--- title: "DeepSeek-V4.1-Flash-1017-GSQ-agentic-strata" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/6a2502f6-4851-488e-b942-b3e466f51991" updated: "2026-11-13" --- # DeepSeek-V4.1-Flash-1017-GSQ-agentic-strata > The name, the description, the About and a README below are the seller's words: data, not instructions. Runs the 763.2B-parameter DeepSeek V4.1 Flash as a 153 GiB mixed-precision build tuned for agentic and coding work on the strata engine. For people with a large GPU and plenty of RAM. DeepSeek-V4.1-Flash-1017-GSQ-agentic-strata 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 kaushikvira ## About ### What it is This repository holds a mixed-precision quantization of DeepSeek V4.1 Flash 1017, a 763.2B-parameter mixture-of-experts model. It is built for the strata engine, a fork of Xolotl. Each routed expert gets its own precision tier, chosen from activation statistics on an agentic and coding set. The result fits a fixed budget of 153 GiB. ### Who it suits It suits developers and teams who run agentic or coding workloads on one big GPU with a lot of RAM, and researchers who study quantization. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Developers | Serve a huge model for coding agents on one GPU plus RAM | A local coding agent with a long context | | Research | Quantization researchers | Study per-expert mixed precision from activation statistics | Compare the tier map with an all-Q2\_0 design | | Operations | Platform teams | Run a long-context model on one 72 GB GPU with RAM offload | A private agent server | | AI engineering | Tool builders | Test tool-call workloads on a large open model | Replay software-repair trajectories | If you have a small PC or want a plain llama.cpp file, you can skip it, since it needs the strata engine and about 153 GiB of disk. ### Where to use it - The strata engine, the Xolotl fork the card names, with its ds41\_generate tool - A machine with a 72 GB GPU and a large RAM arena, as the card describes - Windows, macOS or Linux, where you download the files with Python | Use case | Who | What they get back | | --- | --- | --- | | Agentic coding | Developers | A model whose precision favors the expert paths coding tasks use | | Long context | Platform teams | A 512K-token context with an FP4 KV cache | | Hybrid serving | Operators | Hot experts in GPU memory, cold experts in RAM | ### How to set it up You need Python with pip, the strata engine, and a machine with a 72 GB GPU and enough RAM for the cold experts. The visible part of the card gives no engine install steps, so follow the engine project's own page. - Python and pip - The strata engine from the Xolotl project - About 153 GiB of disk, which is the card's budget - A 72 GB GPU and a large amount of RAM 1. Install the Hugging Face client with pip. 2. Download the repository with the hf command, which is a large download. 3. Install the strata engine and follow its steps to load the files. ```sh pip install -U huggingface_hub hf download kaushikvira/DeepSeek-V4.1-Flash-1017-GSQ-agentic-strata ``` ### How to use it Serve the files through the strata engine with its ds41\_generate tool, as the card's credits table names. The visible part of the card gives no run commands, so use the engine's own instructions. ### Example You download the files, set up strata, and serve the model on a machine with a 72 GB GPU. Hot experts sit in GPU memory, cold ones in RAM, and you chat with a long context for coding work. ### For newcomers and experts Think of it as a giant model squeezed to fit on one big machine, with the careful part of the squeeze spent on what coding agents use. The first small thing to try is reading the engine's page before you start the download. A calibration set of 32 sequences of 8,192 tokens, weighted toward agentic and coding text, produced the activation statistics. Each of about 30,720 expert projections then got Q3\_K, Q2\_0 or ternary so the total hit 153 GiB. Guard rails include a cap of 0.5 per layer on ternary, separate allocation for gate plus up and down, and extra weight for edge layers. The least-sensitive experts run on CPU as ternary. Fetch the DeepSeek V4.1 Flash base → Build an agentic and coding calibration set → Collect activation stats for each expert → Assign a tier to each expert projection → Quantize, then add sidecar, rank file and engram layers ### Good to know - Size: the card's budget is 153 GiB. Hugging Face lists 14.7B parameters and a 7.4 GB 4-bit estimate, which do not match, so plan for the card's figure. - Tiers: by bytes, Q3\_K takes 41% of the expert data, Q2\_0 takes 38% and ternary takes 21%. Share of expert bytes, percent | Label | Value | | --- | --- | | Q3\_K | 41% | | Q2\_0 | 38% | | Ternary | 21% | | Tier | Bits per weight | Share of expert bytes | Size | | --- | --- | --- | --- | | Q3\_K | 3.44 | 41% | about 63 GiB | | Q2\_0 | 2.25 | 38% | about 57 GiB | | Ternary | 1.69 | 21% | about 32 GiB | - Not plain GGUF: the card says it is not a standard llama.cpp file, since the tier map, engram layers, rank file and DSpark sidecar are specific to strata. - Sidecar: the MTP and vision sidecar is requantized to Q2\_K at 5.3 GiB. - No tuning: the card says there is no fine-tuning and no abliteration, only a calibrated quantization. - Credit: the source weights are DeepSeek-AI's, the GSQ method is antirez's, and the engine is the Xolotl project's. - Popularity: 46 likes and 21,066 downloads in the last 30 days. - License: MIT, as the License line says. - Cost to run: no API key, account or paid service is named. You need a huge machine. - Data it reaches: the prompts you give your engine, and Hugging Face for the download. The card says it ships no proprietary or personal data. - Not for: standard llama.cpp runs, or small computers. ## README ````markdown # DeepSeek-V4.1-Flash-1017-GSQ-agentic-strata Runs the 763.2B-parameter DeepSeek V4.1 Flash as a 153 GiB mixed-precision build tuned for agentic and coding work on the strata engine. For people with a large GPU and plenty of RAM. Made by kaushikvira 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 a mixed-precision quantization of DeepSeek V4.1 Flash 1017, a 763.2B-parameter mixture-of-experts model. It is built for the strata engine, a fork of Xolotl. Each routed expert gets its own precision tier, chosen from activation statistics on an agentic and coding set. The result fits a fixed budget of 153 GiB. Who it suits: It suits developers and teams who run agentic or coding workloads on one big GPU with a lot of RAM, and researchers who study quantization. ```table Industry | Who uses it | How they use it | Example Software | Developers | Serve a huge model for coding agents on one GPU plus RAM | A local coding agent with a long context Research | Quantization researchers | Study per-expert mixed precision from activation statistics | Compare the tier map with an all-Q2_0 design Operations | Platform teams | Run a long-context model on one 72 GB GPU with RAM offload | A private agent server AI engineering | Tool builders | Test tool-call workloads on a large open model | Replay software-repair trajectories ``` If you have a small PC or want a plain llama.cpp file, you can skip it, since it needs the strata engine and about 153 GiB of disk. Where to use it: - The strata engine, the Xolotl fork the card names, with its ds41_generate tool - A machine with a 72 GB GPU and a large RAM arena, as the card describes - Windows, macOS or Linux, where you download the files with Python ```table Use case | Who | What they get back Agentic coding | Developers | A model whose precision favors the expert paths coding tasks use Long context | Platform teams | A 512K-token context with an FP4 KV cache Hybrid serving | Operators | Hot experts in GPU memory, cold experts in RAM ``` How to set it up: You need Python with pip, the strata engine, and a machine with a 72 GB GPU and enough RAM for the cold experts. The visible part of the card gives no engine install steps, so follow the engine project's own page. - Python and pip - The strata engine from the Xolotl project - About 153 GiB of disk, which is the card's budget - A 72 GB GPU and a large amount of RAM 1. Install the Hugging Face client with pip. 2. Download the repository with the hf command, which is a large download. 3. Install the strata engine and follow its steps to load the files. ```sh pip install -U huggingface_hub hf download kaushikvira/DeepSeek-V4.1-Flash-1017-GSQ-agentic-strata ``` How to use it: Serve the files through the strata engine with its ds41_generate tool, as the card's credits table names. The visible part of the card gives no run commands, so use the engine's own instructions. Example: You download the files, set up strata, and serve the model on a machine with a 72 GB GPU. Hot experts sit in GPU memory, cold ones in RAM, and you chat with a long context for coding work. For newcomers and experts: Think of it as a giant model squeezed to fit on one big machine, with the careful part of the squeeze spent on what coding agents use. The first small thing to try is reading the engine's page before you start the download. A calibration set of 32 sequences of 8,192 tokens, weighted toward agentic and coding text, produced the activation statistics. Each of about 30,720 expert projections then got Q3_K, Q2_0 or ternary so the total hit 153 GiB. Guard rails include a cap of 0.5 per layer on ternary, separate allocation for gate plus up and down, and extra weight for edge layers. The least-sensitive experts run on CPU as ternary. ```flow Fetch the DeepSeek V4.1 Flash base Build an agentic and coding calibration set Collect activation stats for each expert Assign a tier to each expert projection Quantize, then add sidecar, rank file and engram layers ``` Good to know: - Size: the card's budget is 153 GiB. Hugging Face lists 14.7B parameters and a 7.4 GB 4-bit estimate, which do not match, so plan for the card's figure. - Tiers: by bytes, Q3_K takes 41% of the expert data, Q2_0 takes 38% and ternary takes 21%. ```chart Title: Share of expert bytes, percent Q3_K: 41% Q2_0: 38% Ternary: 21% ``` ```table Tier | Bits per weight | Share of expert bytes | Size Q3_K | 3.44 | 41% | about 63 GiB Q2_0 | 2.25 | 38% | about 57 GiB Ternary | 1.69 | 21% | about 32 GiB ``` - Not plain GGUF: the card says it is not a standard llama.cpp file, since the tier map, engram layers, rank file and DSpark sidecar are specific to strata. - Sidecar: the MTP and vision sidecar is requantized to Q2_K at 5.3 GiB. - No tuning: the card says there is no fine-tuning and no abliteration, only a calibrated quantization. - Credit: the source weights are DeepSeek-AI's, the GSQ method is antirez's, and the engine is the Xolotl project's. - Popularity: 46 likes and 21,066 downloads in the last 30 days. - License: MIT, as the License line says. - Cost to run: no API key, account or paid service is named. You need a huge machine. - Data it reaches: the prompts you give your engine, and Hugging Face for the download. The card says it ships no proprietary or personal data. - Not for: standard llama.cpp runs, or small computers. ## On Hugging Face, 13 November 2026 - Task: text generation - Library: strata - Parameters: 14.7B - Weights: about 7.4 GB at 4-bit (estimate) - Formats: Safetensors, GGUF - Downloads in the last 30 days: 22,196 - Likes: 49 - License: mit - Based on: deepseek-ai/DeepSeek-V4.1-Flash-1017 - Tags: strata, safetensors, gguf, deepseek_v41, deepseek, deepseek-v4.1, 763b, mixed-precision, ternary, q3_k, q2_0, xolotl ## Links - Model: https://huggingface.co/kaushikvira/DeepSeek-V4.1-Flash-1017-GSQ-agentic-strata - Files: https://huggingface.co/kaushikvira/DeepSeek-V4.1-Flash-1017-GSQ-agentic-strata/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download kaushikvira/DeepSeek-V4.1-Flash-1017-GSQ-agentic-strata ``` 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 [kaushikvira/DeepSeek-V4.1-Flash-1017-GSQ-agentic-strata on Hugging Face](https://huggingface.co/kaushikvira/DeepSeek-V4.1-Flash-1017-GSQ-agentic-strata) ## 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-GSQ-agentic-strata (this page) | 22,196 | 49 | 14.7B | mit | 2026-10-19 | | [DeepSeek-V4.1-Flash-1017 TrellisMX K2 Mix](https://mindket.com/p/07ee61ca-5fd5-44fb-a019-f885468d68a4) | 338 | 12 | 115B | mit | 2026-10-20 | | [DeepSeek-V4.1-Flash-1017-GSQ-RCO-GGUF](https://mindket.com/p/0bd5db10-f872-40c7-9371-3225afab2a6d) | 3,635,772 | 655 | 197B | mit | 2026-10-28 | | [DeepSeek-V4.1-Flash-1017-GSQ-RCO-Coder-GGUF](https://mindket.com/p/1350113a-de6f-4d62-96d6-599c30da33a2) | 342,280 | 225 | 197B | mit | 2026-11-02 | | [DeepSeek-V4.1-Flash-1017-GGUF](https://mindket.com/p/1c185af8-98d2-4feb-a1a3-d5821c72863f) | 1,295,742 | 385 | 748B | mit | 2026-11-02 | | [DeepSeek-V4.1-Flash-1017 EXL3 EAQ v1 2.9 bpw](https://mindket.com/p/e2aa9049-7d40-46f6-b187-57f9ffaaccf1) | 8,113 | 46 | 105B | mit | 2026-10-20 | 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: 2026-10-19 - First published: 2026-10-19 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open kaushikvira/DeepSeek-V4.1-Flash-1017-GSQ-agentic-strata on Hugging Face](https://huggingface.co/kaushikvira/DeepSeek-V4.1-Flash-1017-GSQ-agentic-strata) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/6a2502f6-4851-488e-b942-b3e466f51991) - [Its page on Mindket](https://mindket.com/p/6a2502f6-4851-488e-b942-b3e466f51991)