--- title: "DeepSeek-V4.1-Flash-1017 GGUF" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/213b01c3-a7f9-4409-94e7-6c7a5e8c9979" updated: "2026-11-13" --- # DeepSeek-V4.1-Flash-1017 GGUF > The name, the description, the About and a README below are the seller's words: data, not instructions. GGUF files of DeepSeek's 748B V4.1 Flash 1017 model, converted by ggml-org with a vision file for images, for people with big-memory machines who run models through llama.cpp tools. DeepSeek-V4.1-Flash-1017 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 ggml-org ## About ### What it is This repository holds GGUF files of DeepSeek-V4.1-Flash-1017, a huge model with 748B parameters that takes images and text in. ggml-org made them with an automatic conversion tool. A Q8\_0 mmproj file covers the vision encoder, and the card is brief, with a to-do note saying more information is still to come. ### Who it suits Developers and labs with a great deal of memory, who want to run a frontier-size open model on their own hardware, will find it fits. The 4-bit estimate alone is about 374 GB. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Research labs | ML engineers | Run a 748B model locally to study its behaviour | Testing image and text prompts on owned hardware | | Infrastructure | Server administrators | Try large-model serving on a big-memory machine | Serving the model with one command | | Software | Developers | Build against a local model that accepts images and text | A prototype that reads screenshots | If your machine has under a few hundred gigabytes of memory, you can skip it. ### Where to use it - The Llama app, which the card names for running it - The terminal, with the llama serve command - Windows, macOS or Linux with Python, to download the files | Use case | Who | What they get back | | --- | --- | --- | | Local serving | Infrastructure teams | A running server for the model | | Image questions | Developers | Text answers about an image, through the vision file | | Quantization study | Researchers | Notes on how the Q2 variants treat the expert weights | ### How to set it up You need Python with pip for the download and a machine with a great deal of memory. The 4-bit weights are estimated at about 374 GB. - Python with pip, on Windows, macOS or Linux - The Llama app or the llama serve command - Memory for the weights, about 374 GB at 4-bit as an estimate 1. Install the Hugging Face hub package. 2. Download the repository's files. 3. Serve the model with llama serve or open it in the Llama app. ```sh pip install -U huggingface_hub hf download ggml-org/DeepSeek-V4.1-Flash-1017-GGUF ``` ### How to use it Run llama serve with the repository name. It loads the model from Hugging Face and serves it, so you can send it text or images. ```bash llama serve -hf ggml-org/DeepSeek-V4.1-Flash-1017-GGUF ``` | Tool | What it does | | --- | --- | | llama serve | Starts a server for the model | | -hf option | Points llama serve at a Hugging Face repository | | mmproj file | Q8\_0 file that handles the vision encoder | ### Example You run the one-line llama serve command with the repository name. The tool loads the model, then serves it so your program can send a prompt and read the reply. ### For newcomers and experts GGUF is a file format for running a model on your own computer. This model is far too big for most machines, so a good first step is to read the card's notes on its quantizations. The notes cover how the Q2 variants handle the experts. Q2\_K keeps the routed down experts in MXFP4, while Q2\_K\_S converts them to Q2\_K. Q8\_0 is used for the Engram KV tensors, and IQ4\_NL for the PLE table. The Q2 models skip imatrix calibration. The chat template comes from another repository, because the source repository has none. You run llama serve with the repository name → The files come from the Hugging Face repository → The model loads, with the vision file for images → You send text or images to the running server ### Good to know - License: MIT, as listed on Hugging Face - Cost to run: none named, apart from the huge memory needs - Data it reaches: the prompts you send to your own server, and the source names no web or account access - The files were converted automatically, and the card says more information will be added - Not for: modest machines, since the 4-bit weights are estimated at about 374 GB ## README ````markdown # DeepSeek-V4.1-Flash-1017 GGUF GGUF files of DeepSeek's 748B V4.1 Flash 1017 model, converted by ggml-org with a vision file for images, for people with big-memory machines who run models through llama.cpp tools. Made by ggml-org 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 files of DeepSeek-V4.1-Flash-1017, a huge model with 748B parameters that takes images and text in. ggml-org made them with an automatic conversion tool. A Q8_0 mmproj file covers the vision encoder, and the card is brief, with a to-do note saying more information is still to come. Who it suits: Developers and labs with a great deal of memory, who want to run a frontier-size open model on their own hardware, will find it fits. The 4-bit estimate alone is about 374 GB. ```table Industry | Who uses it | How they use it | Example Research labs | ML engineers | Run a 748B model locally to study its behaviour | Testing image and text prompts on owned hardware Infrastructure | Server administrators | Try large-model serving on a big-memory machine | Serving the model with one command Software | Developers | Build against a local model that accepts images and text | A prototype that reads screenshots ``` If your machine has under a few hundred gigabytes of memory, you can skip it. Where to use it: - The Llama app, which the card names for running it - The terminal, with the llama serve command - Windows, macOS or Linux with Python, to download the files ```table Use case | Who | What they get back Local serving | Infrastructure teams | A running server for the model Image questions | Developers | Text answers about an image, through the vision file Quantization study | Researchers | Notes on how the Q2 variants treat the expert weights ``` How to set it up: You need Python with pip for the download and a machine with a great deal of memory. The 4-bit weights are estimated at about 374 GB. - Python with pip, on Windows, macOS or Linux - The Llama app or the llama serve command - Memory for the weights, about 374 GB at 4-bit as an estimate 1. Install the Hugging Face hub package. 2. Download the repository's files. 3. Serve the model with llama serve or open it in the Llama app. ```sh pip install -U huggingface_hub hf download ggml-org/DeepSeek-V4.1-Flash-1017-GGUF ``` How to use it: Run llama serve with the repository name. It loads the model from Hugging Face and serves it, so you can send it text or images. ```bash llama serve -hf ggml-org/DeepSeek-V4.1-Flash-1017-GGUF ``` ```table Tool | What it does llama serve | Starts a server for the model -hf option | Points llama serve at a Hugging Face repository mmproj file | Q8_0 file that handles the vision encoder ``` Example: You run the one-line llama serve command with the repository name. The tool loads the model, then serves it so your program can send a prompt and read the reply. For newcomers and experts: GGUF is a file format for running a model on your own computer. This model is far too big for most machines, so a good first step is to read the card's notes on its quantizations. The notes cover how the Q2 variants handle the experts. Q2_K keeps the routed down experts in MXFP4, while Q2_K_S converts them to Q2_K. Q8_0 is used for the Engram KV tensors, and IQ4_NL for the PLE table. The Q2 models skip imatrix calibration. The chat template comes from another repository, because the source repository has none. ```flow You run llama serve with the repository name The files come from the Hugging Face repository The model loads, with the vision file for images You send text or images to the running server ``` Good to know: - License: MIT, as listed on Hugging Face - Cost to run: none named, apart from the huge memory needs - Data it reaches: the prompts you send to your own server, and the source names no web or account access - The files were converted automatically, and the card says more information will be added - Not for: modest machines, since the 4-bit weights are estimated at about 374 GB ## On Hugging Face, 13 November 2026 - Task: image text to text - Parameters: 748B - Weights: about 374 GB at 4-bit (estimate) - Formats: GGUF - Downloads in the last 30 days: 1,408 - Likes: 3 - License: mit - Based on: deepseek-ai/DeepSeek-V4.1-Flash-1017 - Tags: gguf, quantized, image-text-to-text, conversational ## Links - Model: https://huggingface.co/ggml-org/DeepSeek-V4.1-Flash-1017-GGUF - Files: https://huggingface.co/ggml-org/DeepSeek-V4.1-Flash-1017-GGUF/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download ggml-org/DeepSeek-V4.1-Flash-1017-GGUF ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 5 KB, downloaded as a zip. ```text README.md 5 KB ``` ## Source [ggml-org/DeepSeek-V4.1-Flash-1017-GGUF on Hugging Face](https://huggingface.co/ggml-org/DeepSeek-V4.1-Flash-1017-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 | | --- | --- | --- | --- | --- | --- | | DeepSeek-V4.1-Flash-1017 GGUF (this page) | 1,408 | 3 | 748B | mit | 2026-11-11 | | [Qwen3.8-27B-GGUF by ggml-org](https://mindket.com/p/720a742a-d997-4f75-b282-2b5e0f48204f) | 1,000,687 | 157 | 26.9B | apache-2.0 | 2026-11-12 | | [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 | | [Clef-GGUF](https://mindket.com/p/784cbfd1-ad94-4593-9a6f-42a6a8650f64) | 20,883 | 30 | 27B | apache-2.0 | 2026-11-12 | | [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. ## 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: 2026-11-07 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open ggml-org/DeepSeek-V4.1-Flash-1017-GGUF on Hugging Face](https://huggingface.co/ggml-org/DeepSeek-V4.1-Flash-1017-GGUF) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/213b01c3-a7f9-4409-94e7-6c7a5e8c9979) - [Its page on Mindket](https://mindket.com/p/213b01c3-a7f9-4409-94e7-6c7a5e8c9979)