--- title: "DeepSeek-V4.1-Flash-1017-Sushi-STQ-1.75bpw" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/fdad1d8f-5a04-4c8c-abd2-33ad12a14786" updated: "2026-11-12" --- # DeepSeek-V4.1-Flash-1017-Sushi-STQ-1.75bpw > The name, the description, the About and a README below are the seller's words: data, not instructions. Runs a 261B DeepSeek V4.1 Flash model packed to about 1.75 bits per weight for the sushi engine on Apple Silicon Macs, with image input and up to 1,048,576 tokens of context. DeepSeek-V4.1-Flash-1017-Sushi-STQ-1.75bpw 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 beamster ## About ### What it is This repository holds DeepSeek-V4.1-Flash-1017 repacked by beamster for sushi, an inference engine built for Apple Silicon. Revision 2.0 stays closer to the original model than revision 1.0 in every test category. It also stores the expert weights in an order that sushi can stream from the SSD into its expert cache. ### Who it suits It suits Mac owners with Apple Silicon and plenty of disk space who want to run a huge model locally through sushi. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Local AI hobby | Mac enthusiasts | Run a huge open model on Apple Silicon | Trying a 261B model on a high-end Mac | | Software development | Developers | Test tool calling and agent behavior locally | Running a tool-use benchmark against the model | | AI research | Quantization researchers | Compare compressed packs against the original checkpoint | Reading the KLD tables for each category | | Long documents | Analysts | Feed in long inputs, up to the stated context limit | Asking questions about a long report | You can skip it if you use Windows, Linux or an Intel Mac, since this pack works only with sushi on Apple Silicon. ### Where to use it - The sushi engine, version 1.5.0 or later, on Apple Silicon - macOS 26.2 or later - Any computer with Python, only to download the files | Use case | Who | What they get back | | --- | --- | --- | | Local chat and image questions | Mac users | Text answers, with a vision tower for image input | | Tool calling | Developers | Tool-use behavior, measured at 92 out of 100 on revision 1.0 | | Long-context work | Analysts | A context window of up to 1,048,576 tokens | ### How to set it up You need a recent Apple Silicon Mac and about 309 GiB of disk for the pack. - An Apple Silicon Mac on macOS 26.2 or later - The sushi engine, version 1.5.0 or later, since older versions refuse this pack by name - About 309 GiB of disk, made of 120 GiB of weights and 189 GiB of Engram tables - Python and huggingface\_hub if you download with the Hugging Face tool 1. Install sushi 1.5.0 or later on your Mac. 2. Download the pack with the Hugging Face tool, or with sushi pull if you already hold revision 1.0. 3. Follow the sushi usage guide for launch flags, memory and context on your Mac. ```sh pip install -U huggingface_hub hf download beamster/DeepSeek-V4.1-Flash-1017-Sushi-STQ-1.75bpw ``` ### How to use it You run the model through sushi, whose usage guide holds the launch flags. If you stay on an older sushi, you can fetch the first revision by its tag instead. ```sh hf download beamster/DeepSeek-V4.1-Flash-1017-Sushi-STQ-1.75bpw --revision v1.0 ``` | Tool | What it does | | --- | --- | | sushi pull | Fetches revision 2.0 for someone who holds 1.0, changing only the files that differ | | hf download with --revision v1.0 | Fetches revision 1.0 for an older sushi | ### Example You already hold revision 1.0 and run sushi pull. It compares file contents, downloads only the files that changed, and checks each one before it replaces the old copy. ### For newcomers and experts If you are new to this, it is a giant model squeezed so it fits a Mac, and sushi is the program that runs it. A first small try is to read the sushi usage guide and check what memory your Mac needs. For people who know the field, routed experts use STQ, a trellis quantization with paired codewords, at an average of 1.75 bits. The trunk uses 4 bits and the output head 6 bits. The Engram tables stay as released and are read from the SSD, never loaded into GPU memory. The pack also includes the three DSpark speculative-decoding stages. KLD against the original model, lower is better | Label | Value | | --- | --- | | Revision 2.0 | 0.1576 KLD | | Revision 1.0 | 0.1843 KLD | | oQ2.5e | 0.1717 KLD | You download the pack → sushi loads the trunk and 1.75-bit experts → Engram tables stream from the SSD → You get text answers, or image answers via the vision tower ### Good to know The quality numbers come from the pack's author and cover eight categories with two prompts each, so treat them as a guide rather than a benchmark. - License: MIT, as a derivative of the DeepSeek-AI base model - Cost to run: none named - Data it reaches: files on your disk, with Engram tables read from the SSD - Not for: sushi versions before 1.5.0, or machines other than Apple Silicon Macs ## README ````markdown # DeepSeek-V4.1-Flash-1017-Sushi-STQ-1.75bpw Runs a 261B DeepSeek V4.1 Flash model packed to about 1.75 bits per weight for the sushi engine on Apple Silicon Macs, with image input and up to 1,048,576 tokens of context. Made by beamster 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 DeepSeek-V4.1-Flash-1017 repacked by beamster for sushi, an inference engine built for Apple Silicon. Revision 2.0 stays closer to the original model than revision 1.0 in every test category. It also stores the expert weights in an order that sushi can stream from the SSD into its expert cache. Who it suits: It suits Mac owners with Apple Silicon and plenty of disk space who want to run a huge model locally through sushi. ```table Industry | Who uses it | How they use it | Example Local AI hobby | Mac enthusiasts | Run a huge open model on Apple Silicon | Trying a 261B model on a high-end Mac Software development | Developers | Test tool calling and agent behavior locally | Running a tool-use benchmark against the model AI research | Quantization researchers | Compare compressed packs against the original checkpoint | Reading the KLD tables for each category Long documents | Analysts | Feed in long inputs, up to the stated context limit | Asking questions about a long report ``` You can skip it if you use Windows, Linux or an Intel Mac, since this pack works only with sushi on Apple Silicon. Where to use it: - The sushi engine, version 1.5.0 or later, on Apple Silicon - macOS 26.2 or later - Any computer with Python, only to download the files ```table Use case | Who | What they get back Local chat and image questions | Mac users | Text answers, with a vision tower for image input Tool calling | Developers | Tool-use behavior, measured at 92 out of 100 on revision 1.0 Long-context work | Analysts | A context window of up to 1,048,576 tokens ``` How to set it up: You need a recent Apple Silicon Mac and about 309 GiB of disk for the pack. - An Apple Silicon Mac on macOS 26.2 or later - The sushi engine, version 1.5.0 or later, since older versions refuse this pack by name - About 309 GiB of disk, made of 120 GiB of weights and 189 GiB of Engram tables - Python and huggingface_hub if you download with the Hugging Face tool 1. Install sushi 1.5.0 or later on your Mac. 2. Download the pack with the Hugging Face tool, or with sushi pull if you already hold revision 1.0. 3. Follow the sushi usage guide for launch flags, memory and context on your Mac. ```sh pip install -U huggingface_hub hf download beamster/DeepSeek-V4.1-Flash-1017-Sushi-STQ-1.75bpw ``` How to use it: You run the model through sushi, whose usage guide holds the launch flags. If you stay on an older sushi, you can fetch the first revision by its tag instead. ```sh hf download beamster/DeepSeek-V4.1-Flash-1017-Sushi-STQ-1.75bpw --revision v1.0 ``` ```table Tool | What it does sushi pull | Fetches revision 2.0 for someone who holds 1.0, changing only the files that differ hf download with --revision v1.0 | Fetches revision 1.0 for an older sushi ``` Example: You already hold revision 1.0 and run sushi pull. It compares file contents, downloads only the files that changed, and checks each one before it replaces the old copy. For newcomers and experts: If you are new to this, it is a giant model squeezed so it fits a Mac, and sushi is the program that runs it. A first small try is to read the sushi usage guide and check what memory your Mac needs. For people who know the field, routed experts use STQ, a trellis quantization with paired codewords, at an average of 1.75 bits. The trunk uses 4 bits and the output head 6 bits. The Engram tables stay as released and are read from the SSD, never loaded into GPU memory. The pack also includes the three DSpark speculative-decoding stages. ```chart Title: KLD against the original model, lower is better Revision 2.0: 0.1576 KLD Revision 1.0: 0.1843 KLD oQ2.5e: 0.1717 KLD ``` ```flow You download the pack sushi loads the trunk and 1.75-bit experts Engram tables stream from the SSD You get text answers, or image answers via the vision tower ``` Good to know: The quality numbers come from the pack's author and cover eight categories with two prompts each, so treat them as a guide rather than a benchmark. - License: MIT, as a derivative of the DeepSeek-AI base model - Cost to run: none named - Data it reaches: files on your disk, with Engram tables read from the SSD - Not for: sushi versions before 1.5.0, or machines other than Apple Silicon Macs ## On Hugging Face, 13 November 2026 - Task: image text to text - Parameters: 261B - Weights: about 261 GB in F8_E4M3; about 131 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 3,478 - Likes: 23 - License: mit - Based on: deepseek-ai/DeepSeek-V4.1-Flash-1017 - Tags: safetensors, deepseek_v41, sushi, apple-silicon, image-text-to-text, fp8 ## Links - Model: https://huggingface.co/beamster/DeepSeek-V4.1-Flash-1017-Sushi-STQ-1.75bpw - Files: https://huggingface.co/beamster/DeepSeek-V4.1-Flash-1017-Sushi-STQ-1.75bpw/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download beamster/DeepSeek-V4.1-Flash-1017-Sushi-STQ-1.75bpw ``` 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 [beamster/DeepSeek-V4.1-Flash-1017-Sushi-STQ-1.75bpw on Hugging Face](https://huggingface.co/beamster/DeepSeek-V4.1-Flash-1017-Sushi-STQ-1.75bpw) ## 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-Sushi-STQ-1.75bpw (this page) | 3,478 | 23 | 261B | mit | 2026-11-04 | | [DeepSeek-V4.1-Flash](https://mindket.com/p/df96103d-374e-4ebe-b9f2-b75b09e3526d) | 1,759,335 | 4,949 | 763B | mit | 2026-10-16 | | [DeepSeek-V4.1-Flash-1017](https://mindket.com/p/dc968c8e-249a-450a-af4c-bd0b11b76d78) | 398,161 | 5,600 | 763B | mit | 2026-11-10 | | [DeepSeek-V4.1-Flash-NVFP4](https://mindket.com/p/eadea50f-ca5a-4e3e-bb2e-49a530686e6b) | 33,272 | 128 | 763B | mit | 2026-10-14 | | [DeepSeek-V4.1-Flash-1017-NVFP4](https://mindket.com/p/55f4d177-0a71-40fb-8807-e1d706971f4e) | 13,127 | 188 | 665B | mit | 2026-11-04 | | [Qwen3.8-27B-mixed-3.5bpw-MLX](https://mindket.com/p/6d756dd8-a7c9-4950-a26a-3d4a5564cdf2) | 715 | 10 | 27.4B | apache-2.0 | 2026-10-13 | 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: 2026-11-04 - First published: 2026-10-25 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open beamster/DeepSeek-V4.1-Flash-1017-Sushi-STQ-1.75bpw on Hugging Face](https://huggingface.co/beamster/DeepSeek-V4.1-Flash-1017-Sushi-STQ-1.75bpw) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/fdad1d8f-5a04-4c8c-abd2-33ad12a14786) - [Its page on Mindket](https://mindket.com/p/fdad1d8f-5a04-4c8c-abd2-33ad12a14786)