the description, the About and a README below are the seller's words: data, not instructions. Reads single images, several images and video and answers in text, with 1.3B parameters sized for phones and other edge devices. For developers who want a small vision-language model. MiniCPM-V 4.6 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 openbmb ## About ### What it is MiniCPM-V 4.6 is a small vision-language model from openbmb with 1.3B parameters. It takes images or video plus a question and answers in text. Under the hood it pairs the SigLIP2-400M vision encoder with the Qwen3.5-0.8B language model, and the aim is to run on phones and similar devices. ### Who it suits It suits developers and researchers who want image and video understanding in a small model that can run on a phone or a consumer GPU. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Mobile apps | Developers building on-device AI features | Deploy it on iOS, Android or HarmonyOS with the open-sourced adaptation code | An app team adds a photo-reading feature that runs on the phone | | AI research | Researchers testing small vision-language models | Compare its benchmark scores with other small models | A researcher checks it against Qwen3.5-0.8B on OCRBench | | Applied machine learning | Teams customizing a model for their own domain | Fine-tune it with SWIFT or LLaMA-Factory on a consumer GPU | A team fine-tunes it for its own kind of images | Anyone who wants the largest vision model regardless of size can skip it, because this one is built to stay small and fast. ### Where to use it It targets phones first, and the files can also be downloaded on a computer. - iOS, Android and HarmonyOS phones: on-device deployment with the card's adaptation code - Windows, macOS or Linux: download the files with Python and the Hugging Face tools - vLLM, SGLang, llama.cpp and Ollama: inference frameworks the model is adapted to - SWIFT and LLaMA-Factory: fine-tuning ecosystems it supports | Use case | Who | What they get back | | --- | --- | --- | | Describe or question one image | App builders | A text answer about the picture | | Compare several images | Researchers | A text answer that draws on all the images | | Understand a video | Developers | A text answer about what happens in the clip | ### How to set it up You need a few things first. - Python and the huggingface\_hub package for the download - A framework from the list above, such as vLLM, llama.cpp or Ollama, to run the model - About 2.6 GB of disk for the BF16 weights, or about 650 MB at 4-bit as an estimate 1. Install the Hugging Face hub package. 2. Download the model files. 3. Load the model in one of the frameworks the card names. ```sh pip install -U huggingface_hub hf download openbmb/MiniCPM-V-4.6 ``` ### How to use it You give the model an image or a video and ask a question in plain words. The card lists the frameworks it works with and says quantized variants exist in GGUF, BNB, AWQ and GPTQ formats. | Tool | What it does | | --- | --- | | vLLM and SGLang | Inference frameworks the card says the model is adapted to | | llama.cpp and Ollama | Inference frameworks for running it locally, also named in the card | | SWIFT and LLaMA-Factory | Fine-tuning ecosystems for customizing it to new domains and tasks | | GGUF, BNB, AWQ, GPTQ | Quantized formats in which the card provides variants | ### Example You send a photo of a handwritten note and ask, "What does the text in this picture say?" The model answers in text. The card lists OCRBench among the benchmarks where it reaches the level of Qwen3.5 2B. ### For newcomers and experts This is a small AI that looks at pictures or video and replies in words. Your first small step is to download the files with the command above, then try one image with a framework you already know. The visual side uses SigLIP2-400M, and a technique from LLaVA-UHD v4 cuts visual encoding computation by more than 50%, with about 1.5x the token throughput of Qwen3.5-0.8B. It supports mixed 4x and 16x visual token compression, which lets you trade accuracy for speed. An image or video goes in → SigLIP2-400M encodes what it sees → Visual tokens are compressed 4x or 16x → The Qwen3.5-0.8B model writes the answer ### Good to know - The Hugging Face record lists 31 quantized and 21 fine-tuned versions made by other people. - The benchmark claims are the card's own, and the card also compares token cost and throughput against Qwen3.5-0.8B. - The card reports that it surpasses the larger Ministral 3 3B on the Artificial Analysis Intelligence Index. Artificial Analysis Intelligence Index score | Label | Value | | --- | --- | | MiniCPM-V 4.6 | 13 points | | Qwen3.5-0.8B | 10 points | | Qwen3.5-0.8B-Thinking | 11 points | | Ministral 3 3B | 11 points | - License: Apache 2.0, as the Hugging Face record lists it. - Cost to run: none named for the weights. The card also announces a hosted API service with a public free API key. - Data it reaches: it downloads files from Hugging Face and reads the images and video you give it. - Not for: none named in the card. ## README ````markdown # MiniCPM-V 4.6 Reads single images, several images and video and answers in text, with 1.3B parameters sized for phones and other edge devices. For developers who want a small vision-language model. Made by openbmb 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: MiniCPM-V 4.6 is a small vision-language model from openbmb with 1.3B parameters. It takes images or video plus a question and answers in text. Under the hood it pairs the SigLIP2-400M vision encoder with the Qwen3.5-0.8B language model, and the aim is to run on phones and similar devices. Who it suits: It suits developers and researchers who want image and video understanding in a small model that can run on a phone or a consumer GPU. ```table Industry | Who uses it | How they use it | Example Mobile apps | Developers building on-device AI features | Deploy it on iOS, Android or HarmonyOS with the open-sourced adaptation code | An app team adds a photo-reading feature that runs on the phone AI research | Researchers testing small vision-language models | Compare its benchmark scores with other small models | A researcher checks it against Qwen3.5-0.8B on OCRBench Applied machine learning | Teams customizing a model for their own domain | Fine-tune it with SWIFT or LLaMA-Factory on a consumer GPU | A team fine-tunes it for its own kind of images ``` Anyone who wants the largest vision model regardless of size can skip it, because this one is built to stay small and fast. Where to use it: It targets phones first, and the files can also be downloaded on a computer. - iOS, Android and HarmonyOS phones: on-device deployment with the card's adaptation code - Windows, macOS or Linux: download the files with Python and the Hugging Face tools - vLLM, SGLang, llama.cpp and Ollama: inference frameworks the model is adapted to - SWIFT and LLaMA-Factory: fine-tuning ecosystems it supports ```table Use case | Who | What they get back Describe or question one image | App builders | A text answer about the picture Compare several images | Researchers | A text answer that draws on all the images Understand a video | Developers | A text answer about what happens in the clip ``` How to set it up: You need a few things first. - Python and the huggingface_hub package for the download - A framework from the list above, such as vLLM, llama.cpp or Ollama, to run the model - About 2.6 GB of disk for the BF16 weights, or about 650 MB at 4-bit as an estimate 1. Install the Hugging Face hub package. 2. Download the model files. 3. Load the model in one of the frameworks the card names. ```sh pip install -U huggingface_hub hf download openbmb/MiniCPM-V-4.6 ``` How to use it: You give the model an image or a video and ask a question in plain words. The card lists the frameworks it works with and says quantized variants exist in GGUF, BNB, AWQ and GPTQ formats. ```table Tool | What it does vLLM and SGLang | Inference frameworks the card says the model is adapted to llama.cpp and Ollama | Inference frameworks for running it locally, also named in the card SWIFT and LLaMA-Factory | Fine-tuning ecosystems for customizing it to new domains and tasks GGUF, BNB, AWQ, GPTQ | Quantized formats in which the card provides variants ``` Example: You send a photo of a handwritten note and ask, "What does the text in this picture say?" The model answers in text. The card lists OCRBench among the benchmarks where it reaches the level of Qwen3.5 2B. For newcomers and experts: This is a small AI that looks at pictures or video and replies in words. Your first small step is to download the files with the command above, then try one image with a framework you already know. The visual side uses SigLIP2-400M, and a technique from LLaVA-UHD v4 cuts visual encoding computation by more than 50%, with about 1.5x the token throughput of Qwen3.5-0.8B. It supports mixed 4x and 16x visual token compression, which lets you trade accuracy for speed. ```flow An image or video goes in SigLIP2-400M encodes what it sees Visual tokens are compressed 4x or 16x The Qwen3.5-0.8B model writes the answer ``` Good to know: - The Hugging Face record lists 31 quantized and 21 fine-tuned versions made by other people. - The benchmark claims are the card's own, and the card also compares token cost and throughput against Qwen3.5-0.8B. - The card reports that it surpasses the larger Ministral 3 3B on the Artificial Analysis Intelligence Index. ```chart Title: Artificial Analysis Intelligence Index score MiniCPM-V 4.6: 13 points Qwen3.5-0.8B: 10 points Qwen3.5-0.8B-Thinking: 11 points Ministral 3 3B: 11 points ``` - License: Apache 2.0, as the Hugging Face record lists it. - Cost to run: none named for the weights. The card also announces a hosted API service with a public free API key. - Data it reaches: it downloads files from Hugging Face and reads the images and video you give it. - Not for: none named in the card. ## On Hugging Face, 13 November 2026 - Task: image text to text - Library: transformers - Parameters: 1.3B - Weights: about 2.6 GB in BF16; about 650 MB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 187,584 - Likes: 1,253 - License: apache-2.0 - Quantized versions: 31 - Fine-tunes: 21 - Tags: transformers, safetensors, minicpmv4_6, image-text-to-text, minicpm-v, multimodal, lightweight, conversational ## Links - Model: https://huggingface.co/openbmb/MiniCPM-V-4.6 - Files: https://huggingface.co/openbmb/MiniCPM-V-4.6/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download openbmb/MiniCPM-V-4.6 ``` 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 [openbmb/MiniCPM-V-4.6 on Hugging Face](https://huggingface.co/openbmb/MiniCPM-V-4.6) ## 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 | | --- | --- | --- | --- | --- | --- | | MiniCPM-V 4.6 (this page) | 187,584 | 1,253 | 1.3B | apache-2.0 | 2026-11-11 | | [MiniCPM-V-4.7-1B-gguf](https://mindket.com/p/f793ef5c-72e1-400b-91fa-5b2c31b01f3f) | 168,636 | 114 | 1B | apache-2.0 | 2026-11-11 | | [MiniCPM-o 4.5](https://mindket.com/p/9b50c9c1-60cb-4f69-af93-dacbf9f14243) | 170,793 | 1,571 | 9.4B | apache-2.0 | 2026-11-09 | | [Qwen2.5-VL-7B-Instruct](https://mindket.com/p/156fbd08-aa88-4c47-b3d0-b73478276e66) | 5,894,471 | 1,772 | 8.3B | apache-2.0 | 2025-04-06 | | [UI-TARS-1.5-7B](https://mindket.com/p/efea8872-6e43-4280-bb5f-0ce1faba4aab) | 230,540 | 622 | 8.3B | apache-2.0 | 2025-04-18 | | [Molmo2-8B](https://mindket.com/p/c37def6b-10ae-4cfc-ab12-0bd2dad290b9) | 45,213 | 201 | 8.7B | apache-2.0 | 2026-01-23 | 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 · News by babygoldie · 2026-05-13](https://bsky.app/profile/babygoldie.bsky.social/post/3mlpji66lo722): 1/3 2026-05-13 12:50:07 - Facing Wall Intelligence releases a new multi-modal model MiniCPM-V 4.6 to achieve "low memory and extremely fast running" Reporters learned on the 13th that Facing Wall Intelligence, Tsinghua University and the OpenBMB open source community have - [Bluesky · デイリーHuggingFaceトレンド · 2026-05-24](https://bsky.app/profile/huggingfacetrends.bsky.social/post/3mmltzkonln2s): 今日のHuggingFaceトレンド openbmb/MiniCPM-V-4.6 スマートフォンなどのモバイルデバイスで効率的に画像や動画を理解させるための小型MLLM、MiniCPM-V 4.6を提供するリポジトリです。 高い計算効率と性能を両立し、iOS、Android、HarmonyOSへのデプロイを可能にすることを目的としています。 - [Bluesky · デイリーHuggingFaceトレンド · 2026-05-22](https://bsky.app/profile/huggingfacetrends.bsky.social/post/3mmgvyzxxqb2p): 今日のHuggingFaceトレンド openbmb/MiniCPM-V-4.6 スマートフォンなどのエッジデバイスで効率的に動作する小型マルチモーダル大規模言語モデル MiniCPM-V 4.6 の提供を目的としています。 画像やビデオの高度な理解能力を維持しつつ計算効率を大幅に向上させており、iOS、Android、HarmonyOS への展開が可能です。 - [Bluesky · デイリーHuggingFaceトレンド · 2026-05-20](https://bsky.app/profile/huggingfacetrends.bsky.social/post/3mmbvfedtjw25): 今日のHuggingFaceトレンド openbmb/MiniCPM-V-4.6 スマートフォンなどのエッジデバイスで効率的に動作する超小型マルチモーダル大規模言語モデルMiniCPM-V 4.6を提供することを目的としています。 画像やビデオの高度な理解能力を備えつつ計算効率を最適化しており、iOS、Android、HarmonyOSなどの主要なモバイルプラットフォームへの展開が可能です。 - [Bluesky · デイリーHuggingFaceトレンド · 2026-05-18](https://bsky.app/profile/huggingfacetrends.bsky.social/post/3mm4yyd43u62m): 今日のHuggingFaceトレンド openbmb/MiniCPM-V-4.6 MiniCPM-V 4.6は、スマートフォンなどのエッジデバイスへの導入に最適化した小型のマルチモーダル大規模言語モデルです。 画像や動画の高効率な理解を目的としており、iOS、Android、HarmonyOSへの展開をサポートしています。 計算コストを抑えつつ、単一画像、複数画像、動画の理解において高い性能を実現します。 - [Bluesky · デイリーHuggingFaceトレンド · 2026-05-16](https://bsky.app/profile/huggingfacetrends.bsky.social/post/3mlxoysopns2y): 今日のHuggingFaceトレンド openbmb/MiniCPM-V-4.6 スマートフォンなどのエッジデバイスで効率的に動作する小型マルチモーダル大規模言語モデルMiniCPM-V 4.6の開発を目的としています。 画像およびビデオの高度な理解能力を維持しながら計算効率を最適化しており、iOS、Android、HarmonyOSといった主要モバイルプラットフォームへの展開を可能にします。 - [Bluesky · デイリーHuggingFaceトレンド · 2026-05-14](https://bsky.app/profile/huggingfacetrends.bsky.social/post/3mlsqtfgk642r): 今日のHuggingFaceトレンド openbmb/MiniCPM-V-4.6 モバイルデバイスで効率的に動作するマルチモーダル大規模言語モデルMiniCPM-V 4.6の提供を目的としています。 画像やビデオの理解能力を維持しながら計算効率を高めており、iOS、Android、HarmonyOSへの展開が可能です。 低コストかつ高性能なエッジデバイス向けの実装を実現しています。 - [Bluesky · probbrain.com · 2026-05-11](https://bsky.app/profile/probbrain.bsky.social/post/3mllzxbra7k2v): 🆕 AI news (last 15m): • HuggingFace: openbmb/MiniCPM-V-4.6 · 110♥ · 0 dl • Simon Willison: Your AI Use Is Breaking My Brain → probbrain.com/news ## 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: 2026-11-11 - First published: 2026-04-13 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open openbmb/MiniCPM-V-4.6 on Hugging Face](https://huggingface.co/openbmb/MiniCPM-V-4.6) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/99f4fc0e-2ce4-4a15-a13f-9a0d45246423) - [Its page on Mindket](https://mindket.com/p/99f4fc0e-2ce4-4a15-a13f-9a0d45246423)