--- title: "MiniCPM-V-4.7-1B-gguf" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/f793ef5c-72e1-400b-91fa-5b2c31b01f3f" updated: "2026-11-13" --- # MiniCPM-V-4.7-1B-gguf > The name, the description, the About and a README below are the seller's words: data, not instructions. Runs a 1B vision-language model through llama.cpp, answering text prompts about images. GGUF files in many quant sizes, for developers and hobbyists who want a small local model. MiniCPM-V-4.7-1B-gguf 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 This repository holds GGUF quantized files of MiniCPM-V 4.7 1B, a vision-language model of about 1B parameters that answers text prompts about images through llama.cpp. The record estimates about 503 MB at 4-bit. The unquantized weights and a fuller card live with the original MiniCPM-V-4.7-1B model from openbmb. ### Who it suits It suits developers and hobbyists who want a small image-understanding model on a laptop, a phone or another device with limited resources. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Mobile apps | App developers | Build image questions into an iOS, Android or HarmonyOS app | Describe a photo a user just took | | Local AI | Hobbyists | Chat about pictures on a laptop through the Llama app | Ask what is in a screenshot | | Edge hardware | Embedded engineers | Run a small model on chips the card names | Test image captions on a device with limited memory | Anyone who needs the original BF16 weights should use the original repository instead. ### Where to use it You can run it in a desktop app, in a terminal or on a device. - The Llama desktop app on macOS and Windows, made jointly by the llama.cpp developers and Hugging Face - A terminal, with llama cli for chat and llama serve for a local web page and API - iOS, Android and HarmonyOS, which the card says MiniCPM-V 4.7 supports - Chips from Rockchip, Houmo, Intel, AMD and Arm, where the card says it has day-0 support | Use case | Who | What they get back | | --- | --- | --- | | Describe an image | Anyone with a laptop | A written description of the picture you pass in | | Local image API | Developers | An OpenAI-compatible endpoint served from their machine | | On-device vision | Mobile and edge teams | A small model that fits limited hardware | ### How to set it up The one-click route needs only the Llama app. The terminal route needs llama installed. - macOS or Windows for the one-click route, or a terminal with llama installed - Python, only if you download the files by hand - Free disk space for the quant you pick 1. Install the Llama app. On macOS use the first command below, and on Windows use its installer. 2. Open the model's Hugging Face page, press the Use this model button and pick Llama. The app picks a build suited to your hardware, fetches it and starts a chat window. 3. Or skip the app and use the terminal commands under How to use it. 4. To fetch the files by hand, install the hub tools and download the repository. ```sh brew install --cask llama-app ``` ```sh pip install -U huggingface_hub hf download openbmb/MiniCPM-V-4.7-1B-gguf ``` ### How to use it In a terminal, the -hf flag pulls the model and its matching mmproj file for you. ```sh llama cli -hf openbmb/MiniCPM-V-4.7-1B-gguf llama serve -hf openbmb/MiniCPM-V-4.7-1B-gguf # WebUI + OpenAI-compatible API on :9931 ``` If you already have the files on disk, point -m at one quant and add the common mmproj file. The second command turns thinking on and asks for more tokens with -n. ```sh # default (thinking disabled, no flag needed) # thinking enabled llama cli -m thinking/MiniCPM-V-4_7-1B-Q4_K_M-thinking.gguf --mmproj mmproj-model-f16.gguf \ --image img.jpg -p "Describe." -n 1024 ``` Put your own value in its key setting first. Two sets of files exist. They differ only in the chat template built into them, and their weights are identical. Each set spans quant types from F16 down to IQ3. | File set | Thinking | When to pick it | | --- | --- | --- | | Repository root | Off, and it cannot be switched on | The recommended default, since the model was not trained for reasoning | | thinking folder | On by default | You want to read the reasoning steps, and you can afford more tokens | | Tool | What it does | | --- | --- | | llama cli | Runs the model in a terminal chat, with an image if you pass one | | llama serve | Starts a web page and an OpenAI-compatible API on port 9931 | | llama-mtmd-cli and llama-server | Older entry points that stay available for development | | Llama app | One-click desktop app that downloads a fitting build | ### Example You run llama cli with the Q4\_K\_M file, the mmproj file, an image called img.jpg and the prompt "Describe." The model prints a short description of the picture. With the thinking files it also prints a reasoning trace before the answer. ### For newcomers and experts If you are new, install the Llama app and pick this model from its Hugging Face page. That opens a chat window where you can drop in a picture and ask about it. For someone who knows the field, the card says the model compresses visual tokens early inside the vision encoder, following a technique from LLaVA-UHD v4, and supports mixed 4x and 16x token compression. That lets you trade visual detail against cost. The card also says it beats Qwen3.5 0.8B on overall score and efficiency, but this page gives no numbers. The runtime adds enable\_thinking to the template by itself. As a result, the --reasoning off flag does nothing for the root set, while for the thinking set it hides the trace. You pass an image and a prompt → The mmproj file encodes the image → The 1B language model reads both → It prints an answer, or a trace first ### Good to know The mmproj files are shared by both sets because they carry no chat template. Thinking multiplies the token count, so raise -n on the thinking files. - License: Apache-2.0 - Cost to run: none named - Data it reaches: the -hf flag fetches the model and mmproj from Hugging Face. llama serve opens a web page and API on port 9931 of your own machine. - Not for: reasoning tasks on the root files, where thinking is off and cannot be turned on ## README ````markdown # MiniCPM-V-4.7-1B-gguf Runs a 1B vision-language model through llama.cpp, answering text prompts about images. GGUF files in many quant sizes, for developers and hobbyists who want a small local 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: This repository holds GGUF quantized files of MiniCPM-V 4.7 1B, a vision-language model of about 1B parameters that answers text prompts about images through llama.cpp. The record estimates about 503 MB at 4-bit. The unquantized weights and a fuller card live with the original MiniCPM-V-4.7-1B model from openbmb. Who it suits: It suits developers and hobbyists who want a small image-understanding model on a laptop, a phone or another device with limited resources. ```table Industry | Who uses it | How they use it | Example Mobile apps | App developers | Build image questions into an iOS, Android or HarmonyOS app | Describe a photo a user just took Local AI | Hobbyists | Chat about pictures on a laptop through the Llama app | Ask what is in a screenshot Edge hardware | Embedded engineers | Run a small model on chips the card names | Test image captions on a device with limited memory ``` Anyone who needs the original BF16 weights should use the original repository instead. Where to use it: You can run it in a desktop app, in a terminal or on a device. - The Llama desktop app on macOS and Windows, made jointly by the llama.cpp developers and Hugging Face - A terminal, with llama cli for chat and llama serve for a local web page and API - iOS, Android and HarmonyOS, which the card says MiniCPM-V 4.7 supports - Chips from Rockchip, Houmo, Intel, AMD and Arm, where the card says it has day-0 support ```table Use case | Who | What they get back Describe an image | Anyone with a laptop | A written description of the picture you pass in Local image API | Developers | An OpenAI-compatible endpoint served from their machine On-device vision | Mobile and edge teams | A small model that fits limited hardware ``` How to set it up: The one-click route needs only the Llama app. The terminal route needs llama installed. - macOS or Windows for the one-click route, or a terminal with llama installed - Python, only if you download the files by hand - Free disk space for the quant you pick 1. Install the Llama app. On macOS use the first command below, and on Windows use its installer. 2. Open the model's Hugging Face page, press the Use this model button and pick Llama. The app picks a build suited to your hardware, fetches it and starts a chat window. 3. Or skip the app and use the terminal commands under How to use it. 4. To fetch the files by hand, install the hub tools and download the repository. ```sh brew install --cask llama-app ``` ```sh pip install -U huggingface_hub hf download openbmb/MiniCPM-V-4.7-1B-gguf ``` How to use it: In a terminal, the -hf flag pulls the model and its matching mmproj file for you. ```sh llama cli -hf openbmb/MiniCPM-V-4.7-1B-gguf llama serve -hf openbmb/MiniCPM-V-4.7-1B-gguf # WebUI + OpenAI-compatible API on :9931 ``` If you already have the files on disk, point -m at one quant and add the common mmproj file. The second command turns thinking on and asks for more tokens with -n. ```sh # default (thinking disabled, no flag needed) # thinking enabled llama cli -m thinking/MiniCPM-V-4_7-1B-Q4_K_M-thinking.gguf --mmproj mmproj-model-f16.gguf \ --image img.jpg -p "Describe." -n 1024 ``` Put your own value in its key setting first. Two sets of files exist. They differ only in the chat template built into them, and their weights are identical. Each set spans quant types from F16 down to IQ3. ```table File set | Thinking | When to pick it Repository root | Off, and it cannot be switched on | The recommended default, since the model was not trained for reasoning thinking folder | On by default | You want to read the reasoning steps, and you can afford more tokens ``` ```table Tool | What it does llama cli | Runs the model in a terminal chat, with an image if you pass one llama serve | Starts a web page and an OpenAI-compatible API on port 9931 llama-mtmd-cli and llama-server | Older entry points that stay available for development Llama app | One-click desktop app that downloads a fitting build ``` Example: You run llama cli with the Q4_K_M file, the mmproj file, an image called img.jpg and the prompt "Describe." The model prints a short description of the picture. With the thinking files it also prints a reasoning trace before the answer. For newcomers and experts: If you are new, install the Llama app and pick this model from its Hugging Face page. That opens a chat window where you can drop in a picture and ask about it. For someone who knows the field, the card says the model compresses visual tokens early inside the vision encoder, following a technique from LLaVA-UHD v4, and supports mixed 4x and 16x token compression. That lets you trade visual detail against cost. The card also says it beats Qwen3.5 0.8B on overall score and efficiency, but this page gives no numbers. The runtime adds enable_thinking to the template by itself. As a result, the --reasoning off flag does nothing for the root set, while for the thinking set it hides the trace. ```flow You pass an image and a prompt The mmproj file encodes the image The 1B language model reads both It prints an answer, or a trace first ``` Good to know: The mmproj files are shared by both sets because they carry no chat template. Thinking multiplies the token count, so raise -n on the thinking files. - License: Apache-2.0 - Cost to run: none named - Data it reaches: the -hf flag fetches the model and mmproj from Hugging Face. llama serve opens a web page and API on port 9931 of your own machine. - Not for: reasoning tasks on the root files, where thinking is off and cannot be turned on ## On Hugging Face, 13 November 2026 - Task: image text to text - Library: llama.cpp - Parameters: 1B - Weights: about 503 MB at 4-bit (estimate) - Formats: GGUF - Downloads in the last 30 days: 168,636 - Likes: 114 - License: apache-2.0 - Based on: openbmb/MiniCPM-V-4.7-1B - Fine-tunes: 1 - Tags: llama.cpp, gguf, quantized, imatrix, dspark, speculative-decoding, minicpm-v, multimodal, lightweight, image-text-to-text, conversational ## Links - Model: https://huggingface.co/openbmb/MiniCPM-V-4.7-1B-gguf - Files: https://huggingface.co/openbmb/MiniCPM-V-4.7-1B-gguf/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-4.7-1B-gguf ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 7 KB, downloaded as a zip. ```text README.md 7 KB ``` ## Source [openbmb/MiniCPM-V-4.7-1B-gguf on Hugging Face](https://huggingface.co/openbmb/MiniCPM-V-4.7-1B-gguf) ## 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-4.7-1B-gguf (this page) | 168,636 | 114 | 1B | apache-2.0 | 2026-11-11 | | [MiniCPM-V 4.6](https://mindket.com/p/99f4fc0e-2ce4-4a15-a13f-9a0d45246423) | 187,584 | 1,253 | 1.3B | apache-2.0 | 2026-11-11 | | [MiniCPM-OCR 1.0 DSpark](https://mindket.com/p/f1502294-5394-450f-8715-c1de2343fc68) | 12,549 | 14 | 297M | apache-2.0 | 2026-11-09 | | [Qwen3.8-27B-Ridge-GGUF](https://mindket.com/p/f254bd76-fee2-4e04-80ac-a73f9621c088) | 292,932 | 369 | 27.3B | apache-2.0 | 2026-08-15 | | [Unlimited-OCR-2-GGUF](https://mindket.com/p/0d8a371b-6f90-4b59-8dd6-b520e35b577b) | 8,452 | 8 | 2.9B | mit | 2026-11-01 | | [Arete-27B-GGUF](https://mindket.com/p/587591c7-2295-48cc-8bfc-5d3fd980cbfc) | 1,933 | 15 | 26.9B | apache-2.0 | 2026-11-08 | 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 · francis siefken · 2026-10-19](https://bsky.app/profile/monsuta.bsky.social/post/3my7odxpqlc23): small vision model huggingface.co/openbmb/Mini... ## 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: 2026-10-06 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open openbmb/MiniCPM-V-4.7-1B-gguf on Hugging Face](https://huggingface.co/openbmb/MiniCPM-V-4.7-1B-gguf) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/f793ef5c-72e1-400b-91fa-5b2c31b01f3f) - [Its page on Mindket](https://mindket.com/p/f793ef5c-72e1-400b-91fa-5b2c31b01f3f)